System

Abstract

To provide a system capable of realizing a sustainable society by effectively utilizing snow and ice as an energy source.SOLUTION: Means for collecting weather data and predicting an amount of snow or ice to be generated based on the weather data; A means for transmitting the data to a server, a means for planning an optimal energy use plan based on the predicted production amount and the collected water level data, a means for planning hot water disinfection using hot water generated after cooling of a calculation resource, a means for executing hot water disinfection according to the plan, a means for performing hydroelectric power generation using water stored in a hyper dam, and a means for accumulating the collected data and energy use data in a database and proposing an optimal energy mix for the next season.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] As demand for renewable energy increases and the need to reduce environmental impact grows, addressing the issue of global warming has become an urgent task. In particular, in regions with heavy snow and ice, the effective use of these resources as an energy source could potentially contribute to the realization of a sustainable society. However, achieving this requires the collection of a wide variety of real-time data and the integrated management and optimization of that data. Conventional systems have had difficulty efficiently processing such large amounts of data and automatically formulating optimal energy resource utilization plans. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for collecting meteorological data and predicting snow and ice production based on that data, a means for monitoring water level data of rivers and dams in real time using IoT sensors and transmitting that data to a server, a means for formulating an optimal energy resource utilization plan based on the predicted production amount and the collected water level data, a means for formulating a hot water disinfection plan using hot water generated after cooling the computing resources, a means for executing the hot water disinfection plan according to the plan, a means for generating hydroelectric power using water stored in a hyper dam, and a means for storing the collected data and energy usage data in a database and proposing an optimal energy mix for the next season. This enables snow and ice to be effectively used as an energy source, thereby realizing a sustainable society.

[0006] "Weather data" refers to data that includes information related to weather conditions, such as weather forecasts, temperature, precipitation, and wind speed.

[0007] "Snow and ice production" refers to the amount of snow and ice accumulation expected over a given period and area.

[0008] An "IoT sensor" is a sensor device that collects data from the physical environment and transmits the data over the Internet.

[0009] "River and dam water level data" refers to data that refers to real-time information on water levels in rivers and dams.

[0010] A "server" is a central control device that collects and processes data, and is a device that works in conjunction with multiple terminals via a network.

[0011] "Energy resources" refers to natural resources and energy that can be used as energy sources, such as snow, ice, water, hot water, and electricity.

[0012] "Utilization plan" refers to a plan that determines the optimal allocation and utilization of energy resources based on collected data.

[0013] "Computing resources" refers to the computing power of data centers, cloud computing environments, etc.

[0014] "Hot water" refers to water of a certain temperature that is produced during the cooling process of a computing resource.

[0015] "Hot water disinfection" refers to the process of sterilizing rice seeds and other materials using hot water.

[0016] A "hyper dam" is a large-scale dam structure intended for hydroelectric power generation and water resource management.

[0017] "Hydroelectricity" refers to the process of harnessing the kinetic energy of flowing water to generate electricity.

[0018] A "database" refers to an information management system for efficiently managing and using accumulated data.

[0019] "Energy mix" refers to a configuration that combines various energy sources to achieve optimal energy supply overall. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] Meteorological data acquisition and analysis

[0042] The server collects weather data. It periodically sends HTTP GET requests to the weather data provider's API and receives JSON-formatted data in response. This data includes weather forecasts, temperature, precipitation, wind speed, etc. The server then uses this data to apply machine learning models to predict snow and ice formation, which allows it to understand future weather conditions.

[0043] Examples:

[0044] The server collects weather data from a URL such as "https: / / api.weather.com / data" and uses an ML model to predict the dates and amount of snowfall. The predicted data is used in the next step.

[0045] Real-time monitoring of water level data

[0046] The device obtains real-time water level data from the IoT sensors of rivers and dams. The device establishes communication with the sensors and reads the latest water level data. This data is sent to the server using an HTTP POST request.

[0047] Examples:

[0048] The device uses a sensor ID such as "sensor_001" to read water level data and sends it to "https: / / server.endpoint / waterlevels".

[0049] Optimizing energy use

[0050] The server creates an optimal energy resource utilization plan based on the weather and water level data it collects. The server integrates this data, calculates how to allocate and utilize energy resources, and creates a specific utilization plan based on the results.

[0051] Examples:

[0052] The server runs an energy optimization algorithm using predicted snowfall and real-time water level data as inputs to generate an energy usage plan, which determines how resources can be used efficiently.

[0053] Use of hot water and hot water disinfection

[0054] The server uses the hot water generated after cooling the computing resources to create a hot water disinfection plan. The server uses the hot water temperature data from the cooling system to calculate the conditions suitable for hot water disinfection, and then performs hot water disinfection based on that plan.

[0055] Examples:

[0056] The server collects the temperature data of the hot water obtained from the cooling system, and based on the temperature conditions, it plans the hot water disinfection and controls the process of sterilizing the rice seeds without using pesticides.

[0057] Hydroelectric power generation

[0058] The server generates hydroelectric power using the water stored in the Hyper Dam. The server calculates the amount of power to be generated based on the dam's water level data and issues instructions for the actual power generation operation. This allows the dam's water to be used efficiently to generate electricity.

[0059] Examples:

[0060] Based on water level data obtained from sensors installed in the dam, the system operates hydroelectric power generation equipment and issues instructions to optimize power generation.

[0061] Accumulating data and optimizing for the next season

[0062] The server collects weather and energy usage data and stores it in a database. The server analyzes this data and proposes the optimal energy mix for the next season.

[0063] Examples:

[0064] The server analyzes weather and energy usage data stored in a database and runs algorithms to further optimize future energy usage plans, thereby optimizing the energy mix for the next season.

[0065] The system aims to utilize data collected at each step to efficiently use natural resources as an energy source, thereby reducing energy waste and helping to realize a sustainable society.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] The server collects weather data by periodically issuing an HTTP GET request to the weather data provider's API and retrieving the JSON data returned as a response. This data includes information such as weather forecast, temperature, precipitation, and wind speed.

[0069] Step 2:

[0070] The server analyzes the collected weather data and uses machine learning models to predict the amount of snow and ice that will be produced, which is then used in the next step.

[0071] Step 3:

[0072] The device acquires real-time water level data from the IoT sensor for the river or dam. The device communicates with the sensor and reads the latest water level data. This read data is sent to the server using an HTTP POST request.

[0073] Step 4:

[0074] The server integrates the received water level data with weather data forecasts and then creates an optimal energy resource utilization plan. The server inputs the predicted snowfall amount and real-time water level data, runs the energy optimization algorithm, and generates a specific utilization plan.

[0075] Step 5:

[0076] The server collects temperature data of the hot water generated after cooling the computing resources. Using the temperature data from the cooling system, the system calculates the conditions suitable for hot water disinfection and formulates a hot water disinfection plan based on the conditions.

[0077] Step 6:

[0078] The user executes hot water disinfection according to the hot water disinfection plan on the server, which controls the process of sterilizing rice seeds with hot water without using pesticides.

[0079] Step 7:

[0080] The server generates hydroelectric power using the water stored in the Hyper Dam. Based on water level data from sensors installed in the dam, it calculates the amount of power to be generated and instructs the operation of the power generation equipment. This operation allows for efficient generation of electricity.

[0081] Step 8:

[0082] The server stores the weather data and energy usage data it has collected so far in a database.

[0083] Step 9:

[0084] The server analyzes the accumulated data and runs an algorithm that suggests the optimal energy mix for the next season, thereby optimizing future energy usage plans.

[0085] Example 1

[0086] 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."

[0087] The purpose of this invention is to effectively collect and analyze weather and water level data to efficiently utilize energy resources. Conventional systems merely collect data individually and use them for their respective purposes, and lack a means to integrate and utilize this data to optimize energy use. Furthermore, no concrete means are available for reducing environmental impacts or running costs. Therefore, the challenge is to achieve more advanced optimization of energy use and sustainable environmental management through comprehensive data analysis.

[0088] 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.

[0089] In this invention, the server includes means for collecting meteorological data and predicting the amount of snow and ice produced based on the meteorological data, means for monitoring water level data of rivers and dams in real time using sensors and transmitting the data to the server, and means for formulating an optimal energy resource utilization plan based on the predicted amount of snow and ice produced and the collected water level data. This makes it possible to comprehensively utilize various data to optimize energy utilization and manage sustainable resources.

[0090] "Weather data" refers to various weather-related information such as weather forecasts, temperature, precipitation, and wind speed.

[0091] "Snow and ice production" refers to the predicted amount of snow and ice that will fall and accumulate.

[0092] A "sensor" is a device for measuring physical environmental information, and in this case refers to a device for monitoring the water level of rivers and dams in real time.

[0093] "Energy resources" refers to resources that are used as energy, such as hydroelectric power generation and hot water disinfection.

[0094] "Computing device" refers to a computing resource for data processing and analysis.

[0095] "Hot water disinfection" refers to a method of sterilization or disinfection using heated water.

[0096] A "dam" refers to an artificial structure for storing water, and in this case it serves as a water source for hydroelectric power generation.

[0097] "Storage" refers to devices and systems for long-term storage of data.

[0098] "Energy mix" refers to the optimal use of different types of energy resources.

[0099] A "generative model" refers to a machine learning model that performs analysis and predictions based on specific input data.

[0100] An "actuator" refers to a mechanical device that performs a physical action based on information obtained from a sensor.

[0101] The system of the present invention integrates the collection and analysis of meteorological data, real-time monitoring of water level data, optimization of energy use, hot water use and hot water disinfection, hydroelectric power generation, and data accumulation and optimization for the next season. Specific embodiments of each processing step are described below.

[0102] Hardware and software used

[0103] Server: Collects and analyzes meteorological data, develops energy usage plans, plans hot water disinfection, operates hydroelectric power generation, and accumulates and analyzes data.

[0104] Terminal: Obtains water level data from rivers and dams from IoT sensors and sends it to a server.

[0105] Sensors: Measure water level data in rivers and dams in real time.

[0106] Machine learning models: Predict snow and ice formation based on weather data.

[0107] Database: A storage system for storing collected data and energy usage data.

[0108] Meteorological data collection and analysis

[0109] To collect weather data, the server periodically sends an HTTP GET request to a weather data provider's API (e.g., https: / / api.weather.com / data). The response contains JSON-formatted data including weather forecasts, temperature, precipitation, wind speed, etc. The server then feeds this data into a machine learning model to predict snow and ice formation.

[0110] Examples:

[0111] The server sends an HTTP GET request to the API endpoint, and the resulting JSON data is input into a Python (registered trademark)-based machine learning model to predict future weather conditions.

[0112] Real-time monitoring of water level data

[0113] The device establishes communication with the IoT sensor and acquires real-time water level data of rivers and dams. The acquired data is sent to the server using an HTTP POST request.

[0114] Examples:

[0115] The device reads water level data using a sensor ID such as "Sensor_001" and sends an HTTP POST request to "https: / / server.endpoint / waterlevels".

[0116] Optimizing energy use

[0117] The server integrates the weather and water level data collected and runs an energy optimization algorithm to create an optimal energy usage plan.

[0118] Examples:

[0119] The server integrates weather data and water level data and runs an energy optimization algorithm (e.g., a linear optimization algorithm) to generate an optimal energy usage plan.

[0120] Use of hot water and hot water disinfection

[0121] The server uses the hot water generated after the computer cools down to calculate the conditions for hot water disinfection, and then performs hot water disinfection based on the calculated conditions.

[0122] Examples:

[0123] The server collects the temperature data of the hot water obtained from the cooling system and creates a hot water disinfection plan based on the temperature conditions, for example, to sterilize rice seeds.

[0124] Hydroelectric power generation

[0125] The server operates the hydroelectric power plant based on water level data obtained from the dam, calculates the amount of power generated, and generates electricity using water efficiently.

[0126] Examples:

[0127] The server collects water level data from sensors in the dam facility, calculates the amount of power generated, and sends operating instructions to the power generation facility.

[0128] Accumulating data and optimizing for the next season

[0129] The server collects weather data and energy usage data, stores it in a database, and then analyzes it to propose the optimal energy mix for the next season.

[0130] Examples:

[0131] The server analyzes the data stored in the database and runs an energy mix optimization algorithm to propose the optimal energy mix for the next season.

[0132] Prompt Sentence Examples

[0133] "Run an energy optimization algorithm using weather and water level data as inputs to generate an optimal energy usage plan."

[0134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0135] Step 1:

[0136] The server collects weather data by periodically sending HTTP GET requests to the weather data provider's API (e.g., https: / / api.weather.com / data). The input required is the API endpoint and authentication information, and the output is JSON-formatted data such as the weather forecast, temperature, precipitation, and wind speed. This data is used in the next processing step.

[0137] Step 2:

[0138] The server analyzes the weather data it collects. Specifically, the server inputs the weather data into a machine learning model using Python to predict the amount of snow and ice that will be generated. The input requires weather data (weather forecast, temperature, precipitation, wind speed, etc.), and the output is the predicted amount of snow and ice that will be generated.

[0139] Step 3:

[0140] The terminal acquires water level data from the IoT sensor. Specifically, the terminal establishes communication with the sensor and reads the water level data in real time. The input requires the sensor ID and communication protocol, and the output is the latest water level data.

[0141] Step 4:

[0142] The water level data acquired by the device is sent to the server. Specifically, the device sends an HTTP POST request to "https: / / server.endpoint / waterlevels". The input requires the water level data and a destination URL, and the output is the water level data stored on the server.

[0143] Step 5:

[0144] The server integrates the collected weather and water level data to create an energy usage plan. Specifically, the server executes an energy optimization algorithm (e.g., a linear optimization algorithm). The input required is the integrated weather and water level data, and the output generated is an optimal energy usage plan.

[0145] Step 6:

[0146] The server creates a plan for hot water use and hot water disinfection. Specifically, the server collects hot water temperature data from the cooling system and calculates the conditions suitable for hot water disinfection. The input required is hot water temperature data from the cooling system, and the output generated is a hot water disinfection plan.

[0147] Step 7:

[0148] The server carries out hydroelectric power generation. Specifically, the server collects water level data from sensors installed in the dam and operates the power generation equipment based on that data. Water level data and power generation equipment control data are required as inputs, and the generated power data is obtained as output.

[0149] Step 8:

[0150] The server stores the collected data in a database and proposes the optimal energy mix for the next season. Specifically, the server analyzes the stored data and runs an energy mix optimization algorithm. All stored data is required as input, and a proposed energy mix for the next season is generated as output.

[0151] (Application example 1)

[0152] 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."

[0153] Conventional energy management systems were unable to effectively utilize weather and water level data, making it difficult to optimize energy use within factories. In particular, hot water disinfection and hydroelectric power generation were planned and executed separately, preventing integrated management, resulting in wasted energy resources. Furthermore, there was no established method for efficiently analyzing data using generative AI models, resulting in low accuracy in energy usage plans.

[0154] 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.

[0155] In this invention, the server includes a means for optimizing energy usage of equipment installed in the factory, a means for executing an optimal energy usage plan and hot water disinfection using a smart device, and a means for managing energy based on the optimized energy usage plan. This allows for the integration of weather data and water level data to optimize energy usage and efficiently manage energy resources within the factory. Furthermore, data analysis using a generative AI model can improve the accuracy of the energy usage plan and reduce wasteful energy consumption.

[0156] "Weather data" is a general term for various weather-related information such as weather forecasts, temperature, precipitation, and wind speed.

[0157] An "IoT sensor" is a sensor connected to the Internet that collects real-time data such as water levels in rivers and dams.

[0158] An "optimal energy resource utilization plan" is a specific plan for efficient energy use that calculates energy allocation and utilization methods based on collected data.

[0159] "Warm water generated after cooling of computing resources" refers to warm water generated by the cooling process of computing equipment, which can be reused.

[0160] "Hot water disinfection" is a method of disinfection using hot water that has reached a certain temperature.

[0161] "Hydroelectric power generation" is a method of generating electricity by using water stored in a dam.

[0162] A "database" is an information system for efficiently managing and storing collected data and analysis results.

[0163] "Optimizing energy use of equipment installed in factories" means optimizing energy consumption based on collected data about equipment within factories.

[0164] "Smart devices" refer to multifunctional devices with internet connectivity, such as smartphones, smart glasses, and head-mounted displays.

[0165] A "generative AI model" is a model that uses artificial intelligence to analyze data and make predictions and suggestions.

[0166] A "prompt" is an instruction or question input to a generative AI model and is used to control the model's output.

[0167] The present invention relates to a system for formulating an optimal energy resource utilization plan and for efficiently carrying out hot water disinfection and hydroelectric power generation.

[0168] Meteorological data acquisition and analysis

[0169] The server periodically sends HTTP GET requests to the weather data provider's API to collect weather forecasts, temperature, precipitation, wind speed, etc. This data is received in JSON format and then fed into a generative AI model, which uses this data to predict snow and ice formation and stores the results.

[0170] Real-time monitoring of water level data

[0171] IoT sensors collect real-time water level data from rivers and dams. The device establishes communication with the sensor and reads the latest water level data. This data is sent to the server using an HTTP POST request. The server stores this data in a database.

[0172] Optimizing energy use

[0173] The server creates an optimal energy resource utilization plan based on the collected weather and water level data. Specifically, it uses a generative AI model to analyze the data and calculate energy allocation and utilization methods. The server then manages energy within the factory based on this plan.

[0174] Use of hot water and hot water disinfection

[0175] The server uses the temperature data of the hot water generated after cooling the computing resources to calculate the optimal conditions for hot water disinfection. The server controls the hot water disinfection process using the hot water obtained from the cooling system. The server then executes the hot water disinfection based on the planned conditions.

[0176] Hydroelectric power

[0177] The server generates hydroelectric power using the water stored in the Hyper Dam. It calculates the amount of power to be generated based on the dam's water level data and instructs the actual power generation operation. This allows the dam's water to be used efficiently to generate electricity.

[0178] Accumulating data and optimizing for the next season

[0179] The server stores the collected weather and energy usage data in a database, analyzes this data, and runs an algorithm that suggests the optimal energy mix for the next season.

[0180] Hardware and software used

[0181] IoT sensor: A device that collects water level data, etc.

[0182] Server: Data analysis, energy planning

[0183] Generative AI models: Analyzing weather and water level data

[0184] Energy Management Software: Implementing Energy Planning

[0185] Cooling system: Generating hot water for hot water disinfection

[0186] Examples of prompt statements

[0187] Here are some example prompts the system might use to analyze data using a generative AI model:

[0188] "Predict the temperature and amount of snowfall for the following dates:

[0189] Date: YYYY-MM-DD

[0190] Temperature: [20, 22, 18, 21, 23]

[0191] Snowfall: [0, 15, 10, 0, 5]”

[0192] "Please suggest an energy usage plan based on:

[0193] Forecast temperature and snowfall

[0194] Real-time water level data: 4.5m

[0195] Based on this specific example, the server optimizes energy usage and determines whether the conditions for hot water disinfection are met.

[0196] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0197] Step 1:

[0198] The server periodically sends HTTP GET requests to the weather data provider's API to obtain weather data such as weather forecast, temperature, precipitation, wind speed, etc. The obtained data is passed to the server in JSON format and stored in a weather database.

[0199] Input: API endpoint information

[0200] Data processing: Extraction of weather forecast, temperature, precipitation, and wind speed data

[0201] Output: Weather data in JSON format

[0202] Step 2:

[0203] Using generative AI models, the server analyzes collected weather data and predicts the amount of snow and ice that will be produced. The predictions are stored in a database and used as the basis for energy planning.

[0204] Input: Weather data

[0205] Data Computing: Predicting with Generative AI Models

[0206] Output: Predicted snow and ice formation

[0207] Step 3:

[0208] IoT sensors collect real-time water level data from rivers and dams and send it to a server via a terminal. The server stores the received water level data in a database.

[0209] Input: Water level data from IoT sensors

[0210] Data processing: Extraction and transmission of water level data

[0211] Output: Updates to the server database

[0212] Step 4:

[0213] The server integrates the collected weather and water level data and uses a generative AI model to create an optimal energy resource utilization plan, providing the optimized energy plan to the energy management software.

[0214] Input: Weather data, water level data

[0215] Data Computing: Energy-Optimizing Computations in Generative AI Models

[0216] Output: Energy usage plan

[0217] Step 5:

[0218] The server acquires the temperature data of the hot water generated after cooling the computing resources, calculates the optimal conditions for hot water disinfection based on that data, and then performs hot water disinfection after confirming whether the optimal conditions are met.

[0219] Input: Hot water temperature data

[0220] Data calculation: Calculation of hot water disinfection conditions

[0221] Output: Instruction to perform hot water disinfection

[0222] Step 6:

[0223] The server calculates the amount of power to be generated based on the dam's water level data and issues instructions to operate the hydroelectric power generation equipment, thereby generating electricity using water efficiently.

[0224] Input: Dam water level data

[0225] Data calculation: Calculation of power generation amount

[0226] Output: Hydroelectric power generation operating instructions

[0227] Step 7:

[0228] The server stores the collected weather and energy usage data in a database and proposes an optimized energy mix for the next season.

[0229] Input: Weather data, energy usage data

[0230] Data calculation: Energy mix optimization calculation

[0231] Output: Energy mix proposal for the next season

[0232] Example prompt:

[0233] "Predict the temperature and amount of snowfall for the following dates:

[0234] Date: YYYY-MM-DD

[0235] Temperature: [20, 22, 18, 21, 23]

[0236] Snowfall: [0, 15, 10, 0, 5]”

[0237] "Please suggest an energy usage plan based on:

[0238] Forecast temperature and snowfall

[0239] Real-time water level data: 4.5m

[0240] 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.

[0241] Meteorological data acquisition and analysis

[0242] The server collects weather data. It periodically issues HTTP GET requests to the weather data provider's API and retrieves the JSON data in response. This data includes information such as weather forecast, temperature, precipitation, and wind speed. The server then uses this data to apply machine learning models to predict snow and ice formation, allowing it to understand future weather conditions.

[0243] Examples:

[0244] The server collects weather data from https: / / api.weather.com / data and uses the ML model to predict the dates and amount of snowfall. The predicted data is used in the next step.

[0245] Real-time monitoring of water level data

[0246] The device receives real-time water level data from the IoT sensors of rivers and dams. The device communicates with the sensors and reads the latest water level data. This data is sent to the server using an HTTP POST request.

[0247] Examples:

[0248] The device uses a sensor ID such as "sensor_001" to read water level data and sends it to "https: / / server.endpoint / waterlevels".

[0249] Optimizing energy use

[0250] The server creates an optimal energy resource utilization plan based on the weather and water level data it collects. The server integrates this data, calculates how to allocate and utilize energy resources, and creates a specific utilization plan based on the results.

[0251] Examples:

[0252] The server runs an energy optimization algorithm using predicted snowfall and real-time water level data as inputs to generate an energy usage plan, which determines how resources can be used efficiently.

[0253] Use of hot water and hot water disinfection

[0254] The server uses the hot water generated after cooling the computing resources to create a hot water disinfection plan. The server uses the hot water temperature data from the cooling system to calculate the conditions suitable for hot water disinfection, and then performs hot water disinfection based on that plan.

[0255] Examples:

[0256] The server collects the temperature data of the hot water obtained from the cooling system, and based on the temperature conditions, it plans the hot water disinfection and controls the process of sterilizing the rice seeds without using pesticides.

[0257] Hydroelectric power generation

[0258] The server generates hydroelectric power using the water stored in the Hyper Dam. The server calculates the amount of power to be generated based on the dam's water level data and issues instructions for the actual power generation operation. This allows the dam's water to be used efficiently to generate electricity.

[0259] Examples:

[0260] Based on water level data obtained from sensors installed in the dam, the system operates hydroelectric power generation equipment and issues instructions to optimize power generation.

[0261] Accumulating data and optimizing for the next season

[0262] The server stores the collected weather data and energy usage data in a database.

[0263] Examples:

[0264] The server analyzes weather and energy usage data stored in a database and runs algorithms to further optimize future energy usage plans, thereby optimizing the energy mix for the next season.

[0265] Emotion engine collaboration

[0266] The device is equipped with an emotion engine that collects user emotion data. The emotion engine analyzes the user's emotions in real time from their facial expressions and voice. This emotion data is sent to a server and reflected in the energy usage plan.

[0267] Examples:

[0268] When a user uses a device equipped with an emotion engine, emotional data is collected in real time and sent to a server, which then adjusts the allocation and usage of energy resources based on the collected emotional data.

[0269] Energy Use Feedback

[0270] The server provides the user with feedback on energy usage based on the data obtained from the emotion engine. This feedback is adjusted according to the user's emotional state, allowing for more appropriate energy usage plans to be proposed.

[0271] Examples:

[0272] The server receives the emotional data and provides feedback on energy usage based on the user's emotional state. For example, a user with a high stress level may receive specific feedback, such as advice on how to reduce energy costs.

[0273] The system aims to utilize data collected at each step to efficiently use natural resources as energy sources. Furthermore, by combining it with an emotion engine, it can adjust energy usage plans in real time according to the user's emotional state, helping to realize a sustainable society.

[0274] The processing flow will be explained below.

[0275] Step 1:

[0276] The server collects weather data. The server periodically sends an HTTP GET request to the weather data provider's API and obtains weather data in JSON format as a response. This data includes weather forecasts, temperature, precipitation, wind speed, etc.

[0277] Step 2:

[0278] The server analyzes the collected weather data and uses machine learning models to predict the amount of snow and ice that will be generated. The server then uses the weather data as input for the ML model and generates a forecast, which is then used in the next step.

[0279] Step 3:

[0280] The device acquires real-time water level data from the IoT sensor for the river or dam. The device communicates with the sensor and reads the latest water level data. This read data is then sent to the server using an HTTP POST request.

[0281] Step 4:

[0282] The server then integrates the received water level data with weather forecasts and uses this data to create an optimal energy resource utilization plan. Specifically, it runs an energy optimization algorithm using predicted snowfall and real-time water level data as inputs.

[0283] Step 5:

[0284] The server collects the temperature data of the hot water generated after cooling the computing resources. The server obtains the temperature data of the hot water from the cooling system and uses the data to calculate the conditions suitable for hot water disinfection.

[0285] Step 6:

[0286] The user executes the hot water disinfection according to the hot water disinfection plan of the server. Based on the instructions of the server, the process of sterilizing the rice seeds with hot water without using pesticides proceeds.

[0287] Step 7:

[0288] The server generates hydroelectric power using the water stored in the Hyper Dam. The server calculates the amount of power to be generated based on the dam's water level data and issues instructions to operate the power generation equipment. This allows for efficient power generation.

[0289] Step 8:

[0290] The server stores the weather and energy usage data collected so far in a database, which is then used to optimize energy usage plans for the next season.

[0291] Step 9:

[0292] The server analyzes the data stored in the database and runs an algorithm that suggests the optimal energy mix for the next season, thereby optimizing future energy usage plans.

[0293] Step 10:

[0294] The device is equipped with an emotion engine for collecting user emotion data, which analyzes emotions from the user's facial expressions and voice in real time and transmits this emotion data to a server.

[0295] Step 11:

[0296] The server reflects the received emotional data in its energy usage plan, adjusting the allocation and usage of energy resources based on the emotional data, and achieving optimal energy usage in line with the user's emotional state.

[0297] Step 12:

[0298] The server uses data from the emotion engine to provide users with specific feedback on energy usage based on their emotional state, such as advice on how to reduce energy costs if their stress level is high.

[0299] Example 2

[0300] 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."

[0301] In conventional energy utilization systems, weather data and water level data are collected and analyzed separately, making it difficult to integrate data in real time and efficiently optimize energy resources. Furthermore, energy utilization plans are formulated without taking into account the user's emotional state, resulting in a lack of user comfort and efficiency. Therefore, the challenge is to provide a system that integrates weather data and water level data and optimizes energy utilization plans in real time, reflecting the user's emotional data.

[0302] 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.

[0303] In this invention, the server includes means for collecting meteorological data and predicting the amount of snow and ice produced based on the meteorological data, means for monitoring water level data of rivers and dams in real time using sensors and transmitting the data to the server, and means for formulating an optimal energy resource utilization plan based on the predicted amount of snow and ice produced and the collected water level data. This makes it possible to integrate the meteorological data and water level data and formulate an optimal energy resource utilization plan in real time.

[0304] "Weather Data" refers to information about climatic conditions such as temperature, precipitation, wind speed, and weather forecasts.

[0305] "Snow and ice generation forecasting" refers to the use of machine learning models and algorithms to estimate future snow and ice volumes.

[0306] A "sensor" refers to a device that detects physical conditions or changes and acquires them as data.

[0307] "Water level data" refers to information showing the water height of rivers and dams.

[0308] "Server" refers to a computer system that collects, analyzes, stores, and distributes data.

[0309] "Optimal energy resource utilization plan" refers to a plan for efficient use of energy resources based on collected data.

[0310] "Warm water generated after cooling computing resources" refers to heated water generated during the cooling process of computers and servers.

[0311] "Hot water disinfection" refers to a method of using heated water to kill microorganisms and pathogens.

[0312] A "reservoir" refers to an artificial storage facility for storing water.

[0313] "Hydroelectric power" refers to a method of generating electricity using the power of flowing water.

[0314] "Database" refers to a system for efficiently managing, storing, and retrieving data.

[0315] "Energy mix" refers to a plan for using an appropriate combination of different types of energy sources.

[0316] "Emotional data" refers to information about the emotional state of a user analyzed from facial expressions and voice.

[0317] "Energy usage feedback" refers to providing information and advice on energy usage based on the user's behavior and situation.

[0318] MODE FOR CARRYING OUT THE INVENTION

[0319] The present invention is a system for efficiently utilizing natural resources as an energy source, and includes the following elements:

[0320] Meteorological data acquisition and analysis

[0321] The server sends an HTTP GET request to the API of an external weather data provider and collects the JSON-formatted weather data received as a response. This weather data includes information such as temperature, precipitation, wind speed, and weather forecast. The server uses machine learning models such as TENSORFLOW (registered trademark) to predict the amount of snow and ice that will be generated from this data. Specifically, the server collects data from "https: / / api.weather.com / data" and inputs it into the machine learning model to predict snowfall.

[0322] Example prompt sentence:

[0323] "How does the server collect weather data and use machine learning models to predict snowfall?"

[0324] Real-time monitoring of water level data

[0325] The device obtains water level data in real time from IoT sensors installed in rivers and dams. The sensor and device communicate, and the device reads the latest water level data. This data is sent to the server via an HTTP POST request using the sensor ID (e.g., "sensor_001"). Specifically, the data is sent to "https: / / server.endpoint / waterlevels."

[0326] Example prompt sentence:

[0327] "Please explain how the device will send the water level data obtained from the IoT sensor to the server in real time."

[0328] Optimizing energy use

[0329] The server integrates the collected weather and water level data and creates an optimal energy resource utilization plan based on this data. The server analyzes the data using Python libraries (e.g., SciPy, Pandas) and executes energy optimization algorithms. The generated energy utilization plan is saved in JSON format.

[0330] Example prompt sentence:

[0331] "Explain how weather and water level data collected by a server can be used to optimize energy usage planning."

[0332] Use of hot water and hot water disinfection

[0333] The server collects temperature data from the hot water generated during the cooling process of computing resources and creates a hot water disinfection plan.The server calculates temperature conditions using Matplotlib based on the data obtained from the cooling system and carries out hot water disinfection based on that plan.Specifically, it controls the process of sterilizing rice seeds without using pesticides.

[0334] Example prompt sentence:

[0335] "Please tell me how to use the hot water generated by the server after cooling the computing resources to perform hot water disinfection."

[0336] Hydroelectric power generation

[0337] The server calculates the amount of power generated based on the water level data stored in the reservoir and issues instructions for operating the hydroelectric power plant.The server then sends operation instructions to the PLC using the MODBUS protocol to optimize the power generation equipment.

[0338] Example prompt sentence:

[0339] "Explain how the server generates hydroelectric power based on reservoir water level data."

[0340] Accumulating data and optimizing for the next season

[0341] The server stores the collected weather and energy usage data in a database (e.g., MongoDB or AWS® DynamoDB). This data is analyzed using R or Python, and algorithms are run to optimize the energy mix for the next season.

[0342] Example prompt sentence:

[0343] "Please tell me how the data collected by the server will be used to optimize energy usage plans for the next season."

[0344] Emotion engine collaboration

[0345] The device uses an emotion engine (e.g., Affectiva SDK) to collect the user's emotion data. The device analyzes the user's facial expressions and voice and transmits the emotion data to the server in real time. The analyzed emotion data is then sent to the server using WebSocket.

[0346] Example prompt sentence:

[0347] "Please tell me how the server uses the emotional data collected by the device's emotion engine to reflect in the energy usage plan."

[0348] Energy Use Feedback

[0349] The server provides users with feedback on energy usage based on the emotional data obtained from the emotion engine. Based on the analysis results, the server suggests optimal energy usage methods according to the user's emotional state. The feedback is provided to users via a mobile app or web portal.

[0350] Example prompt sentence:

[0351] "Explain how the server provides feedback on energy usage based on data obtained from the emotion engine."

[0352] As described above, the present invention provides a system for efficiently utilizing natural resources as energy sources through the collection, analysis, and optimization of various data.

[0353] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0354] Step 1:

[0355] The server collects weather data. Specifically, it periodically sends HTTP GET requests from "https: / / api.weather.com / data" and obtains weather data in JSON format as a response. This data includes temperature, precipitation, wind speed, weather forecast, etc. The HTTP request is required as input, and the obtained weather data in JSON format is obtained as output. The server analyzes the obtained data and stores it in a database.

[0356] Step 2:

[0357] The server uses the weather data acquired to predict the amount of snow and ice produced using a machine learning model. Specifically, preprocessed data is input into the model using TensorFlow to obtain the predicted results. JSON data and a machine learning model are required as input, and the predicted amount of snow and ice produced is obtained as output. Furthermore, the prediction results are stored in a database for use in the next step.

[0358] Step 3:

[0359] The device retrieves water level data from an IoT sensor. Specifically, it uses a sensor ID, such as "sensor_001", to collect water level data in real time. As input, it requires the sensor ID and communication protocol, and as output, it gets the latest water level data. This data is sent to the server via an HTTP POST request.

[0360] Step 4:

[0361] The server receives water level data, stores it in a database, and integrates it with weather data to optimize the energy usage plan. Specifically, it integrates the data using Python's Pandas library and runs an optimization algorithm using SciPy. Weather and water level data are required as input, and the optimized energy usage plan is obtained as output. This plan is saved in JSON format.

[0362] Step 5:

[0363] The server collects the hot water temperature data from the cooling system. Specifically, it obtains the data from the temperature sensor provided by the cooling system and creates a hot water disinfection plan. The input is the temperature data, and the output is the temperature conditions suitable for disinfection and a specific operation plan. Based on this plan, a signal to control the disinfection process is sent.

[0364] Step 6:

[0365] The server calculates the amount of power generation based on the water level data stored in the Hyper Dam and issues instructions for operating the hydroelectric power plant. Specifically, it uses the MODBUS protocol to send operation instructions to the PLC to optimize the power generation equipment. The inputs required are water level data and a power generation plan, and the output is the actual power generation operation instructions.

[0366] Step 7:

[0367] The server stores all collected data and energy usage data in a database. Specifically, it uses MongoDB or AWS DynamoDB to store the data and then proposes the optimal energy mix for the next season. All collected data is required as input, and the optimized energy mix plan is obtained as output.

[0368] Step 8:

[0369] The device collects the user's emotional data and sends it to the server. Specifically, it uses an emotion engine such as the Affectiva SDK to analyze the user's facial expressions and voice. The input is the user's facial and voice data, and the output is the emotion analysis results. This data is sent to the server in real time.

[0370] Step 9:

[0371] The server provides users with feedback on energy usage based on the emotional data. Specifically, it analyzes the collected emotional and energy usage data and proposes optimal energy usage methods. The inputs are the emotional and energy usage data, and the output is feedback information provided to the user. The user receives the feedback through a mobile app or web portal.

[0372] In this manner, the system of the present invention specifically implements process steps for efficiently utilizing natural resources as energy sources.

[0373] (Application example 2)

[0374] 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."

[0375] Conventional energy usage planning systems have limited means for optimizing energy consumption while efficiently utilizing weather and water level data, making energy management particularly difficult in physical stores. Furthermore, they are unable to dynamically adjust energy usage plans based on user sentiment. This has resulted in insufficient optimization of the user experience and efficient energy consumption.

[0376] 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.

[0377] In this invention, the server includes: means for collecting weather data and predicting the amount of snow and ice generated based on the weather data; means for monitoring water level data of rivers and dams in real time using IoT sensors and transmitting the data to the server; means for formulating an optimal energy resource utilization plan based on the predicted generation amount and the collected water level data; means for formulating a hot water disinfection plan using hot water generated after cooling the computing resources; means for performing hot water disinfection; means for generating hydroelectric power using water stored in a hyper dam; means for storing the collected data and energy utilization data in a database and proposing an optimal energy mix for the next season; means for optimizing energy consumption and automatically controlling the utilization plan using smart glasses to operate the store; means for automatically adjusting temperature control and lighting in the store in cooperation with weather data and water level data; and means for collecting customer emotion data using an emotion engine and adjusting the energy utilization plan. This enables an energy management system that can efficiently consume energy in physical stores while providing a comfortable environment for customers.

[0378] "Weather data" refers to data relating to weather conditions such as weather forecasts, temperature, precipitation, and wind speed.

[0379] "Snow and ice production" refers to the amount of snow and ice production predicted based on collected meteorological data.

[0380] An "IoT sensor" is a sensor that connects to the Internet and measures and monitors environmental conditions, machine operation, and other factors in real time.

[0381] "Water level data" refers to data relating to the water level of bodies of water such as rivers and dams.

[0382] "Energy resources" are energy supply sources such as electricity, gas, and oil.

[0383] "Optimal energy resource utilization planning" is a concept that indicates planning the efficient utilization of energy resources based on collected data.

[0384] "Hot water disinfection" is a method of disinfecting microorganisms such as pathogens and viruses using hot water.

[0385] A "hyper dam" is a large dam that stores a large amount of water to generate hydroelectric power.

[0386] "Hydroelectric power generation" is a method of generating electricity by utilizing the potential energy of water flow and elevation.

[0387] "Smart glasses" are glasses-type devices worn by users that can display augmented reality (AR) and other information.

[0388] The "emotion engine" is a system for analyzing the user's emotional state from facial expressions, voice, etc.

[0389] "Energy mix" is a concept that refers to optimizing overall energy supply by combining different energy sources.

[0390] A "database" is a system for efficiently storing, searching, managing, and updating various types of data.

[0391] A "server" is a computer system that stores, processes, and transmits data over a network.

[0392] The system of the present invention collects meteorological data and water level data, and based on this data, formulates an optimal energy resource utilization plan. This system is realized through the following steps.

[0393] First, the server collects weather data by periodically issuing an HTTP GET request to the weather data provider's API and obtaining the JSON data in response. This data includes information such as weather forecast, temperature, precipitation, and wind speed. Next, the server uses this data to apply machine learning models to predict the amount of snow and ice formation.

[0394] Next, the device uses IoT sensors to obtain real-time water level data from rivers and dams. This data is then sent to the server using an HTTP POST request. The server then integrates the obtained weather and water level data and uses them to create an optimal energy resource utilization plan.

[0395] The system also includes a plan for hot water disinfection using the hot water generated after the computing resources are cooled. The hot water disinfection plan is calculated on the server based on the hot water temperature data from the cooling system. Specifically, it controls the process of sterilizing rice seeds without using pesticides.

[0396] Hydroelectric power generation using water stored in a hyper dam is also an important element of this invention. The server calculates the amount of power to be generated based on the dam's water level data and issues instructions for the actual power generation operation.

[0397] The server also stores all collected data and energy usage data in a database and runs algorithms to suggest an optimized energy mix for the next season.

[0398] Furthermore, this system utilizes smart glasses and an emotion engine to automatically optimize energy consumption in physical store operations. The user device, equipped with smart glasses, collects customers' emotional data in real time and sends it to a server. Based on the collected emotional data, the server automatically adjusts the temperature and lighting in the store, providing a comfortable environment for customers and simultaneously optimizing energy management.

[0399] As a concrete example, a physical store can automatically adjust air conditioning and lighting settings based on weather data, according to predicted temperatures and weather changes. Also, an emotion engine analyzes customers' emotional data and sets the environment to make them feel comfortable. This makes it possible to achieve both efficient energy use and customer satisfaction.

[0400] The following example prompts are also set up as specific scenarios:

[0401] "Users visit the store

[0402] The emotion engine analyzes customer sentiment data in real time and uses it as input to optimize energy consumption.

[0403] It runs energy optimization algorithms based on weather and water level data.

[0404] Energy optimization plans are immediately reflected in store temperature settings, lighting, and air conditioning system adjustments.

[0405] The user receives a presentation of their emotional state through smart glasses.

[0406] Depending on your emotional state, lighting and background music settings in the store will be adjusted to support a comfortable stay.

[0407] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0408] Step 1:

[0409] A server collects weather data.

[0410] Input: Weather data provider's API.

[0411] Data processing: Issue an HTTP GET request and analyze the JSON data obtained as a response.

[0412] Output: Obtain weather information such as weather forecast, temperature, precipitation, wind speed, etc.

[0413] Specific operation: The server periodically calls the API and stores the acquired data in an internal database.

[0414] Step 2:

[0415] The server uses machine learning models to predict the amount of snow and ice that will form.

[0416] Input: Weather data obtained in step 1.

[0417] Data calculation: Based on the acquired weather data, a machine learning model is used to predict the amount of snow and ice that will be generated.

[0418] Output: Predicted snow and ice production.

[0419] Specific operation: The server inputs weather data into the model, obtains and stores the forecast results.

[0420] Step 3:

[0421] The device uses IoT sensors to obtain real-time water level data for rivers and dams.

[0422] Input: Water level data from IoT sensors.

[0423] Data processing: Water level data is sent to the server using an HTTP POST request.

[0424] Output: Real-time water level data.

[0425] Specific operation: The terminal communicates with the IoT sensor and sends the water level data it reads to the server.

[0426] Step 4:

[0427] The server integrates the weather and water level data collected to create an optimal plan for using energy resources.

[0428] Input: Weather data for step 2, water level data for step 3.

[0429] Data Computing: Integrates data and runs energy optimization algorithms.

[0430] Output: Energy resource utilization plan.

[0431] How it works: The server inputs weather and water level data into the algorithm, generates and saves an optimized plan.

[0432] Step 5:

[0433] A hot water disinfection plan is created using the hot water generated after the server cools down the computing resources.

[0434] Input: Hot water temperature data from the cooling system.

[0435] Data calculation: Calculates the optimal conditions for hot water disinfection based on hot water temperature data.

[0436] Output: Plan for hot water disinfection.

[0437] Specific operation: The server analyzes the hot water temperature data and sets appropriate disinfection conditions.

[0438] Step 6:

[0439] The device uses smart glasses to collect emotional data from customers in real time and transmits it to a server.

[0440] Input: Smart glasses emotion engine data.

[0441] Data processing: Emotion data is sent to the server using an HTTP POST request.

[0442] Output: Real-time sentiment data.

[0443] Specific operation: The device works in conjunction with smart glasses to read emotion data and send it to the server.

[0444] Step 7:

[0445] The server automatically adjusts temperature control and lighting in the store based on emotional data.

[0446] Input: Emotion data from step 6.

[0447] Data calculation: Analyzes emotional data and calculates optimal environment settings.

[0448] Output: Temperature and lighting settings in the store.

[0449] Specific operation: The server analyzes the emotional data and provides appropriate settings for temperature control and lighting systems.

[0450] Step 8:

[0451] The server stores all collected data and energy usage data in a database and proposes an optimized energy mix for the next season.

[0452] Input: Weather data, water level data, energy usage data, emotion data.

[0453] Data processing: Store data in a database and run analysis algorithms.

[0454] Output: Proposal for optimising the energy mix for the next season.

[0455] How it works: The server stores the data in a database and uses an algorithm to generate a plan for the next season.

[0456] 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.

[0457] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0458] 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.

[0459] [Second embodiment]

[0460] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0461] 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.

[0462] 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).

[0463] 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.

[0464] 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.

[0465] 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).

[0466] 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.

[0467] 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.

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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."

[0472] Meteorological data acquisition and analysis

[0473] The server collects weather data. It periodically sends HTTP GET requests to the weather data provider's API and receives JSON-formatted data in response. This data includes weather forecasts, temperature, precipitation, wind speed, etc. The server then uses this data to apply machine learning models to predict snow and ice formation, which allows it to understand future weather conditions.

[0474] Examples:

[0475] The server collects weather data from a URL such as "https: / / api.weather.com / data" and uses an ML model to predict the dates and amount of snowfall. The predicted data is used in the next step.

[0476] Real-time monitoring of water level data

[0477] The device obtains real-time water level data from the IoT sensors of rivers and dams. The device establishes communication with the sensors and reads the latest water level data. This data is sent to the server using an HTTP POST request.

[0478] Examples:

[0479] The device uses a sensor ID such as "sensor_001" to read water level data and sends it to "https: / / server.endpoint / waterlevels".

[0480] Optimizing energy use

[0481] The server creates an optimal energy resource utilization plan based on the weather and water level data it collects. The server integrates this data, calculates how to allocate and utilize energy resources, and creates a specific utilization plan based on the results.

[0482] Examples:

[0483] The server runs an energy optimization algorithm using predicted snowfall and real-time water level data as inputs to generate an energy usage plan, which determines how resources can be used efficiently.

[0484] Use of hot water and hot water disinfection

[0485] The server uses the hot water generated after cooling the computing resources to create a hot water disinfection plan. The server uses the hot water temperature data from the cooling system to calculate the conditions suitable for hot water disinfection, and then performs hot water disinfection based on that plan.

[0486] Examples:

[0487] The server collects the temperature data of the hot water obtained from the cooling system, and based on the temperature conditions, it plans the hot water disinfection and controls the process of sterilizing the rice seeds without using pesticides.

[0488] Hydroelectric power generation

[0489] The server generates hydroelectric power using the water stored in the Hyper Dam. The server calculates the amount of power to be generated based on the dam's water level data and issues instructions for the actual power generation operation. This allows the dam's water to be used efficiently to generate electricity.

[0490] Examples:

[0491] Based on water level data obtained from sensors installed in the dam, the system operates hydroelectric power generation equipment and issues instructions to optimize power generation.

[0492] Accumulating data and optimizing for the next season

[0493] The server collects weather and energy usage data and stores it in a database. The server analyzes this data and proposes the optimal energy mix for the next season.

[0494] Examples:

[0495] The server analyzes weather and energy usage data stored in a database and runs algorithms to further optimize future energy usage plans, thereby optimizing the energy mix for the next season.

[0496] The system aims to utilize data collected at each step to efficiently use natural resources as an energy source, thereby reducing energy waste and helping to realize a sustainable society.

[0497] The processing flow will be explained below.

[0498] Step 1:

[0499] The server collects weather data by periodically issuing an HTTP GET request to the weather data provider's API and retrieving the JSON data returned as a response. This data includes information such as weather forecast, temperature, precipitation, and wind speed.

[0500] Step 2:

[0501] The server analyzes the collected weather data and uses machine learning models to predict the amount of snow and ice that will be produced, which is then used in the next step.

[0502] Step 3:

[0503] The device acquires real-time water level data from the IoT sensor for the river or dam. The device communicates with the sensor and reads the latest water level data. This read data is sent to the server using an HTTP POST request.

[0504] Step 4:

[0505] The server integrates the received water level data with weather data forecasts and then creates an optimal energy resource utilization plan. The server inputs the predicted snowfall amount and real-time water level data, runs the energy optimization algorithm, and generates a specific utilization plan.

[0506] Step 5:

[0507] The server collects temperature data of the hot water generated after cooling the computing resources. Using the temperature data from the cooling system, the system calculates the conditions suitable for hot water disinfection and formulates a hot water disinfection plan based on the conditions.

[0508] Step 6:

[0509] The user executes hot water disinfection according to the hot water disinfection plan on the server, which controls the process of sterilizing rice seeds with hot water without using pesticides.

[0510] Step 7:

[0511] The server generates hydroelectric power using the water stored in the Hyper Dam. Based on water level data from sensors installed in the dam, it calculates the amount of power to be generated and instructs the operation of the power generation equipment. This operation allows for efficient generation of electricity.

[0512] Step 8:

[0513] The server stores the weather data and energy usage data it has collected so far in a database.

[0514] Step 9:

[0515] The server analyzes the accumulated data and runs an algorithm that suggests the optimal energy mix for the next season, thereby optimizing future energy usage plans.

[0516] Example 1

[0517] 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."

[0518] The purpose of this invention is to effectively collect and analyze weather and water level data to efficiently utilize energy resources. Conventional systems merely collect data individually and use them for their respective purposes, and lack a means to integrate and utilize this data to optimize energy use. Furthermore, no concrete means are available for reducing environmental impacts or running costs. Therefore, the challenge is to achieve more advanced optimization of energy use and sustainable environmental management through comprehensive data analysis.

[0519] 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.

[0520] In this invention, the server includes means for collecting meteorological data and predicting the amount of snow and ice produced based on the meteorological data, means for monitoring water level data of rivers and dams in real time using sensors and transmitting the data to the server, and means for formulating an optimal energy resource utilization plan based on the predicted amount of snow and ice produced and the collected water level data. This makes it possible to comprehensively utilize various data to optimize energy utilization and manage sustainable resources.

[0521] "Weather data" refers to various weather-related information such as weather forecasts, temperature, precipitation, and wind speed.

[0522] "Snow and ice production" refers to the predicted amount of snow and ice that will fall and accumulate.

[0523] A "sensor" is a device for measuring physical environmental information, and in this case refers to a device for monitoring the water level of rivers and dams in real time.

[0524] "Energy resources" refers to resources that are used as energy, such as hydroelectric power generation and hot water disinfection.

[0525] "Computing device" refers to a computing resource for data processing and analysis.

[0526] "Hot water disinfection" refers to a method of sterilization or disinfection using heated water.

[0527] A "dam" refers to an artificial structure for storing water, and in this case it serves as a water source for hydroelectric power generation.

[0528] "Storage" refers to devices and systems for long-term storage of data.

[0529] "Energy mix" refers to the optimal use of different types of energy resources.

[0530] A "generative model" refers to a machine learning model that performs analysis and predictions based on specific input data.

[0531] An "actuator" refers to a mechanical device that performs a physical action based on information obtained from a sensor.

[0532] The system of the present invention integrates the collection and analysis of meteorological data, real-time monitoring of water level data, optimization of energy use, hot water use and hot water disinfection, hydroelectric power generation, and data accumulation and optimization for the next season. Specific embodiments of each processing step are described below.

[0533] Hardware and software used

[0534] Server: Collects and analyzes meteorological data, develops energy usage plans, plans hot water disinfection, operates hydroelectric power generation, and accumulates and analyzes data.

[0535] Terminal: Obtains water level data from rivers and dams from IoT sensors and sends it to a server.

[0536] Sensors: Measure water level data in rivers and dams in real time.

[0537] Machine learning models: Predict snow and ice formation based on weather data.

[0538] Database: A storage system for storing collected data and energy usage data.

[0539] Meteorological data collection and analysis

[0540] To collect weather data, the server periodically sends an HTTP GET request to a weather data provider's API (e.g., https: / / api.weather.com / data). The response contains JSON-formatted data including weather forecasts, temperature, precipitation, wind speed, etc. The server then feeds this data into a machine learning model to predict snow and ice formation.

[0541] Examples:

[0542] The server sends an HTTP GET request to the API endpoint and inputs the resulting JSON data into a Python-based machine learning model to predict future weather conditions.

[0543] Real-time monitoring of water level data

[0544] The device establishes communication with the IoT sensor and acquires real-time water level data of rivers and dams. The acquired data is sent to the server using an HTTP POST request.

[0545] Examples:

[0546] The device reads water level data using a sensor ID such as "Sensor_001" and sends an HTTP POST request to "https: / / server.endpoint / waterlevels".

[0547] Optimizing energy use

[0548] The server integrates the weather and water level data collected and runs an energy optimization algorithm to create an optimal energy usage plan.

[0549] Examples:

[0550] The server integrates weather data and water level data and runs an energy optimization algorithm (e.g., a linear optimization algorithm) to generate an optimal energy usage plan.

[0551] Use of hot water and hot water disinfection

[0552] The server uses the hot water generated after the computer cools down to calculate the conditions for hot water disinfection, and then performs hot water disinfection based on the calculated conditions.

[0553] Examples:

[0554] The server collects the temperature data of the hot water obtained from the cooling system and creates a hot water disinfection plan based on the temperature conditions, for example, to sterilize rice seeds.

[0555] Hydroelectric power generation

[0556] The server operates the hydroelectric power plant based on water level data obtained from the dam, calculates the amount of power generated, and generates electricity using water efficiently.

[0557] Examples:

[0558] The server collects water level data from sensors in the dam facility, calculates the amount of power generated, and sends operating instructions to the power generation facility.

[0559] Accumulating data and optimizing for the next season

[0560] The server collects weather data and energy usage data, stores it in a database, and then analyzes it to propose the optimal energy mix for the next season.

[0561] Examples:

[0562] The server analyzes the data stored in the database and runs an energy mix optimization algorithm to propose the optimal energy mix for the next season.

[0563] Prompt Sentence Examples

[0564] "Run an energy optimization algorithm using weather and water level data as inputs to generate an optimal energy usage plan."

[0565] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0566] Step 1:

[0567] The server collects weather data by periodically sending HTTP GET requests to the weather data provider's API (e.g., https: / / api.weather.com / data). The input required is the API endpoint and authentication information, and the output is JSON-formatted data such as the weather forecast, temperature, precipitation, and wind speed. This data is used in the next processing step.

[0568] Step 2:

[0569] The server analyzes the weather data it collects. Specifically, the server inputs the weather data into a machine learning model using Python to predict the amount of snow and ice that will be generated. The input requires weather data (weather forecast, temperature, precipitation, wind speed, etc.), and the output is the predicted amount of snow and ice that will be generated.

[0570] Step 3:

[0571] The terminal acquires water level data from the IoT sensor. Specifically, the terminal establishes communication with the sensor and reads the water level data in real time. The input requires the sensor ID and communication protocol, and the output is the latest water level data.

[0572] Step 4:

[0573] The water level data acquired by the device is sent to the server. Specifically, the device sends an HTTP POST request to "https: / / server.endpoint / waterlevels". The input requires the water level data and a destination URL, and the output is the water level data stored on the server.

[0574] Step 5:

[0575] The server integrates the collected weather and water level data to create an energy usage plan. Specifically, the server executes an energy optimization algorithm (e.g., a linear optimization algorithm). The input required is the integrated weather and water level data, and the output generated is an optimal energy usage plan.

[0576] Step 6:

[0577] The server creates a plan for hot water use and hot water disinfection. Specifically, the server collects hot water temperature data from the cooling system and calculates the conditions suitable for hot water disinfection. The input required is hot water temperature data from the cooling system, and the output generated is a hot water disinfection plan.

[0578] Step 7:

[0579] The server carries out hydroelectric power generation. Specifically, the server collects water level data from sensors installed in the dam and operates the power generation equipment based on that data. Water level data and power generation equipment control data are required as inputs, and the generated power data is obtained as output.

[0580] Step 8:

[0581] The server stores the collected data in a database and proposes the optimal energy mix for the next season. Specifically, the server analyzes the stored data and runs an energy mix optimization algorithm. All stored data is required as input, and a proposed energy mix for the next season is generated as output.

[0582] (Application example 1)

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

[0584] Conventional energy management systems were unable to effectively utilize weather and water level data, making it difficult to optimize energy use within factories. In particular, hot water disinfection and hydroelectric power generation were planned and executed separately, preventing integrated management, resulting in wasted energy resources. Furthermore, there was no established method for efficiently analyzing data using generative AI models, resulting in low accuracy in energy usage plans.

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

[0586] In this invention, the server includes a means for optimizing energy usage of equipment installed in the factory, a means for executing an optimal energy usage plan and hot water disinfection using a smart device, and a means for managing energy based on the optimized energy usage plan. This allows for the integration of weather data and water level data to optimize energy usage and efficiently manage energy resources within the factory. Furthermore, data analysis using a generative AI model can improve the accuracy of the energy usage plan and reduce wasteful energy consumption.

[0587] "Weather data" is a general term for various weather-related information such as weather forecasts, temperature, precipitation, and wind speed.

[0588] An "IoT sensor" is a sensor connected to the Internet that collects real-time data such as water levels in rivers and dams.

[0589] An "optimal energy resource utilization plan" is a specific plan for efficient energy use that calculates energy allocation and utilization methods based on collected data.

[0590] "Warm water generated after cooling of computing resources" refers to warm water generated by the cooling process of computing equipment, which can be reused.

[0591] "Hot water disinfection" is a method of disinfection using hot water that has reached a certain temperature.

[0592] "Hydroelectric power generation" is a method of generating electricity by using water stored in a dam.

[0593] A "database" is an information system for efficiently managing and storing collected data and analysis results.

[0594] "Optimizing energy use of equipment installed in factories" means optimizing energy consumption based on collected data about equipment within factories.

[0595] "Smart devices" refer to multifunctional devices with internet connectivity, such as smartphones, smart glasses, and head-mounted displays.

[0596] A "generative AI model" is a model that uses artificial intelligence to analyze data and make predictions and suggestions.

[0597] A "prompt" is an instruction or question input to a generative AI model and is used to control the model's output.

[0598] The present invention relates to a system for formulating an optimal energy resource utilization plan and for efficiently carrying out hot water disinfection and hydroelectric power generation.

[0599] Meteorological data acquisition and analysis

[0600] The server periodically sends HTTP GET requests to the weather data provider's API to collect weather forecasts, temperature, precipitation, wind speed, etc. This data is received in JSON format and then fed into a generative AI model, which uses this data to predict snow and ice formation and stores the results.

[0601] Real-time monitoring of water level data

[0602] IoT sensors collect real-time water level data from rivers and dams. The device establishes communication with the sensor and reads the latest water level data. This data is sent to the server using an HTTP POST request. The server stores this data in a database.

[0603] Optimizing energy use

[0604] The server creates an optimal energy resource utilization plan based on the collected weather and water level data. Specifically, it uses a generative AI model to analyze the data and calculate energy allocation and utilization methods. The server then manages energy within the factory based on this plan.

[0605] Use of hot water and hot water disinfection

[0606] The server uses the temperature data of the hot water generated after cooling the computing resources to calculate the optimal conditions for hot water disinfection. The server controls the hot water disinfection process using the hot water obtained from the cooling system. The server then executes the hot water disinfection based on the planned conditions.

[0607] Hydroelectric power

[0608] The server generates hydroelectric power using the water stored in the Hyper Dam. It calculates the amount of power to be generated based on the dam's water level data and instructs the actual power generation operation. This allows the dam's water to be used efficiently to generate electricity.

[0609] Accumulating data and optimizing for the next season

[0610] The server stores the collected weather and energy usage data in a database, analyzes this data, and runs an algorithm that suggests the optimal energy mix for the next season.

[0611] Hardware and software used

[0612] IoT sensor: A device that collects water level data, etc.

[0613] Server: Data analysis, energy planning

[0614] Generative AI models: Analyzing weather and water level data

[0615] Energy Management Software: Implementing Energy Planning

[0616] Cooling system: Generating hot water for hot water disinfection

[0617] Examples of prompt statements

[0618] Here are some example prompts the system might use to analyze data using a generative AI model:

[0619] "Predict the temperature and amount of snowfall for the following dates:

[0620] Date: YYYY-MM-DD

[0621] Temperature: [20, 22, 18, 21, 23]

[0622] Snowfall: [0, 15, 10, 0, 5]”

[0623] "Please suggest an energy usage plan based on:

[0624] Forecast temperature and snowfall

[0625] Real-time water level data: 4.5m

[0626] Based on this specific example, the server optimizes energy usage and determines whether the conditions for hot water disinfection are met.

[0627] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0628] Step 1:

[0629] The server periodically sends HTTP GET requests to the weather data provider's API to obtain weather data such as weather forecast, temperature, precipitation, wind speed, etc. The obtained data is passed to the server in JSON format and stored in a weather database.

[0630] Input: API endpoint information

[0631] Data processing: Extraction of weather forecast, temperature, precipitation, and wind speed data

[0632] Output: Weather data in JSON format

[0633] Step 2:

[0634] Using generative AI models, the server analyzes collected weather data and predicts the amount of snow and ice that will be produced. The predictions are stored in a database and used as the basis for energy planning.

[0635] Input: Weather data

[0636] Data Computing: Predicting with Generative AI Models

[0637] Output: Predicted snow and ice formation

[0638] Step 3:

[0639] IoT sensors collect real-time water level data from rivers and dams and send it to a server via a terminal. The server stores the received water level data in a database.

[0640] Input: Water level data from IoT sensors

[0641] Data processing: Extraction and transmission of water level data

[0642] Output: Updates to the server database

[0643] Step 4:

[0644] The server integrates the collected weather and water level data and uses a generative AI model to create an optimal energy resource utilization plan, providing the optimized energy plan to the energy management software.

[0645] Input: Weather data, water level data

[0646] Data Computing: Energy-Optimizing Computations in Generative AI Models

[0647] Output: Energy usage plan

[0648] Step 5:

[0649] The server acquires the temperature data of the hot water generated after cooling the computing resources, calculates the optimal conditions for hot water disinfection based on that data, and then performs hot water disinfection after confirming whether the optimal conditions are met.

[0650] Input: Hot water temperature data

[0651] Data calculation: Calculation of hot water disinfection conditions

[0652] Output: Instruction to perform hot water disinfection

[0653] Step 6:

[0654] The server calculates the amount of power to be generated based on the dam's water level data and issues instructions to operate the hydroelectric power generation equipment, thereby generating electricity using water efficiently.

[0655] Input: Dam water level data

[0656] Data calculation: Calculation of power generation amount

[0657] Output: Hydroelectric power generation operating instructions

[0658] Step 7:

[0659] The server stores the collected weather and energy usage data in a database and proposes an optimized energy mix for the next season.

[0660] Input: Weather data, energy usage data

[0661] Data calculation: Energy mix optimization calculation

[0662] Output: Energy mix proposal for the next season

[0663] Example prompt:

[0664] "Predict the temperature and amount of snowfall for the following dates:

[0665] Date: YYYY-MM-DD

[0666] Temperature: [20, 22, 18, 21, 23]

[0667] Snowfall: [0, 15, 10, 0, 5]”

[0668] "Please suggest an energy usage plan based on:

[0669] Forecast temperature and snowfall

[0670] Real-time water level data: 4.5m

[0671] 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.

[0672] Meteorological data acquisition and analysis

[0673] The server collects weather data. It periodically issues HTTP GET requests to the weather data provider's API and retrieves the JSON data in response. This data includes information such as weather forecast, temperature, precipitation, and wind speed. The server then uses this data to apply machine learning models to predict snow and ice formation, allowing it to understand future weather conditions.

[0674] Examples:

[0675] The server collects weather data from https: / / api.weather.com / data and uses the ML model to predict the dates and amount of snowfall. The predicted data is used in the next step.

[0676] Real-time monitoring of water level data

[0677] The device receives real-time water level data from the IoT sensors of rivers and dams. The device communicates with the sensors and reads the latest water level data. This data is sent to the server using an HTTP POST request.

[0678] Examples:

[0679] The device uses a sensor ID such as "sensor_001" to read water level data and sends it to "https: / / server.endpoint / waterlevels".

[0680] Optimizing energy use

[0681] The server creates an optimal energy resource utilization plan based on the weather and water level data it collects. The server integrates this data, calculates how to allocate and utilize energy resources, and creates a specific utilization plan based on the results.

[0682] Examples:

[0683] The server runs an energy optimization algorithm using predicted snowfall and real-time water level data as inputs to generate an energy usage plan, which determines how resources can be used efficiently.

[0684] Use of hot water and hot water disinfection

[0685] The server uses the hot water generated after cooling the computing resources to create a hot water disinfection plan. The server uses the hot water temperature data from the cooling system to calculate the conditions suitable for hot water disinfection, and then performs hot water disinfection based on that plan.

[0686] Examples:

[0687] The server collects the temperature data of the hot water obtained from the cooling system, and based on the temperature conditions, it plans the hot water disinfection and controls the process of sterilizing the rice seeds without using pesticides.

[0688] Hydroelectric power generation

[0689] The server generates hydroelectric power using the water stored in the Hyper Dam. The server calculates the amount of power to be generated based on the dam's water level data and issues instructions for the actual power generation operation. This allows the dam's water to be used efficiently to generate electricity.

[0690] Examples:

[0691] Based on water level data obtained from sensors installed in the dam, the system operates hydroelectric power generation equipment and issues instructions to optimize power generation.

[0692] Accumulating data and optimizing for the next season

[0693] The server stores the collected weather data and energy usage data in a database.

[0694] Examples:

[0695] The server analyzes weather and energy usage data stored in a database and runs algorithms to further optimize future energy usage plans, thereby optimizing the energy mix for the next season.

[0696] Emotion engine collaboration

[0697] The device is equipped with an emotion engine that collects user emotion data. The emotion engine analyzes the user's emotions in real time from their facial expressions and voice. This emotion data is sent to a server and reflected in the energy usage plan.

[0698] Examples:

[0699] When a user uses a device equipped with an emotion engine, emotional data is collected in real time and sent to a server, which then adjusts the allocation and usage of energy resources based on the collected emotional data.

[0700] Energy Use Feedback

[0701] The server provides the user with feedback on energy usage based on the data obtained from the emotion engine. This feedback is adjusted according to the user's emotional state, allowing for more appropriate energy usage plans to be proposed.

[0702] Examples:

[0703] The server receives the emotional data and provides feedback on energy usage based on the user's emotional state. For example, a user with a high stress level may receive specific feedback, such as advice on how to reduce energy costs.

[0704] The system aims to utilize data collected at each step to efficiently use natural resources as energy sources. Furthermore, by combining it with an emotion engine, it can adjust energy usage plans in real time according to the user's emotional state, helping to realize a sustainable society.

[0705] The processing flow will be explained below.

[0706] Step 1:

[0707] The server collects weather data. The server periodically sends an HTTP GET request to the weather data provider's API and obtains weather data in JSON format as a response. This data includes weather forecasts, temperature, precipitation, wind speed, etc.

[0708] Step 2:

[0709] The server analyzes the collected weather data and uses machine learning models to predict the amount of snow and ice that will be generated. The server then uses the weather data as input for the ML model and generates a forecast, which is then used in the next step.

[0710] Step 3:

[0711] The device acquires real-time water level data from the IoT sensor for the river or dam. The device communicates with the sensor and reads the latest water level data. This read data is then sent to the server using an HTTP POST request.

[0712] Step 4:

[0713] The server then integrates the received water level data with weather forecasts and uses this data to create an optimal energy resource utilization plan. Specifically, it runs an energy optimization algorithm using predicted snowfall and real-time water level data as inputs.

[0714] Step 5:

[0715] The server collects the temperature data of the hot water generated after cooling the computing resources. The server obtains the temperature data of the hot water from the cooling system and uses the data to calculate the conditions suitable for hot water disinfection.

[0716] Step 6:

[0717] The user executes the hot water disinfection according to the hot water disinfection plan of the server. Based on the instructions of the server, the process of sterilizing the rice seeds with hot water without using pesticides proceeds.

[0718] Step 7:

[0719] The server generates hydroelectric power using the water stored in the Hyper Dam. The server calculates the amount of power to be generated based on the dam's water level data and issues instructions to operate the power generation equipment. This allows for efficient power generation.

[0720] Step 8:

[0721] The server stores the weather and energy usage data collected so far in a database, which is then used to optimize energy usage plans for the next season.

[0722] Step 9:

[0723] The server analyzes the data stored in the database and runs an algorithm that suggests the optimal energy mix for the next season, thereby optimizing future energy usage plans.

[0724] Step 10:

[0725] The device is equipped with an emotion engine for collecting user emotion data, which analyzes emotions from the user's facial expressions and voice in real time and transmits this emotion data to a server.

[0726] Step 11:

[0727] The server reflects the received emotional data in its energy usage plan, adjusting the allocation and usage of energy resources based on the emotional data, and achieving optimal energy usage in line with the user's emotional state.

[0728] Step 12:

[0729] The server uses data from the emotion engine to provide users with specific feedback on energy usage based on their emotional state, such as advice on how to reduce energy costs if their stress level is high.

[0730] Example 2

[0731] 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."

[0732] In conventional energy utilization systems, weather data and water level data are collected and analyzed separately, making it difficult to integrate data in real time and efficiently optimize energy resources. Furthermore, energy utilization plans are formulated without taking into account the user's emotional state, resulting in a lack of user comfort and efficiency. Therefore, the challenge is to provide a system that integrates weather data and water level data and optimizes energy utilization plans in real time, reflecting the user's emotional data.

[0733] 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.

[0734] In this invention, the server includes means for collecting meteorological data and predicting the amount of snow and ice produced based on the meteorological data, means for monitoring water level data of rivers and dams in real time using sensors and transmitting the data to the server, and means for formulating an optimal energy resource utilization plan based on the predicted amount of snow and ice produced and the collected water level data. This makes it possible to integrate the meteorological data and water level data and formulate an optimal energy resource utilization plan in real time.

[0735] "Weather Data" refers to information about climatic conditions such as temperature, precipitation, wind speed, and weather forecasts.

[0736] "Snow and ice generation forecasting" refers to the use of machine learning models and algorithms to estimate future snow and ice volumes.

[0737] A "sensor" refers to a device that detects physical conditions or changes and acquires them as data.

[0738] "Water level data" refers to information showing the water height of rivers and dams.

[0739] "Server" refers to a computer system that collects, analyzes, stores, and distributes data.

[0740] "Optimal energy resource utilization plan" refers to a plan for efficient use of energy resources based on collected data.

[0741] "Warm water generated after cooling computing resources" refers to heated water generated during the cooling process of computers and servers.

[0742] "Hot water disinfection" refers to a method of using heated water to kill microorganisms and pathogens.

[0743] A "reservoir" refers to an artificial storage facility for storing water.

[0744] "Hydroelectric power" refers to a method of generating electricity using the power of flowing water.

[0745] "Database" refers to a system for efficiently managing, storing, and retrieving data.

[0746] "Energy mix" refers to a plan for using an appropriate combination of different types of energy sources.

[0747] "Emotional data" refers to information about the emotional state of a user analyzed from facial expressions and voice.

[0748] "Energy usage feedback" refers to providing information and advice on energy usage based on the user's behavior and situation.

[0749] MODE FOR CARRYING OUT THE INVENTION

[0750] The present invention is a system for efficiently utilizing natural resources as an energy source, and includes the following elements:

[0751] Meteorological data acquisition and analysis

[0752] The server sends an HTTP GET request to the API of an external weather data provider and collects the JSON-formatted weather data received as a response. This weather data includes information such as temperature, precipitation, wind speed, and weather forecast. The server uses machine learning models such as TensorFlow to predict the amount of snow and ice that will be generated from this data. Specifically, the server collects data from "https: / / api.weather.com / data" and inputs it into the machine learning model to predict snowfall.

[0753] Example prompt sentence:

[0754] "How does the server collect weather data and use machine learning models to predict snowfall?"

[0755] Real-time monitoring of water level data

[0756] The device obtains water level data in real time from IoT sensors installed in rivers and dams. The sensor and device communicate, and the device reads the latest water level data. This data is sent to the server via an HTTP POST request using the sensor ID (e.g., "sensor_001"). Specifically, the data is sent to "https: / / server.endpoint / waterlevels."

[0757] Example prompt sentence:

[0758] "Please explain how the device will send the water level data obtained from the IoT sensor to the server in real time."

[0759] Optimizing energy use

[0760] The server integrates the collected weather and water level data and creates an optimal energy resource utilization plan based on this data. The server analyzes the data using Python libraries (e.g., SciPy, Pandas) and executes energy optimization algorithms. The generated energy utilization plan is saved in JSON format.

[0761] Example prompt sentence:

[0762] "Explain how weather and water level data collected by a server can be used to optimize energy usage planning."

[0763] Use of hot water and hot water disinfection

[0764] The server collects temperature data from the hot water generated during the cooling process of computing resources and creates a hot water disinfection plan.The server calculates temperature conditions using Matplotlib based on the data obtained from the cooling system and carries out hot water disinfection based on that plan.Specifically, it controls the process of sterilizing rice seeds without using pesticides.

[0765] Example prompt sentence:

[0766] "Please tell me how to use the hot water generated by the server after cooling the computing resources to perform hot water disinfection."

[0767] Hydroelectric power generation

[0768] The server calculates the amount of power generated based on the water level data stored in the reservoir and issues instructions for operating the hydroelectric power plant.The server then sends operation instructions to the PLC using the MODBUS protocol to optimize the power generation equipment.

[0769] Example prompt sentence:

[0770] "Explain how the server generates hydroelectric power based on reservoir water level data."

[0771] Accumulating data and optimizing for the next season

[0772] The server stores the collected weather and energy usage data in a database (e.g., MongoDB or AWS DynamoDB). This data is analyzed using R or Python to run algorithms that optimize the energy mix for the next season.

[0773] Example prompt sentence:

[0774] "Please tell me how the data collected by the server will be used to optimize energy usage plans for the next season."

[0775] Emotion engine collaboration

[0776] The device uses an emotion engine (e.g., Affectiva SDK) to collect the user's emotion data. The device analyzes the user's facial expressions and voice and transmits the emotion data to the server in real time. The analyzed emotion data is then sent to the server using WebSocket.

[0777] Example prompt sentence:

[0778] "Please tell me how the server uses the emotional data collected by the device's emotion engine to reflect in the energy usage plan."

[0779] Energy Use Feedback

[0780] The server provides users with feedback on energy usage based on the emotional data obtained from the emotion engine. Based on the analysis results, the server suggests optimal energy usage methods according to the user's emotional state. The feedback is provided to users via a mobile app or web portal.

[0781] Example prompt sentence:

[0782] "Explain how the server provides feedback on energy usage based on data obtained from the emotion engine."

[0783] As described above, the present invention provides a system for efficiently utilizing natural resources as energy sources through the collection, analysis, and optimization of various data.

[0784] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0785] Step 1:

[0786] The server collects weather data. Specifically, it periodically sends HTTP GET requests from "https: / / api.weather.com / data" and obtains weather data in JSON format as a response. This data includes temperature, precipitation, wind speed, weather forecast, etc. The HTTP request is required as input, and the obtained weather data in JSON format is obtained as output. The server analyzes the obtained data and stores it in a database.

[0787] Step 2:

[0788] The server uses the weather data acquired to predict the amount of snow and ice produced using a machine learning model. Specifically, preprocessed data is input into the model using TensorFlow to obtain the predicted results. JSON data and a machine learning model are required as input, and the predicted amount of snow and ice produced is obtained as output. Furthermore, the prediction results are stored in a database for use in the next step.

[0789] Step 3:

[0790] The device retrieves water level data from an IoT sensor. Specifically, it uses a sensor ID, such as "sensor_001", to collect water level data in real time. As input, it requires the sensor ID and communication protocol, and as output, it gets the latest water level data. This data is sent to the server via an HTTP POST request.

[0791] Step 4:

[0792] The server receives water level data, stores it in a database, and integrates it with weather data to optimize the energy usage plan. Specifically, it integrates the data using Python's Pandas library and runs an optimization algorithm using SciPy. Weather and water level data are required as input, and the optimized energy usage plan is obtained as output. This plan is saved in JSON format.

[0793] Step 5:

[0794] The server collects the hot water temperature data from the cooling system. Specifically, it obtains the data from the temperature sensor provided by the cooling system and creates a hot water disinfection plan. The input is the temperature data, and the output is the temperature conditions suitable for disinfection and a specific operation plan. Based on this plan, a signal to control the disinfection process is sent.

[0795] Step 6:

[0796] The server calculates the amount of power generation based on the water level data stored in the Hyper Dam and issues instructions for operating the hydroelectric power plant. Specifically, it uses the MODBUS protocol to send operation instructions to the PLC to optimize the power generation equipment. The inputs required are water level data and a power generation plan, and the output is the actual power generation operation instructions.

[0797] Step 7:

[0798] The server stores all collected data and energy usage data in a database. Specifically, it uses MongoDB or AWS DynamoDB to store the data and then proposes the optimal energy mix for the next season. All collected data is required as input, and the optimized energy mix plan is obtained as output.

[0799] Step 8:

[0800] The device collects the user's emotional data and sends it to the server. Specifically, it uses an emotion engine such as the Affectiva SDK to analyze the user's facial expressions and voice. The input is the user's facial and voice data, and the output is the emotion analysis results. This data is sent to the server in real time.

[0801] Step 9:

[0802] The server provides users with feedback on energy usage based on the emotional data. Specifically, it analyzes the collected emotional and energy usage data and proposes optimal energy usage methods. The inputs are the emotional and energy usage data, and the output is feedback information provided to the user. The user receives the feedback through a mobile app or web portal.

[0803] In this manner, the system of the present invention specifically implements process steps for efficiently utilizing natural resources as energy sources.

[0804] (Application example 2)

[0805] 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."

[0806] Conventional energy usage planning systems have limited means for optimizing energy consumption while efficiently utilizing weather and water level data, making energy management particularly difficult in physical stores. Furthermore, they are unable to dynamically adjust energy usage plans based on user sentiment. This has resulted in insufficient optimization of the user experience and efficient energy consumption.

[0807] 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.

[0808] In this invention, the server includes: means for collecting weather data and predicting the amount of snow and ice generated based on the weather data; means for monitoring water level data of rivers and dams in real time using IoT sensors and transmitting the data to the server; means for formulating an optimal energy resource utilization plan based on the predicted generation amount and the collected water level data; means for formulating a hot water disinfection plan using hot water generated after cooling the computing resources; means for performing hot water disinfection; means for generating hydroelectric power using water stored in a hyper dam; means for storing the collected data and energy utilization data in a database and proposing an optimal energy mix for the next season; means for optimizing energy consumption and automatically controlling the utilization plan using smart glasses to operate the store; means for automatically adjusting temperature control and lighting in the store in cooperation with weather data and water level data; and means for collecting customer emotion data using an emotion engine and adjusting the energy utilization plan. This enables an energy management system that can efficiently consume energy in physical stores while providing a comfortable environment for customers.

[0809] "Weather data" refers to data relating to weather conditions such as weather forecasts, temperature, precipitation, and wind speed.

[0810] "Snow and ice production" refers to the amount of snow and ice production predicted based on collected meteorological data.

[0811] An "IoT sensor" is a sensor that connects to the Internet and measures and monitors environmental conditions, machine operation, and other factors in real time.

[0812] "Water level data" refers to data relating to the water level of bodies of water such as rivers and dams.

[0813] "Energy resources" are energy supply sources such as electricity, gas, and oil.

[0814] "Optimal energy resource utilization planning" is a concept that indicates planning the efficient utilization of energy resources based on collected data.

[0815] "Hot water disinfection" is a method of disinfecting microorganisms such as pathogens and viruses using hot water.

[0816] A "hyper dam" is a large dam that stores a large amount of water to generate hydroelectric power.

[0817] "Hydroelectric power generation" is a method of generating electricity by utilizing the potential energy of water flow and elevation.

[0818] "Smart glasses" are glasses-type devices worn by users that can display augmented reality (AR) and other information.

[0819] The "emotion engine" is a system for analyzing the user's emotional state from facial expressions, voice, etc.

[0820] "Energy mix" is a concept that refers to optimizing overall energy supply by combining different energy sources.

[0821] A "database" is a system for efficiently storing, searching, managing, and updating various types of data.

[0822] A "server" is a computer system that stores, processes, and transmits data over a network.

[0823] The system of the present invention collects meteorological data and water level data, and based on this data, formulates an optimal energy resource utilization plan. This system is realized through the following steps.

[0824] First, the server collects weather data by periodically issuing an HTTP GET request to the weather data provider's API and obtaining the JSON data in response. This data includes information such as weather forecast, temperature, precipitation, and wind speed. Next, the server uses this data to apply machine learning models to predict the amount of snow and ice formation.

[0825] Next, the device uses IoT sensors to obtain real-time water level data from rivers and dams. This data is then sent to the server using an HTTP POST request. The server then integrates the obtained weather and water level data and uses them to create an optimal energy resource utilization plan.

[0826] The system also includes a plan for hot water disinfection using the hot water generated after the computing resources are cooled. The hot water disinfection plan is calculated on the server based on the hot water temperature data from the cooling system. Specifically, it controls the process of sterilizing rice seeds without using pesticides.

[0827] Hydroelectric power generation using water stored in a hyper dam is also an important element of this invention. The server calculates the amount of power to be generated based on the dam's water level data and issues instructions for the actual power generation operation.

[0828] The server also stores all collected data and energy usage data in a database and runs algorithms to suggest an optimized energy mix for the next season.

[0829] Furthermore, this system utilizes smart glasses and an emotion engine to automatically optimize energy consumption in physical store operations. The user device, equipped with smart glasses, collects customers' emotional data in real time and sends it to a server. Based on the collected emotional data, the server automatically adjusts the temperature and lighting in the store, providing a comfortable environment for customers and simultaneously optimizing energy management.

[0830] As a concrete example, a physical store can automatically adjust air conditioning and lighting settings based on weather data, according to predicted temperatures and weather changes. Also, an emotion engine analyzes customers' emotional data and sets the environment to make them feel comfortable. This makes it possible to achieve both efficient energy use and customer satisfaction.

[0831] The following example prompts are also set up as specific scenarios:

[0832] "Users visit the store

[0833] The emotion engine analyzes customer sentiment data in real time and uses it as input to optimize energy consumption.

[0834] It runs energy optimization algorithms based on weather and water level data.

[0835] Energy optimization plans are immediately reflected in store temperature settings, lighting, and air conditioning system adjustments.

[0836] The user receives a presentation of their emotional state through smart glasses.

[0837] Depending on your emotional state, lighting and background music settings in the store will be adjusted to support a comfortable stay.

[0838] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0839] Step 1:

[0840] A server collects weather data.

[0841] Input: Weather data provider's API.

[0842] Data processing: Issue an HTTP GET request and analyze the JSON data obtained as a response.

[0843] Output: Obtain weather information such as weather forecast, temperature, precipitation, wind speed, etc.

[0844] Specific operation: The server periodically calls the API and stores the acquired data in an internal database.

[0845] Step 2:

[0846] The server uses machine learning models to predict the amount of snow and ice that will form.

[0847] Input: Weather data obtained in step 1.

[0848] Data calculation: Based on the acquired weather data, a machine learning model is used to predict the amount of snow and ice that will be generated.

[0849] Output: Predicted snow and ice production.

[0850] Specific operation: The server inputs weather data into the model, obtains and stores the forecast results.

[0851] Step 3:

[0852] The device uses IoT sensors to obtain real-time water level data for rivers and dams.

[0853] Input: Water level data from IoT sensors.

[0854] Data processing: Water level data is sent to the server using an HTTP POST request.

[0855] Output: Real-time water level data.

[0856] Specific operation: The terminal communicates with the IoT sensor and sends the water level data it reads to the server.

[0857] Step 4:

[0858] The server integrates the weather and water level data collected to create an optimal plan for using energy resources.

[0859] Input: Weather data for step 2, water level data for step 3.

[0860] Data Computing: Integrates data and runs energy optimization algorithms.

[0861] Output: Energy resource utilization plan.

[0862] How it works: The server inputs weather and water level data into the algorithm, generates and saves an optimized plan.

[0863] Step 5:

[0864] A hot water disinfection plan is created using the hot water generated after the server cools down the computing resources.

[0865] Input: Hot water temperature data from the cooling system.

[0866] Data calculation: Calculates the optimal conditions for hot water disinfection based on hot water temperature data.

[0867] Output: Plan for hot water disinfection.

[0868] Specific operation: The server analyzes the hot water temperature data and sets appropriate disinfection conditions.

[0869] Step 6:

[0870] The device uses smart glasses to collect emotional data from customers in real time and transmits it to a server.

[0871] Input: Smart glasses emotion engine data.

[0872] Data processing: Emotion data is sent to the server using an HTTP POST request.

[0873] Output: Real-time sentiment data.

[0874] Specific operation: The device works in conjunction with smart glasses to read emotion data and send it to the server.

[0875] Step 7:

[0876] The server automatically adjusts temperature control and lighting in the store based on emotional data.

[0877] Input: Emotion data from step 6.

[0878] Data calculation: Analyzes emotional data and calculates optimal environment settings.

[0879] Output: Temperature and lighting settings in the store.

[0880] Specific operation: The server analyzes the emotional data and provides appropriate settings for temperature control and lighting systems.

[0881] Step 8:

[0882] The server stores all collected data and energy usage data in a database and proposes an optimized energy mix for the next season.

[0883] Input: Weather data, water level data, energy usage data, emotion data.

[0884] Data processing: Store data in a database and run analysis algorithms.

[0885] Output: Proposal for optimising the energy mix for the next season.

[0886] How it works: The server stores the data in a database and uses an algorithm to generate a plan for the next season.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] [Third embodiment]

[0891] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0892] 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.

[0893] 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).

[0894] 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.

[0895] 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.

[0896] 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).

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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."

[0903] Meteorological data acquisition and analysis

[0904] The server collects weather data. It periodically sends HTTP GET requests to the weather data provider's API and receives JSON-formatted data in response. This data includes weather forecasts, temperature, precipitation, wind speed, etc. The server then uses this data to apply machine learning models to predict snow and ice formation, which allows it to understand future weather conditions.

[0905] Examples:

[0906] The server collects weather data from a URL such as "https: / / api.weather.com / data" and uses an ML model to predict the dates and amount of snowfall. The predicted data is used in the next step.

[0907] Real-time monitoring of water level data

[0908] The device obtains real-time water level data from the IoT sensors of rivers and dams. The device establishes communication with the sensors and reads the latest water level data. This data is sent to the server using an HTTP POST request.

[0909] Examples:

[0910] The device uses a sensor ID such as "sensor_001" to read water level data and sends it to "https: / / server.endpoint / waterlevels".

[0911] Optimizing energy use

[0912] The server creates an optimal energy resource utilization plan based on the weather and water level data it collects. The server integrates this data, calculates how to allocate and utilize energy resources, and creates a specific utilization plan based on the results.

[0913] Examples:

[0914] The server runs an energy optimization algorithm using predicted snowfall and real-time water level data as inputs to generate an energy usage plan, which determines how resources can be used efficiently.

[0915] Use of hot water and hot water disinfection

[0916] The server uses the hot water generated after cooling the computing resources to create a hot water disinfection plan. The server uses the hot water temperature data from the cooling system to calculate the conditions suitable for hot water disinfection, and then performs hot water disinfection based on that plan.

[0917] Examples:

[0918] The server collects the temperature data of the hot water obtained from the cooling system, and based on the temperature conditions, it plans the hot water disinfection and controls the process of sterilizing the rice seeds without using pesticides.

[0919] Hydroelectric power generation

[0920] The server generates hydroelectric power using the water stored in the Hyper Dam. The server calculates the amount of power to be generated based on the dam's water level data and issues instructions for the actual power generation operation. This allows the dam's water to be used efficiently to generate electricity.

[0921] Examples:

[0922] Based on water level data obtained from sensors installed in the dam, the system operates hydroelectric power generation equipment and issues instructions to optimize power generation.

[0923] Accumulating data and optimizing for the next season

[0924] The server collects weather and energy usage data and stores it in a database. The server analyzes this data and proposes the optimal energy mix for the next season.

[0925] Examples:

[0926] The server analyzes weather and energy usage data stored in a database and runs algorithms to further optimize future energy usage plans, thereby optimizing the energy mix for the next season.

[0927] The system aims to utilize data collected at each step to efficiently use natural resources as an energy source, thereby reducing energy waste and helping to realize a sustainable society.

[0928] The processing flow will be explained below.

[0929] Step 1:

[0930] The server collects weather data by periodically issuing an HTTP GET request to the weather data provider's API and retrieving the JSON data returned as a response. This data includes information such as weather forecast, temperature, precipitation, and wind speed.

[0931] Step 2:

[0932] The server analyzes the collected weather data and uses machine learning models to predict the amount of snow and ice that will be produced, which is then used in the next step.

[0933] Step 3:

[0934] The device acquires real-time water level data from the IoT sensor for the river or dam. The device communicates with the sensor and reads the latest water level data. This read data is sent to the server using an HTTP POST request.

[0935] Step 4:

[0936] The server integrates the received water level data with weather data forecasts and then creates an optimal energy resource utilization plan. The server inputs the predicted snowfall amount and real-time water level data, runs the energy optimization algorithm, and generates a specific utilization plan.

[0937] Step 5:

[0938] The server collects temperature data of the hot water generated after cooling the computing resources. Using the temperature data from the cooling system, the system calculates the conditions suitable for hot water disinfection and formulates a hot water disinfection plan based on the conditions.

[0939] Step 6:

[0940] The user executes hot water disinfection according to the hot water disinfection plan on the server, which controls the process of sterilizing rice seeds with hot water without using pesticides.

[0941] Step 7:

[0942] The server generates hydroelectric power using the water stored in the Hyper Dam. Based on water level data from sensors installed in the dam, it calculates the amount of power to be generated and instructs the operation of the power generation equipment. This operation allows for efficient generation of electricity.

[0943] Step 8:

[0944] The server stores the weather data and energy usage data it has collected so far in a database.

[0945] Step 9:

[0946] The server analyzes the accumulated data and runs an algorithm that suggests the optimal energy mix for the next season, thereby optimizing future energy usage plans.

[0947] Example 1

[0948] 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."

[0949] The purpose of this invention is to effectively collect and analyze weather and water level data to efficiently utilize energy resources. Conventional systems merely collect data individually and use them for their respective purposes, and lack a means to integrate and utilize this data to optimize energy use. Furthermore, no concrete means are available for reducing environmental impacts or running costs. Therefore, the challenge is to achieve more advanced optimization of energy use and sustainable environmental management through comprehensive data analysis.

[0950] 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.

[0951] In this invention, the server includes means for collecting meteorological data and predicting the amount of snow and ice produced based on the meteorological data, means for monitoring water level data of rivers and dams in real time using sensors and transmitting the data to the server, and means for formulating an optimal energy resource utilization plan based on the predicted amount of snow and ice produced and the collected water level data. This makes it possible to comprehensively utilize various data to optimize energy utilization and manage sustainable resources.

[0952] "Weather data" refers to various weather-related information such as weather forecasts, temperature, precipitation, and wind speed.

[0953] "Snow and ice production" refers to the predicted amount of snow and ice that will fall and accumulate.

[0954] A "sensor" is a device for measuring physical environmental information, and in this case refers to a device for monitoring the water level of rivers and dams in real time.

[0955] "Energy resources" refers to resources that are used as energy, such as hydroelectric power generation and hot water disinfection.

[0956] "Computing device" refers to a computing resource for data processing and analysis.

[0957] "Hot water disinfection" refers to a method of sterilization or disinfection using heated water.

[0958] A "dam" refers to an artificial structure for storing water, and in this case it serves as a water source for hydroelectric power generation.

[0959] "Storage" refers to devices and systems for long-term storage of data.

[0960] "Energy mix" refers to the optimal use of different types of energy resources.

[0961] A "generative model" refers to a machine learning model that performs analysis and predictions based on specific input data.

[0962] An "actuator" refers to a mechanical device that performs a physical action based on information obtained from a sensor.

[0963] The system of the present invention integrates the collection and analysis of meteorological data, real-time monitoring of water level data, optimization of energy use, hot water use and hot water disinfection, hydroelectric power generation, and data accumulation and optimization for the next season. Specific embodiments of each processing step are described below.

[0964] Hardware and software used

[0965] Server: Collects and analyzes meteorological data, develops energy usage plans, plans hot water disinfection, operates hydroelectric power generation, and accumulates and analyzes data.

[0966] Terminal: Obtains water level data from rivers and dams from IoT sensors and sends it to a server.

[0967] Sensors: Measure water level data in rivers and dams in real time.

[0968] Machine learning models: Predict snow and ice formation based on weather data.

[0969] Database: A storage system for storing collected data and energy usage data.

[0970] Meteorological data collection and analysis

[0971] To collect weather data, the server periodically sends an HTTP GET request to a weather data provider's API (e.g., https: / / api.weather.com / data). The response contains JSON-formatted data including weather forecasts, temperature, precipitation, wind speed, etc. The server then feeds this data into a machine learning model to predict snow and ice formation.

[0972] Examples:

[0973] The server sends an HTTP GET request to the API endpoint and inputs the resulting JSON data into a Python-based machine learning model to predict future weather conditions.

[0974] Real-time monitoring of water level data

[0975] The device establishes communication with the IoT sensor and acquires real-time water level data of rivers and dams. The acquired data is sent to the server using an HTTP POST request.

[0976] Examples:

[0977] The device reads water level data using a sensor ID such as "Sensor_001" and sends an HTTP POST request to "https: / / server.endpoint / waterlevels".

[0978] Optimizing energy use

[0979] The server integrates the weather and water level data collected and runs an energy optimization algorithm to create an optimal energy usage plan.

[0980] Examples:

[0981] The server integrates weather data and water level data and runs an energy optimization algorithm (e.g., a linear optimization algorithm) to generate an optimal energy usage plan.

[0982] Use of hot water and hot water disinfection

[0983] The server uses the hot water generated after the computer cools down to calculate the conditions for hot water disinfection, and then performs hot water disinfection based on the calculated conditions.

[0984] Examples:

[0985] The server collects the temperature data of the hot water obtained from the cooling system and creates a hot water disinfection plan based on the temperature conditions, for example, to sterilize rice seeds.

[0986] Hydroelectric power generation

[0987] The server operates the hydroelectric power plant based on water level data obtained from the dam, calculates the amount of power generated, and generates electricity using water efficiently.

[0988] Examples:

[0989] The server collects water level data from sensors in the dam facility, calculates the amount of power generated, and sends operating instructions to the power generation facility.

[0990] Accumulating data and optimizing for the next season

[0991] The server collects weather data and energy usage data, stores it in a database, and then analyzes it to propose the optimal energy mix for the next season.

[0992] Examples:

[0993] The server analyzes the data stored in the database and runs an energy mix optimization algorithm to propose the optimal energy mix for the next season.

[0994] Prompt Sentence Examples

[0995] "Run an energy optimization algorithm using weather and water level data as inputs to generate an optimal energy usage plan."

[0996] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0997] Step 1:

[0998] The server collects weather data by periodically sending HTTP GET requests to the weather data provider's API (e.g., https: / / api.weather.com / data). The input required is the API endpoint and authentication information, and the output is JSON-formatted data such as the weather forecast, temperature, precipitation, and wind speed. This data is used in the next processing step.

[0999] Step 2:

[1000] The server analyzes the weather data it collects. Specifically, the server inputs the weather data into a machine learning model using Python to predict the amount of snow and ice that will be generated. The input requires weather data (weather forecast, temperature, precipitation, wind speed, etc.), and the output is the predicted amount of snow and ice that will be generated.

[1001] Step 3:

[1002] The terminal acquires water level data from the IoT sensor. Specifically, the terminal establishes communication with the sensor and reads the water level data in real time. The input requires the sensor ID and communication protocol, and the output is the latest water level data.

[1003] Step 4:

[1004] The water level data acquired by the device is sent to the server. Specifically, the device sends an HTTP POST request to "https: / / server.endpoint / waterlevels". The input requires the water level data and a destination URL, and the output is the water level data stored on the server.

[1005] Step 5:

[1006] The server integrates the collected weather and water level data to create an energy usage plan. Specifically, the server executes an energy optimization algorithm (e.g., a linear optimization algorithm). The input required is the integrated weather and water level data, and the output generated is an optimal energy usage plan.

[1007] Step 6:

[1008] The server creates a plan for hot water use and hot water disinfection. Specifically, the server collects hot water temperature data from the cooling system and calculates the conditions suitable for hot water disinfection. The input required is hot water temperature data from the cooling system, and the output generated is a hot water disinfection plan.

[1009] Step 7:

[1010] The server carries out hydroelectric power generation. Specifically, the server collects water level data from sensors installed in the dam and operates the power generation equipment based on that data. Water level data and power generation equipment control data are required as inputs, and the generated power data is obtained as output.

[1011] Step 8:

[1012] The server stores the collected data in a database and proposes the optimal energy mix for the next season. Specifically, the server analyzes the stored data and runs an energy mix optimization algorithm. All stored data is required as input, and a proposed energy mix for the next season is generated as output.

[1013] (Application example 1)

[1014] 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."

[1015] Conventional energy management systems were unable to effectively utilize weather and water level data, making it difficult to optimize energy use within factories. In particular, hot water disinfection and hydroelectric power generation were planned and executed separately, preventing integrated management, resulting in wasted energy resources. Furthermore, there was no established method for efficiently analyzing data using generative AI models, resulting in low accuracy in energy usage plans.

[1016] 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.

[1017] In this invention, the server includes a means for optimizing energy usage of equipment installed in the factory, a means for executing an optimal energy usage plan and hot water disinfection using a smart device, and a means for managing energy based on the optimized energy usage plan. This allows for the integration of weather data and water level data to optimize energy usage and efficiently manage energy resources within the factory. Furthermore, data analysis using a generative AI model can improve the accuracy of the energy usage plan and reduce wasteful energy consumption.

[1018] "Weather data" is a general term for various weather-related information such as weather forecasts, temperature, precipitation, and wind speed.

[1019] An "IoT sensor" is a sensor connected to the Internet that collects real-time data such as water levels in rivers and dams.

[1020] An "optimal energy resource utilization plan" is a specific plan for efficient energy use that calculates energy allocation and utilization methods based on collected data.

[1021] "Warm water generated after cooling of computing resources" refers to warm water generated by the cooling process of computing equipment, which can be reused.

[1022] "Hot water disinfection" is a method of disinfection using hot water that has reached a certain temperature.

[1023] "Hydroelectric power generation" is a method of generating electricity by using water stored in a dam.

[1024] A "database" is an information system for efficiently managing and storing collected data and analysis results.

[1025] "Optimizing energy use of equipment installed in factories" means optimizing energy consumption based on collected data about equipment within factories.

[1026] "Smart devices" refer to multifunctional devices with internet connectivity, such as smartphones, smart glasses, and head-mounted displays.

[1027] A "generative AI model" is a model that uses artificial intelligence to analyze data and make predictions and suggestions.

[1028] A "prompt" is an instruction or question input to a generative AI model and is used to control the model's output.

[1029] The present invention relates to a system for formulating an optimal energy resource utilization plan and for efficiently carrying out hot water disinfection and hydroelectric power generation.

[1030] Meteorological data acquisition and analysis

[1031] The server periodically sends HTTP GET requests to the weather data provider's API to collect weather forecasts, temperature, precipitation, wind speed, etc. This data is received in JSON format and then fed into a generative AI model, which uses this data to predict snow and ice formation and stores the results.

[1032] Real-time monitoring of water level data

[1033] IoT sensors collect real-time water level data from rivers and dams. The device establishes communication with the sensor and reads the latest water level data. This data is sent to the server using an HTTP POST request. The server stores this data in a database.

[1034] Optimizing energy use

[1035] The server creates an optimal energy resource utilization plan based on the collected weather and water level data. Specifically, it uses a generative AI model to analyze the data and calculate energy allocation and utilization methods. The server then manages energy within the factory based on this plan.

[1036] Use of hot water and hot water disinfection

[1037] The server uses the temperature data of the hot water generated after cooling the computing resources to calculate the optimal conditions for hot water disinfection. The server controls the hot water disinfection process using the hot water obtained from the cooling system. The server then executes the hot water disinfection based on the planned conditions.

[1038] Hydroelectric power

[1039] The server generates hydroelectric power using the water stored in the Hyper Dam. It calculates the amount of power to be generated based on the dam's water level data and instructs the actual power generation operation. This allows the dam's water to be used efficiently to generate electricity.

[1040] Accumulating data and optimizing for the next season

[1041] The server stores the collected weather and energy usage data in a database, analyzes this data, and runs an algorithm that suggests the optimal energy mix for the next season.

[1042] Hardware and software used

[1043] IoT sensor: A device that collects water level data, etc.

[1044] Server: Data analysis, energy planning

[1045] Generative AI models: Analyzing weather and water level data

[1046] Energy Management Software: Implementing Energy Planning

[1047] Cooling system: Generating hot water for hot water disinfection

[1048] Examples of prompt statements

[1049] Here are some example prompts the system might use to analyze data using a generative AI model:

[1050] "Predict the temperature and amount of snowfall for the following dates:

[1051] Date: YYYY-MM-DD

[1052] Temperature: [20, 22, 18, 21, 23]

[1053] Snowfall: [0, 15, 10, 0, 5]”

[1054] "Please suggest an energy usage plan based on:

[1055] Forecast temperature and snowfall

[1056] Real-time water level data: 4.5m

[1057] Based on this specific example, the server optimizes energy usage and determines whether the conditions for hot water disinfection are met.

[1058] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1059] Step 1:

[1060] The server periodically sends HTTP GET requests to the weather data provider's API to obtain weather data such as weather forecast, temperature, precipitation, wind speed, etc. The obtained data is passed to the server in JSON format and stored in a weather database.

[1061] Input: API endpoint information

[1062] Data processing: Extraction of weather forecast, temperature, precipitation, and wind speed data

[1063] Output: Weather data in JSON format

[1064] Step 2:

[1065] Using generative AI models, the server analyzes collected weather data and predicts the amount of snow and ice that will be produced. The predictions are stored in a database and used as the basis for energy planning.

[1066] Input: Weather data

[1067] Data Computing: Predicting with Generative AI Models

[1068] Output: Predicted snow and ice formation

[1069] Step 3:

[1070] IoT sensors collect real-time water level data from rivers and dams and send it to a server via a terminal. The server stores the received water level data in a database.

[1071] Input: Water level data from IoT sensors

[1072] Data processing: Extraction and transmission of water level data

[1073] Output: Updates to the server database

[1074] Step 4:

[1075] The server integrates the collected weather and water level data and uses a generative AI model to create an optimal energy resource utilization plan, providing the optimized energy plan to the energy management software.

[1076] Input: Weather data, water level data

[1077] Data Computing: Energy-Optimizing Computations in Generative AI Models

[1078] Output: Energy usage plan

[1079] Step 5:

[1080] The server acquires the temperature data of the hot water generated after cooling the computing resources, calculates the optimal conditions for hot water disinfection based on that data, and then performs hot water disinfection after confirming whether the optimal conditions are met.

[1081] Input: Hot water temperature data

[1082] Data calculation: Calculation of hot water disinfection conditions

[1083] Output: Instruction to perform hot water disinfection

[1084] Step 6:

[1085] The server calculates the amount of power to be generated based on the dam's water level data and issues instructions to operate the hydroelectric power generation equipment, thereby generating electricity using water efficiently.

[1086] Input: Dam water level data

[1087] Data calculation: Calculation of power generation amount

[1088] Output: Hydroelectric power generation operating instructions

[1089] Step 7:

[1090] The server stores the collected weather and energy usage data in a database and proposes an optimized energy mix for the next season.

[1091] Input: Weather data, energy usage data

[1092] Data calculation: Energy mix optimization calculation

[1093] Output: Energy mix proposal for the next season

[1094] Example prompt:

[1095] "Predict the temperature and amount of snowfall for the following dates:

[1096] Date: YYYY-MM-DD

[1097] Temperature: [20, 22, 18, 21, 23]

[1098] Snowfall: [0, 15, 10, 0, 5]”

[1099] "Please suggest an energy usage plan based on:

[1100] Forecast temperature and snowfall

[1101] Real-time water level data: 4.5m

[1102] 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.

[1103] Meteorological data acquisition and analysis

[1104] The server collects weather data. It periodically issues HTTP GET requests to the weather data provider's API and retrieves the JSON data in response. This data includes information such as weather forecast, temperature, precipitation, and wind speed. The server then uses this data to apply machine learning models to predict snow and ice formation, allowing it to understand future weather conditions.

[1105] Examples:

[1106] The server collects weather data from https: / / api.weather.com / data and uses the ML model to predict the dates and amount of snowfall. The predicted data is used in the next step.

[1107] Real-time monitoring of water level data

[1108] The device receives real-time water level data from the IoT sensors of rivers and dams. The device communicates with the sensors and reads the latest water level data. This data is sent to the server using an HTTP POST request.

[1109] Examples:

[1110] The device uses a sensor ID such as "sensor_001" to read water level data and sends it to "https: / / server.endpoint / waterlevels".

[1111] Optimizing energy use

[1112] The server creates an optimal energy resource utilization plan based on the weather and water level data it collects. The server integrates this data, calculates how to allocate and utilize energy resources, and creates a specific utilization plan based on the results.

[1113] Examples:

[1114] The server runs an energy optimization algorithm using predicted snowfall and real-time water level data as inputs to generate an energy usage plan, which determines how resources can be used efficiently.

[1115] Use of hot water and hot water disinfection

[1116] The server uses the hot water generated after cooling the computing resources to create a hot water disinfection plan. The server uses the hot water temperature data from the cooling system to calculate the conditions suitable for hot water disinfection, and then performs hot water disinfection based on that plan.

[1117] Examples:

[1118] The server collects the temperature data of the hot water obtained from the cooling system, and based on the temperature conditions, it plans the hot water disinfection and controls the process of sterilizing the rice seeds without using pesticides.

[1119] Hydroelectric power generation

[1120] The server generates hydroelectric power using the water stored in the Hyper Dam. The server calculates the amount of power to be generated based on the dam's water level data and issues instructions for the actual power generation operation. This allows the dam's water to be used efficiently to generate electricity.

[1121] Examples:

[1122] Based on water level data obtained from sensors installed in the dam, the system operates hydroelectric power generation equipment and issues instructions to optimize power generation.

[1123] Accumulating data and optimizing for the next season

[1124] The server stores the collected weather data and energy usage data in a database.

[1125] Examples:

[1126] The server analyzes weather and energy usage data stored in a database and runs algorithms to further optimize future energy usage plans, thereby optimizing the energy mix for the next season.

[1127] Emotion engine collaboration

[1128] The device is equipped with an emotion engine that collects user emotion data. The emotion engine analyzes the user's emotions in real time from their facial expressions and voice. This emotion data is sent to a server and reflected in the energy usage plan.

[1129] Examples:

[1130] When a user uses a device equipped with an emotion engine, emotional data is collected in real time and sent to a server, which then adjusts the allocation and usage of energy resources based on the collected emotional data.

[1131] Energy Use Feedback

[1132] The server provides the user with feedback on energy usage based on the data obtained from the emotion engine. This feedback is adjusted according to the user's emotional state, allowing for more appropriate energy usage plans to be proposed.

[1133] Examples:

[1134] The server receives the emotional data and provides feedback on energy usage based on the user's emotional state. For example, a user with a high stress level may receive specific feedback, such as advice on how to reduce energy costs.

[1135] The system aims to utilize data collected at each step to efficiently use natural resources as energy sources. Furthermore, by combining it with an emotion engine, it can adjust energy usage plans in real time according to the user's emotional state, helping to realize a sustainable society.

[1136] The processing flow will be explained below.

[1137] Step 1:

[1138] The server collects weather data. The server periodically sends an HTTP GET request to the weather data provider's API and obtains weather data in JSON format as a response. This data includes weather forecasts, temperature, precipitation, wind speed, etc.

[1139] Step 2:

[1140] The server analyzes the collected weather data and uses machine learning models to predict the amount of snow and ice that will be generated. The server then uses the weather data as input for the ML model and generates a forecast, which is then used in the next step.

[1141] Step 3:

[1142] The device acquires real-time water level data from the IoT sensor for the river or dam. The device communicates with the sensor and reads the latest water level data. This read data is then sent to the server using an HTTP POST request.

[1143] Step 4:

[1144] The server then integrates the received water level data with weather forecasts and uses this data to create an optimal energy resource utilization plan. Specifically, it runs an energy optimization algorithm using predicted snowfall and real-time water level data as inputs.

[1145] Step 5:

[1146] The server collects the temperature data of the hot water generated after cooling the computing resources. The server obtains the temperature data of the hot water from the cooling system and uses the data to calculate the conditions suitable for hot water disinfection.

[1147] Step 6:

[1148] The user executes the hot water disinfection according to the hot water disinfection plan of the server. Based on the instructions of the server, the process of sterilizing the rice seeds with hot water without using pesticides proceeds.

[1149] Step 7:

[1150] The server generates hydroelectric power using the water stored in the Hyper Dam. The server calculates the amount of power to be generated based on the dam's water level data and issues instructions to operate the power generation equipment. This allows for efficient power generation.

[1151] Step 8:

[1152] The server stores the weather and energy usage data collected so far in a database, which is then used to optimize energy usage plans for the next season.

[1153] Step 9:

[1154] The server analyzes the data stored in the database and runs an algorithm that suggests the optimal energy mix for the next season, thereby optimizing future energy usage plans.

[1155] Step 10:

[1156] The device is equipped with an emotion engine for collecting user emotion data, which analyzes emotions from the user's facial expressions and voice in real time and transmits this emotion data to a server.

[1157] Step 11:

[1158] The server reflects the received emotional data in its energy usage plan, adjusting the allocation and usage of energy resources based on the emotional data, and achieving optimal energy usage in line with the user's emotional state.

[1159] Step 12:

[1160] The server uses data from the emotion engine to provide users with specific feedback on energy usage based on their emotional state, such as advice on how to reduce energy costs if their stress level is high.

[1161] Example 2

[1162] 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."

[1163] In conventional energy utilization systems, weather data and water level data are collected and analyzed separately, making it difficult to integrate data in real time and efficiently optimize energy resources. Furthermore, energy utilization plans are formulated without taking into account the user's emotional state, resulting in a lack of user comfort and efficiency. Therefore, the challenge is to provide a system that integrates weather data and water level data and optimizes energy utilization plans in real time, reflecting the user's emotional data.

[1164] 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.

[1165] In this invention, the server includes means for collecting meteorological data and predicting the amount of snow and ice produced based on the meteorological data, means for monitoring water level data of rivers and dams in real time using sensors and transmitting the data to the server, and means for formulating an optimal energy resource utilization plan based on the predicted amount of snow and ice produced and the collected water level data. This makes it possible to integrate the meteorological data and water level data and formulate an optimal energy resource utilization plan in real time.

[1166] "Weather Data" refers to information about climatic conditions such as temperature, precipitation, wind speed, and weather forecasts.

[1167] "Snow and ice generation forecasting" refers to the use of machine learning models and algorithms to estimate future snow and ice volumes.

[1168] A "sensor" refers to a device that detects physical conditions or changes and acquires them as data.

[1169] "Water level data" refers to information showing the water height of rivers and dams.

[1170] "Server" refers to a computer system that collects, analyzes, stores, and distributes data.

[1171] "Optimal energy resource utilization plan" refers to a plan for efficient use of energy resources based on collected data.

[1172] "Warm water generated after cooling computing resources" refers to heated water generated during the cooling process of computers and servers.

[1173] "Hot water disinfection" refers to a method of using heated water to kill microorganisms and pathogens.

[1174] A "reservoir" refers to an artificial storage facility for storing water.

[1175] "Hydroelectric power" refers to a method of generating electricity using the power of flowing water.

[1176] "Database" refers to a system for efficiently managing, storing, and retrieving data.

[1177] "Energy mix" refers to a plan for using an appropriate combination of different types of energy sources.

[1178] "Emotional data" refers to information about the emotional state of a user analyzed from facial expressions and voice.

[1179] "Energy usage feedback" refers to providing information and advice on energy usage based on the user's behavior and situation.

[1180] MODE FOR CARRYING OUT THE INVENTION

[1181] The present invention is a system for efficiently utilizing natural resources as an energy source, and includes the following elements:

[1182] Meteorological data acquisition and analysis

[1183] The server sends an HTTP GET request to the API of an external weather data provider and collects the JSON-formatted weather data received as a response. This weather data includes information such as temperature, precipitation, wind speed, and weather forecast. The server uses machine learning models such as TensorFlow to predict the amount of snow and ice that will be generated from this data. Specifically, the server collects data from "https: / / api.weather.com / data" and inputs it into the machine learning model to predict snowfall.

[1184] Example prompt sentence:

[1185] "How does the server collect weather data and use machine learning models to predict snowfall?"

[1186] Real-time monitoring of water level data

[1187] The device obtains water level data in real time from IoT sensors installed in rivers and dams. The sensor and device communicate, and the device reads the latest water level data. This data is sent to the server via an HTTP POST request using the sensor ID (e.g., "sensor_001"). Specifically, the data is sent to "https: / / server.endpoint / waterlevels."

[1188] Example prompt sentence:

[1189] "Please explain how the device will send the water level data obtained from the IoT sensor to the server in real time."

[1190] Optimizing energy use

[1191] The server integrates the collected weather and water level data and creates an optimal energy resource utilization plan based on this data. The server analyzes the data using Python libraries (e.g., SciPy, Pandas) and executes energy optimization algorithms. The generated energy utilization plan is saved in JSON format.

[1192] Example prompt sentence:

[1193] "Explain how weather and water level data collected by a server can be used to optimize energy usage planning."

[1194] Use of hot water and hot water disinfection

[1195] The server collects temperature data from the hot water generated during the cooling process of computing resources and creates a hot water disinfection plan.The server calculates temperature conditions using Matplotlib based on the data obtained from the cooling system and carries out hot water disinfection based on that plan.Specifically, it controls the process of sterilizing rice seeds without using pesticides.

[1196] Example prompt sentence:

[1197] "Please tell me how to use the hot water generated by the server after cooling the computing resources to perform hot water disinfection."

[1198] Hydroelectric power generation

[1199] The server calculates the amount of power generated based on the water level data stored in the reservoir and issues instructions for operating the hydroelectric power plant.The server then sends operation instructions to the PLC using the MODBUS protocol to optimize the power generation equipment.

[1200] Example prompt sentence:

[1201] "Explain how the server generates hydroelectric power based on reservoir water level data."

[1202] Accumulating data and optimizing for the next season

[1203] The server stores the collected weather and energy usage data in a database (e.g., MongoDB or AWS DynamoDB). This data is analyzed using R or Python to run algorithms that optimize the energy mix for the next season.

[1204] Example prompt sentence:

[1205] "Please tell me how the data collected by the server will be used to optimize energy usage plans for the next season."

[1206] Emotion engine collaboration

[1207] The device uses an emotion engine (e.g., Affectiva SDK) to collect the user's emotion data. The device analyzes the user's facial expressions and voice and transmits the emotion data to the server in real time. The analyzed emotion data is then sent to the server using WebSocket.

[1208] Example prompt sentence:

[1209] "Please tell me how the server uses the emotional data collected by the device's emotion engine to reflect in the energy usage plan."

[1210] Energy Use Feedback

[1211] The server provides users with feedback on energy usage based on the emotional data obtained from the emotion engine. Based on the analysis results, the server suggests optimal energy usage methods according to the user's emotional state. The feedback is provided to users via a mobile app or web portal.

[1212] Example prompt sentence:

[1213] "Explain how the server provides feedback on energy usage based on data obtained from the emotion engine."

[1214] As described above, the present invention provides a system for efficiently utilizing natural resources as energy sources through the collection, analysis, and optimization of various data.

[1215] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1216] Step 1:

[1217] The server collects weather data. Specifically, it periodically sends HTTP GET requests from "https: / / api.weather.com / data" and obtains weather data in JSON format as a response. This data includes temperature, precipitation, wind speed, weather forecast, etc. The HTTP request is required as input, and the obtained weather data in JSON format is obtained as output. The server analyzes the obtained data and stores it in a database.

[1218] Step 2:

[1219] The server uses the weather data acquired to predict the amount of snow and ice produced using a machine learning model. Specifically, preprocessed data is input into the model using TensorFlow to obtain the predicted results. JSON data and a machine learning model are required as input, and the predicted amount of snow and ice produced is obtained as output. Furthermore, the prediction results are stored in a database for use in the next step.

[1220] Step 3:

[1221] The device retrieves water level data from an IoT sensor. Specifically, it uses a sensor ID, such as "sensor_001", to collect water level data in real time. As input, it requires the sensor ID and communication protocol, and as output, it gets the latest water level data. This data is sent to the server via an HTTP POST request.

[1222] Step 4:

[1223] The server receives water level data, stores it in a database, and integrates it with weather data to optimize the energy usage plan. Specifically, it integrates the data using Python's Pandas library and runs an optimization algorithm using SciPy. Weather and water level data are required as input, and the optimized energy usage plan is obtained as output. This plan is saved in JSON format.

[1224] Step 5:

[1225] The server collects the hot water temperature data from the cooling system. Specifically, it obtains the data from the temperature sensor provided by the cooling system and creates a hot water disinfection plan. The input is the temperature data, and the output is the temperature conditions suitable for disinfection and a specific operation plan. Based on this plan, a signal to control the disinfection process is sent.

[1226] Step 6:

[1227] The server calculates the amount of power generation based on the water level data stored in the Hyper Dam and issues instructions for operating the hydroelectric power plant. Specifically, it uses the MODBUS protocol to send operation instructions to the PLC to optimize the power generation equipment. The inputs required are water level data and a power generation plan, and the output is the actual power generation operation instructions.

[1228] Step 7:

[1229] The server stores all collected data and energy usage data in a database. Specifically, it uses MongoDB or AWS DynamoDB to store the data and then proposes the optimal energy mix for the next season. All collected data is required as input, and the optimized energy mix plan is obtained as output.

[1230] Step 8:

[1231] The device collects the user's emotional data and sends it to the server. Specifically, it uses an emotion engine such as the Affectiva SDK to analyze the user's facial expressions and voice. The input is the user's facial and voice data, and the output is the emotion analysis results. This data is sent to the server in real time.

[1232] Step 9:

[1233] The server provides users with feedback on energy usage based on the emotional data. Specifically, it analyzes the collected emotional and energy usage data and proposes optimal energy usage methods. The inputs are the emotional and energy usage data, and the output is feedback information provided to the user. The user receives the feedback through a mobile app or web portal.

[1234] In this manner, the system of the present invention specifically implements process steps for efficiently utilizing natural resources as energy sources.

[1235] (Application example 2)

[1236] 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."

[1237] Conventional energy usage planning systems have limited means for optimizing energy consumption while efficiently utilizing weather and water level data, making energy management particularly difficult in physical stores. Furthermore, they are unable to dynamically adjust energy usage plans based on user sentiment. This has resulted in insufficient optimization of the user experience and efficient energy consumption.

[1238] 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.

[1239] In this invention, the server includes: means for collecting weather data and predicting the amount of snow and ice generated based on the weather data; means for monitoring water level data of rivers and dams in real time using IoT sensors and transmitting the data to the server; means for formulating an optimal energy resource utilization plan based on the predicted generation amount and the collected water level data; means for formulating a hot water disinfection plan using hot water generated after cooling the computing resources; means for performing hot water disinfection; means for generating hydroelectric power using water stored in a hyper dam; means for storing the collected data and energy utilization data in a database and proposing an optimal energy mix for the next season; means for optimizing energy consumption and automatically controlling the utilization plan using smart glasses to operate the store; means for automatically adjusting temperature control and lighting in the store in cooperation with weather data and water level data; and means for collecting customer emotion data using an emotion engine and adjusting the energy utilization plan. This enables an energy management system that can efficiently consume energy in physical stores while providing a comfortable environment for customers.

[1240] "Weather data" refers to data relating to weather conditions such as weather forecasts, temperature, precipitation, and wind speed.

[1241] "Snow and ice production" refers to the amount of snow and ice production predicted based on collected meteorological data.

[1242] An "IoT sensor" is a sensor that connects to the Internet and measures and monitors environmental conditions, machine operation, and other factors in real time.

[1243] "Water level data" refers to data relating to the water level of bodies of water such as rivers and dams.

[1244] "Energy resources" are energy supply sources such as electricity, gas, and oil.

[1245] "Optimal energy resource utilization planning" is a concept that indicates planning the efficient utilization of energy resources based on collected data.

[1246] "Hot water disinfection" is a method of disinfecting microorganisms such as pathogens and viruses using hot water.

[1247] A "hyper dam" is a large dam that stores a large amount of water to generate hydroelectric power.

[1248] "Hydroelectric power generation" is a method of generating electricity by utilizing the potential energy of water flow and elevation.

[1249] "Smart glasses" are glasses-type devices worn by users that can display augmented reality (AR) and other information.

[1250] The "emotion engine" is a system for analyzing the user's emotional state from facial expressions, voice, etc.

[1251] "Energy mix" is a concept that refers to optimizing overall energy supply by combining different energy sources.

[1252] A "database" is a system for efficiently storing, searching, managing, and updating various types of data.

[1253] A "server" is a computer system that stores, processes, and transmits data over a network.

[1254] The system of the present invention collects meteorological data and water level data, and based on this data, formulates an optimal energy resource utilization plan. This system is realized through the following steps.

[1255] First, the server collects weather data by periodically issuing an HTTP GET request to the weather data provider's API and obtaining the JSON data in response. This data includes information such as weather forecast, temperature, precipitation, and wind speed. Next, the server uses this data to apply machine learning models to predict the amount of snow and ice formation.

[1256] Next, the device uses IoT sensors to obtain real-time water level data from rivers and dams. This data is then sent to the server using an HTTP POST request. The server then integrates the obtained weather and water level data and uses them to create an optimal energy resource utilization plan.

[1257] The system also includes a plan for hot water disinfection using the hot water generated after the computing resources are cooled. The hot water disinfection plan is calculated on the server based on the hot water temperature data from the cooling system. Specifically, it controls the process of sterilizing rice seeds without using pesticides.

[1258] Hydroelectric power generation using water stored in a hyper dam is also an important element of this invention. The server calculates the amount of power to be generated based on the dam's water level data and issues instructions for the actual power generation operation.

[1259] The server also stores all collected data and energy usage data in a database and runs algorithms to suggest an optimized energy mix for the next season.

[1260] Furthermore, this system utilizes smart glasses and an emotion engine to automatically optimize energy consumption in physical store operations. The user device, equipped with smart glasses, collects customers' emotional data in real time and sends it to a server. Based on the collected emotional data, the server automatically adjusts the temperature and lighting in the store, providing a comfortable environment for customers and simultaneously optimizing energy management.

[1261] As a concrete example, a physical store can automatically adjust air conditioning and lighting settings based on weather data, according to predicted temperatures and weather changes. Also, an emotion engine analyzes customers' emotional data and sets the environment to make them feel comfortable. This makes it possible to achieve both efficient energy use and customer satisfaction.

[1262] The following example prompts are also set up as specific scenarios:

[1263] "Users visit the store

[1264] The emotion engine analyzes customer sentiment data in real time and uses it as input to optimize energy consumption.

[1265] It runs energy optimization algorithms based on weather and water level data.

[1266] Energy optimization plans are immediately reflected in store temperature settings, lighting, and air conditioning system adjustments.

[1267] The user receives a presentation of their emotional state through smart glasses.

[1268] Depending on your emotional state, lighting and background music settings in the store will be adjusted to support a comfortable stay.

[1269] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1270] Step 1:

[1271] A server collects weather data.

[1272] Input: Weather data provider's API.

[1273] Data processing: Issue an HTTP GET request and analyze the JSON data obtained as a response.

[1274] Output: Obtain weather information such as weather forecast, temperature, precipitation, wind speed, etc.

[1275] Specific operation: The server periodically calls the API and stores the acquired data in an internal database.

[1276] Step 2:

[1277] The server uses machine learning models to predict the amount of snow and ice that will form.

[1278] Input: Weather data obtained in step 1.

[1279] Data calculation: Based on the acquired weather data, a machine learning model is used to predict the amount of snow and ice that will be generated.

[1280] Output: Predicted snow and ice production.

[1281] Specific operation: The server inputs weather data into the model, obtains and stores the forecast results.

[1282] Step 3:

[1283] The device uses IoT sensors to obtain real-time water level data for rivers and dams.

[1284] Input: Water level data from IoT sensors.

[1285] Data processing: Water level data is sent to the server using an HTTP POST request.

[1286] Output: Real-time water level data.

[1287] Specific operation: The terminal communicates with the IoT sensor and sends the water level data it reads to the server.

[1288] Step 4:

[1289] The server integrates the weather and water level data collected to create an optimal plan for using energy resources.

[1290] Input: Weather data for step 2, water level data for step 3.

[1291] Data Computing: Integrates data and runs energy optimization algorithms.

[1292] Output: Energy resource utilization plan.

[1293] How it works: The server inputs weather and water level data into the algorithm, generates and saves an optimized plan.

[1294] Step 5:

[1295] A hot water disinfection plan is created using the hot water generated after the server cools down the computing resources.

[1296] Input: Hot water temperature data from the cooling system.

[1297] Data calculation: Calculates the optimal conditions for hot water disinfection based on hot water temperature data.

[1298] Output: Plan for hot water disinfection.

[1299] Specific operation: The server analyzes the hot water temperature data and sets appropriate disinfection conditions.

[1300] Step 6:

[1301] The device uses smart glasses to collect emotional data from customers in real time and transmits it to a server.

[1302] Input: Smart glasses emotion engine data.

[1303] Data processing: Emotion data is sent to the server using an HTTP POST request.

[1304] Output: Real-time sentiment data.

[1305] Specific operation: The device works in conjunction with smart glasses to read emotion data and send it to the server.

[1306] Step 7:

[1307] The server automatically adjusts temperature control and lighting in the store based on emotional data.

[1308] Input: Emotion data from step 6.

[1309] Data calculation: Analyzes emotional data and calculates optimal environment settings.

[1310] Output: Temperature and lighting settings in the store.

[1311] Specific operation: The server analyzes the emotional data and provides appropriate settings for temperature control and lighting systems.

[1312] Step 8:

[1313] The server stores all collected data and energy usage data in a database and proposes an optimized energy mix for the next season.

[1314] Input: Weather data, water level data, energy usage data, emotion data.

[1315] Data processing: Store data in a database and run analysis algorithms.

[1316] Output: Proposal for optimising the energy mix for the next season.

[1317] How it works: The server stores the data in a database and uses an algorithm to generate a plan for the next season.

[1318] 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.

[1319] 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.

[1320] 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.

[1321] [Fourth embodiment]

[1322] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1323] 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.

[1324] 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).

[1325] 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.

[1326] 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.

[1327] 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).

[1328] 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.

[1329] 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.

[1330] 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.

[1331] 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.

[1332] 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.

[1333] 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.

[1334] 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."

[1335] Meteorological data acquisition and analysis

[1336] The server collects weather data. It periodically sends HTTP GET requests to the weather data provider's API and receives JSON-formatted data in response. This data includes weather forecasts, temperature, precipitation, wind speed, etc. The server then uses this data to apply machine learning models to predict snow and ice formation, which allows it to understand future weather conditions.

[1337] Examples:

[1338] The server collects weather data from a URL such as "https: / / api.weather.com / data" and uses an ML model to predict the dates and amount of snowfall. The predicted data is used in the next step.

[1339] Real-time monitoring of water level data

[1340] The device obtains real-time water level data from the IoT sensors of rivers and dams. The device establishes communication with the sensors and reads the latest water level data. This data is sent to the server using an HTTP POST request.

[1341] Examples:

[1342] The device uses a sensor ID such as "sensor_001" to read water level data and sends it to "https: / / server.endpoint / waterlevels".

[1343] Optimizing energy use

[1344] The server creates an optimal energy resource utilization plan based on the weather and water level data it collects. The server integrates this data, calculates how to allocate and utilize energy resources, and creates a specific utilization plan based on the results.

[1345] Examples:

[1346] The server runs an energy optimization algorithm using predicted snowfall and real-time water level data as inputs to generate an energy usage plan, which determines how resources can be used efficiently.

[1347] Use of hot water and hot water disinfection

[1348] The server uses the hot water generated after cooling the computing resources to create a hot water disinfection plan. The server uses the hot water temperature data from the cooling system to calculate the conditions suitable for hot water disinfection, and then performs hot water disinfection based on that plan.

[1349] Examples:

[1350] The server collects the temperature data of the hot water obtained from the cooling system, and based on the temperature conditions, it plans the hot water disinfection and controls the process of sterilizing the rice seeds without using pesticides.

[1351] Hydroelectric power generation

[1352] The server generates hydroelectric power using the water stored in the Hyper Dam. The server calculates the amount of power to be generated based on the dam's water level data and issues instructions for the actual power generation operation. This allows the dam's water to be used efficiently to generate electricity.

[1353] Examples:

[1354] Based on water level data obtained from sensors installed in the dam, the system operates hydroelectric power generation equipment and issues instructions to optimize power generation.

[1355] Accumulating data and optimizing for the next season

[1356] The server collects weather and energy usage data and stores it in a database. The server analyzes this data and proposes the optimal energy mix for the next season.

[1357] Examples:

[1358] The server analyzes weather and energy usage data stored in a database and runs algorithms to further optimize future energy usage plans, thereby optimizing the energy mix for the next season.

[1359] The system aims to utilize data collected at each step to efficiently use natural resources as an energy source, thereby reducing energy waste and helping to realize a sustainable society.

[1360] The processing flow will be explained below.

[1361] Step 1:

[1362] The server collects weather data by periodically issuing an HTTP GET request to the weather data provider's API and retrieving the JSON data returned as a response. This data includes information such as weather forecast, temperature, precipitation, and wind speed.

[1363] Step 2:

[1364] The server analyzes the collected weather data and uses machine learning models to predict the amount of snow and ice that will be produced, which is then used in the next step.

[1365] Step 3:

[1366] The device acquires real-time water level data from the IoT sensor for the river or dam. The device communicates with the sensor and reads the latest water level data. This read data is sent to the server using an HTTP POST request.

[1367] Step 4:

[1368] The server integrates the received water level data with weather data forecasts and then creates an optimal energy resource utilization plan. The server inputs the predicted snowfall amount and real-time water level data, runs the energy optimization algorithm, and generates a specific utilization plan.

[1369] Step 5:

[1370] The server collects temperature data of the hot water generated after cooling the computing resources. Using the temperature data from the cooling system, the system calculates the conditions suitable for hot water disinfection and formulates a hot water disinfection plan based on the conditions.

[1371] Step 6:

[1372] The user executes hot water disinfection according to the hot water disinfection plan on the server, which controls the process of sterilizing rice seeds with hot water without using pesticides.

[1373] Step 7:

[1374] The server generates hydroelectric power using the water stored in the Hyper Dam. Based on water level data from sensors installed in the dam, it calculates the amount of power to be generated and instructs the operation of the power generation equipment. This operation allows for efficient generation of electricity.

[1375] Step 8:

[1376] The server stores the weather data and energy usage data it has collected so far in a database.

[1377] Step 9:

[1378] The server analyzes the accumulated data and runs an algorithm that suggests the optimal energy mix for the next season, thereby optimizing future energy usage plans.

[1379] Example 1

[1380] 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."

[1381] The purpose of this invention is to effectively collect and analyze weather and water level data to efficiently utilize energy resources. Conventional systems merely collect data individually and use them for their respective purposes, and lack a means to integrate and utilize this data to optimize energy use. Furthermore, no concrete means are available for reducing environmental impacts or running costs. Therefore, the challenge is to achieve more advanced optimization of energy use and sustainable environmental management through comprehensive data analysis.

[1382] 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.

[1383] In this invention, the server includes means for collecting meteorological data and predicting the amount of snow and ice produced based on the meteorological data, means for monitoring water level data of rivers and dams in real time using sensors and transmitting the data to the server, and means for formulating an optimal energy resource utilization plan based on the predicted amount of snow and ice produced and the collected water level data. This makes it possible to comprehensively utilize various data to optimize energy utilization and manage sustainable resources.

[1384] "Weather data" refers to various weather-related information such as weather forecasts, temperature, precipitation, and wind speed.

[1385] "Snow and ice production" refers to the predicted amount of snow and ice that will fall and accumulate.

[1386] A "sensor" is a device for measuring physical environmental information, and in this case refers to a device for monitoring the water level of rivers and dams in real time.

[1387] "Energy resources" refers to resources that are used as energy, such as hydroelectric power generation and hot water disinfection.

[1388] "Computing device" refers to a computing resource for data processing and analysis.

[1389] "Hot water disinfection" refers to a method of sterilization or disinfection using heated water.

[1390] A "dam" refers to an artificial structure for storing water, and in this case it serves as a water source for hydroelectric power generation.

[1391] "Storage" refers to devices and systems for long-term storage of data.

[1392] "Energy mix" refers to the optimal use of different types of energy resources.

[1393] A "generative model" refers to a machine learning model that performs analysis and predictions based on specific input data.

[1394] An "actuator" refers to a mechanical device that performs a physical action based on information obtained from a sensor.

[1395] The system of the present invention integrates the collection and analysis of meteorological data, real-time monitoring of water level data, optimization of energy use, hot water use and hot water disinfection, hydroelectric power generation, and data accumulation and optimization for the next season. Specific embodiments of each processing step are described below.

[1396] Hardware and software used

[1397] Server: Collects and analyzes meteorological data, develops energy usage plans, plans hot water disinfection, operates hydroelectric power generation, and accumulates and analyzes data.

[1398] Terminal: Obtains water level data from rivers and dams from IoT sensors and sends it to a server.

[1399] Sensors: Measure water level data in rivers and dams in real time.

[1400] Machine learning models: Predict snow and ice formation based on weather data.

[1401] Database: A storage system for storing collected data and energy usage data.

[1402] Meteorological data collection and analysis

[1403] To collect weather data, the server periodically sends an HTTP GET request to a weather data provider's API (e.g., https: / / api.weather.com / data). The response contains JSON-formatted data including weather forecasts, temperature, precipitation, wind speed, etc. The server then feeds this data into a machine learning model to predict snow and ice formation.

[1404] Examples:

[1405] The server sends an HTTP GET request to the API endpoint and inputs the resulting JSON data into a Python-based machine learning model to predict future weather conditions.

[1406] Real-time monitoring of water level data

[1407] The device establishes communication with the IoT sensor and acquires real-time water level data of rivers and dams. The acquired data is sent to the server using an HTTP POST request.

[1408] Examples:

[1409] The device reads water level data using a sensor ID such as "Sensor_001" and sends an HTTP POST request to "https: / / server.endpoint / waterlevels".

[1410] Optimizing energy use

[1411] The server integrates the weather and water level data collected and runs an energy optimization algorithm to create an optimal energy usage plan.

[1412] Examples:

[1413] The server integrates weather data and water level data and runs an energy optimization algorithm (e.g., a linear optimization algorithm) to generate an optimal energy usage plan.

[1414] Use of hot water and hot water disinfection

[1415] The server uses the hot water generated after the computer cools down to calculate the conditions for hot water disinfection, and then performs hot water disinfection based on the calculated conditions.

[1416] Examples:

[1417] The server collects the temperature data of the hot water obtained from the cooling system and creates a hot water disinfection plan based on the temperature conditions, for example, to sterilize rice seeds.

[1418] Hydroelectric power generation

[1419] The server operates the hydroelectric power plant based on water level data obtained from the dam, calculates the amount of power generated, and generates electricity using water efficiently.

[1420] Examples:

[1421] The server collects water level data from sensors in the dam facility, calculates the amount of power generated, and sends operating instructions to the power generation facility.

[1422] Accumulating data and optimizing for the next season

[1423] The server collects weather data and energy usage data, stores it in a database, and then analyzes it to propose the optimal energy mix for the next season.

[1424] Examples:

[1425] The server analyzes the data stored in the database and runs an energy mix optimization algorithm to propose the optimal energy mix for the next season.

[1426] Prompt Sentence Examples

[1427] "Run an energy optimization algorithm using weather and water level data as inputs to generate an optimal energy usage plan."

[1428] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1429] Step 1:

[1430] The server collects weather data by periodically sending HTTP GET requests to the weather data provider's API (e.g., https: / / api.weather.com / data). The input required is the API endpoint and authentication information, and the output is JSON-formatted data such as the weather forecast, temperature, precipitation, and wind speed. This data is used in the next processing step.

[1431] Step 2:

[1432] The server analyzes the weather data it collects. Specifically, the server inputs the weather data into a machine learning model using Python to predict the amount of snow and ice that will be generated. The input requires weather data (weather forecast, temperature, precipitation, wind speed, etc.), and the output is the predicted amount of snow and ice that will be generated.

[1433] Step 3:

[1434] The terminal acquires water level data from the IoT sensor. Specifically, the terminal establishes communication with the sensor and reads the water level data in real time. The input requires the sensor ID and communication protocol, and the output is the latest water level data.

[1435] Step 4:

[1436] The water level data acquired by the device is sent to the server. Specifically, the device sends an HTTP POST request to "https: / / server.endpoint / waterlevels". The input requires the water level data and a destination URL, and the output is the water level data stored on the server.

[1437] Step 5:

[1438] The server integrates the collected weather and water level data to create an energy usage plan. Specifically, the server executes an energy optimization algorithm (e.g., a linear optimization algorithm). The input required is the integrated weather and water level data, and the output generated is an optimal energy usage plan.

[1439] Step 6:

[1440] The server creates a plan for hot water use and hot water disinfection. Specifically, the server collects hot water temperature data from the cooling system and calculates the conditions suitable for hot water disinfection. The input required is hot water temperature data from the cooling system, and the output generated is a hot water disinfection plan.

[1441] Step 7:

[1442] The server carries out hydroelectric power generation. Specifically, the server collects water level data from sensors installed in the dam and operates the power generation equipment based on that data. Water level data and power generation equipment control data are required as inputs, and the generated power data is obtained as output.

[1443] Step 8:

[1444] The server stores the collected data in a database and proposes the optimal energy mix for the next season. Specifically, the server analyzes the stored data and runs an energy mix optimization algorithm. All stored data is required as input, and a proposed energy mix for the next season is generated as output.

[1445] (Application example 1)

[1446] 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."

[1447] Conventional energy management systems were unable to effectively utilize weather and water level data, making it difficult to optimize energy use within factories. In particular, hot water disinfection and hydroelectric power generation were planned and executed separately, preventing integrated management, resulting in wasted energy resources. Furthermore, there was no established method for efficiently analyzing data using generative AI models, resulting in low accuracy in energy usage plans.

[1448] 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.

[1449] In this invention, the server includes a means for optimizing energy usage of equipment installed in the factory, a means for executing an optimal energy usage plan and hot water disinfection using a smart device, and a means for managing energy based on the optimized energy usage plan. This allows for the integration of weather data and water level data to optimize energy usage and efficiently manage energy resources within the factory. Furthermore, data analysis using a generative AI model can improve the accuracy of the energy usage plan and reduce wasteful energy consumption.

[1450] "Weather data" is a general term for various weather-related information such as weather forecasts, temperature, precipitation, and wind speed.

[1451] An "IoT sensor" is a sensor connected to the Internet that collects real-time data such as water levels in rivers and dams.

[1452] An "optimal energy resource utilization plan" is a specific plan for efficient energy use that calculates energy allocation and utilization methods based on collected data.

[1453] "Warm water generated after cooling of computing resources" refers to warm water generated by the cooling process of computing equipment, which can be reused.

[1454] "Hot water disinfection" is a method of disinfection using hot water that has reached a certain temperature.

[1455] "Hydroelectric power generation" is a method of generating electricity by using water stored in a dam.

[1456] A "database" is an information system for efficiently managing and storing collected data and analysis results.

[1457] "Optimizing energy use of equipment installed in factories" means optimizing energy consumption based on collected data about equipment within factories.

[1458] "Smart devices" refer to multifunctional devices with internet connectivity, such as smartphones, smart glasses, and head-mounted displays.

[1459] A "generative AI model" is a model that uses artificial intelligence to analyze data and make predictions and suggestions.

[1460] A "prompt" is an instruction or question input to a generative AI model and is used to control the model's output.

[1461] The present invention relates to a system for formulating an optimal energy resource utilization plan and for efficiently carrying out hot water disinfection and hydroelectric power generation.

[1462] Meteorological data acquisition and analysis

[1463] The server periodically sends HTTP GET requests to the weather data provider's API to collect weather forecasts, temperature, precipitation, wind speed, etc. This data is received in JSON format and then fed into a generative AI model, which uses this data to predict snow and ice formation and stores the results.

[1464] Real-time monitoring of water level data

[1465] IoT sensors collect real-time water level data from rivers and dams. The device establishes communication with the sensor and reads the latest water level data. This data is sent to the server using an HTTP POST request. The server stores this data in a database.

[1466] Optimizing energy use

[1467] The server creates an optimal energy resource utilization plan based on the collected weather and water level data. Specifically, it uses a generative AI model to analyze the data and calculate energy allocation and utilization methods. The server then manages energy within the factory based on this plan.

[1468] Use of hot water and hot water disinfection

[1469] The server uses the temperature data of the hot water generated after cooling the computing resources to calculate the optimal conditions for hot water disinfection. The server controls the hot water disinfection process using the hot water obtained from the cooling system. The server then executes the hot water disinfection based on the planned conditions.

[1470] Hydroelectric power

[1471] The server generates hydroelectric power using the water stored in the Hyper Dam. It calculates the amount of power to be generated based on the dam's water level data and instructs the actual power generation operation. This allows the dam's water to be used efficiently to generate electricity.

[1472] Accumulating data and optimizing for the next season

[1473] The server stores the collected weather and energy usage data in a database, analyzes this data, and runs an algorithm that suggests the optimal energy mix for the next season.

[1474] Hardware and software used

[1475] IoT sensor: A device that collects water level data, etc.

[1476] Server: Data analysis, energy planning

[1477] Generative AI models: Analyzing weather and water level data

[1478] Energy Management Software: Implementing Energy Planning

[1479] Cooling system: Generating hot water for hot water disinfection

[1480] Examples of prompt statements

[1481] Here are some example prompts the system might use to analyze data using a generative AI model:

[1482] "Predict the temperature and amount of snowfall for the following dates:

[1483] Date: YYYY-MM-DD

[1484] Temperature: [20, 22, 18, 21, 23]

[1485] Snowfall: [0, 15, 10, 0, 5]”

[1486] "Please suggest an energy usage plan based on:

[1487] Forecast temperature and snowfall

[1488] Real-time water level data: 4.5m

[1489] Based on this specific example, the server optimizes energy usage and determines whether the conditions for hot water disinfection are met.

[1490] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1491] Step 1:

[1492] The server periodically sends HTTP GET requests to the weather data provider's API to obtain weather data such as weather forecast, temperature, precipitation, wind speed, etc. The obtained data is passed to the server in JSON format and stored in a weather database.

[1493] Input: API endpoint information

[1494] Data processing: Extraction of weather forecast, temperature, precipitation, and wind speed data

[1495] Output: Weather data in JSON format

[1496] Step 2:

[1497] Using generative AI models, the server analyzes collected weather data and predicts the amount of snow and ice that will be produced. The predictions are stored in a database and used as the basis for energy planning.

[1498] Input: Weather data

[1499] Data Computing: Predicting with Generative AI Models

[1500] Output: Predicted snow and ice formation

[1501] Step 3:

[1502] IoT sensors collect real-time water level data from rivers and dams and send it to a server via a terminal. The server stores the received water level data in a database.

[1503] Input: Water level data from IoT sensors

[1504] Data processing: Extraction and transmission of water level data

[1505] Output: Updates to the server database

[1506] Step 4:

[1507] The server integrates the collected weather and water level data and uses a generative AI model to create an optimal energy resource utilization plan, providing the optimized energy plan to the energy management software.

[1508] Input: Weather data, water level data

[1509] Data Computing: Energy-Optimizing Computations in Generative AI Models

[1510] Output: Energy usage plan

[1511] Step 5:

[1512] The server acquires the temperature data of the hot water generated after cooling the computing resources, calculates the optimal conditions for hot water disinfection based on that data, and then performs hot water disinfection after confirming whether the optimal conditions are met.

[1513] Input: Hot water temperature data

[1514] Data calculation: Calculation of hot water disinfection conditions

[1515] Output: Instruction to perform hot water disinfection

[1516] Step 6:

[1517] The server calculates the amount of power to be generated based on the dam's water level data and issues instructions to operate the hydroelectric power generation equipment, thereby generating electricity using water efficiently.

[1518] Input: Dam water level data

[1519] Data calculation: Calculation of power generation amount

[1520] Output: Hydroelectric power generation operating instructions

[1521] Step 7:

[1522] The server stores the collected weather and energy usage data in a database and proposes an optimized energy mix for the next season.

[1523] Input: Weather data, energy usage data

[1524] Data calculation: Energy mix optimization calculation

[1525] Output: Energy mix proposal for the next season

[1526] Example prompt:

[1527] "Predict the temperature and amount of snowfall for the following dates:

[1528] Date: YYYY-MM-DD

[1529] Temperature: [20, 22, 18, 21, 23]

[1530] Snowfall: [0, 15, 10, 0, 5]”

[1531] "Please suggest an energy usage plan based on:

[1532] Forecast temperature and snowfall

[1533] Real-time water level data: 4.5m

[1534] 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.

[1535] Meteorological data acquisition and analysis

[1536] The server collects weather data. It periodically issues HTTP GET requests to the weather data provider's API and retrieves the JSON data in response. This data includes information such as weather forecast, temperature, precipitation, and wind speed. The server then uses this data to apply machine learning models to predict snow and ice formation, allowing it to understand future weather conditions.

[1537] Examples:

[1538] The server collects weather data from https: / / api.weather.com / data and uses the ML model to predict the dates and amount of snowfall. The predicted data is used in the next step.

[1539] Real-time monitoring of water level data

[1540] The device receives real-time water level data from the IoT sensors of rivers and dams. The device communicates with the sensors and reads the latest water level data. This data is sent to the server using an HTTP POST request.

[1541] Examples:

[1542] The device uses a sensor ID such as "sensor_001" to read water level data and sends it to "https: / / server.endpoint / waterlevels".

[1543] Optimizing energy use

[1544] The server creates an optimal energy resource utilization plan based on the weather and water level data it collects. The server integrates this data, calculates how to allocate and utilize energy resources, and creates a specific utilization plan based on the results.

[1545] Examples:

[1546] The server runs an energy optimization algorithm using predicted snowfall and real-time water level data as inputs to generate an energy usage plan, which determines how resources can be used efficiently.

[1547] Use of hot water and hot water disinfection

[1548] The server uses the hot water generated after cooling the computing resources to create a hot water disinfection plan. The server uses the hot water temperature data from the cooling system to calculate the conditions suitable for hot water disinfection, and then performs hot water disinfection based on that plan.

[1549] Examples:

[1550] The server collects the temperature data of the hot water obtained from the cooling system, and based on the temperature conditions, it plans the hot water disinfection and controls the process of sterilizing the rice seeds without using pesticides.

[1551] Hydroelectric power generation

[1552] The server generates hydroelectric power using the water stored in the Hyper Dam. The server calculates the amount of power to be generated based on the dam's water level data and issues instructions for the actual power generation operation. This allows the dam's water to be used efficiently to generate electricity.

[1553] Examples:

[1554] Based on water level data obtained from sensors installed in the dam, the system operates hydroelectric power generation equipment and issues instructions to optimize power generation.

[1555] Accumulating data and optimizing for the next season

[1556] The server stores the collected weather data and energy usage data in a database.

[1557] Examples:

[1558] The server analyzes weather and energy usage data stored in a database and runs algorithms to further optimize future energy usage plans, thereby optimizing the energy mix for the next season.

[1559] Emotion engine collaboration

[1560] The device is equipped with an emotion engine that collects user emotion data. The emotion engine analyzes the user's emotions in real time from their facial expressions and voice. This emotion data is sent to a server and reflected in the energy usage plan.

[1561] Examples:

[1562] When a user uses a device equipped with an emotion engine, emotional data is collected in real time and sent to a server, which then adjusts the allocation and usage of energy resources based on the collected emotional data.

[1563] Energy Use Feedback

[1564] The server provides the user with feedback on energy usage based on the data obtained from the emotion engine. This feedback is adjusted according to the user's emotional state, allowing for more appropriate energy usage plans to be proposed.

[1565] Examples:

[1566] The server receives the emotional data and provides feedback on energy usage based on the user's emotional state. For example, a user with a high stress level may receive specific feedback, such as advice on how to reduce energy costs.

[1567] The system aims to utilize data collected at each step to efficiently use natural resources as energy sources. Furthermore, by combining it with an emotion engine, it can adjust energy usage plans in real time according to the user's emotional state, helping to realize a sustainable society.

[1568] The processing flow will be explained below.

[1569] Step 1:

[1570] The server collects weather data. The server periodically sends an HTTP GET request to the weather data provider's API and obtains weather data in JSON format as a response. This data includes weather forecasts, temperature, precipitation, wind speed, etc.

[1571] Step 2:

[1572] The server analyzes the collected weather data and uses machine learning models to predict the amount of snow and ice that will be generated. The server then uses the weather data as input for the ML model and generates a forecast, which is then used in the next step.

[1573] Step 3:

[1574] The device acquires real-time water level data from the IoT sensor for the river or dam. The device communicates with the sensor and reads the latest water level data. This read data is then sent to the server using an HTTP POST request.

[1575] Step 4:

[1576] The server then integrates the received water level data with weather forecasts and uses this data to create an optimal energy resource utilization plan. Specifically, it runs an energy optimization algorithm using predicted snowfall and real-time water level data as inputs.

[1577] Step 5:

[1578] The server collects the temperature data of the hot water generated after cooling the computing resources. The server obtains the temperature data of the hot water from the cooling system and uses the data to calculate the conditions suitable for hot water disinfection.

[1579] Step 6:

[1580] The user executes the hot water disinfection according to the hot water disinfection plan of the server. Based on the instructions of the server, the process of sterilizing the rice seeds with hot water without using pesticides proceeds.

[1581] Step 7:

[1582] The server generates hydroelectric power using the water stored in the Hyper Dam. The server calculates the amount of power to be generated based on the dam's water level data and issues instructions to operate the power generation equipment. This allows for efficient power generation.

[1583] Step 8:

[1584] The server stores the weather and energy usage data collected so far in a database, which is then used to optimize energy usage plans for the next season.

[1585] Step 9:

[1586] The server analyzes the data stored in the database and runs an algorithm that suggests the optimal energy mix for the next season, thereby optimizing future energy usage plans.

[1587] Step 10:

[1588] The device is equipped with an emotion engine for collecting user emotion data, which analyzes emotions from the user's facial expressions and voice in real time and transmits this emotion data to a server.

[1589] Step 11:

[1590] The server reflects the received emotional data in its energy usage plan, adjusting the allocation and usage of energy resources based on the emotional data, and achieving optimal energy usage in line with the user's emotional state.

[1591] Step 12:

[1592] The server uses data from the emotion engine to provide users with specific feedback on energy usage based on their emotional state, such as advice on how to reduce energy costs if their stress level is high.

[1593] Example 2

[1594] 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."

[1595] In conventional energy utilization systems, weather data and water level data are collected and analyzed separately, making it difficult to integrate data in real time and efficiently optimize energy resources. Furthermore, energy utilization plans are formulated without taking into account the user's emotional state, resulting in a lack of user comfort and efficiency. Therefore, the challenge is to provide a system that integrates weather data and water level data and optimizes energy utilization plans in real time, reflecting the user's emotional data.

[1596] 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.

[1597] In this invention, the server includes means for collecting meteorological data and predicting the amount of snow and ice produced based on the meteorological data, means for monitoring water level data of rivers and dams in real time using sensors and transmitting the data to the server, and means for formulating an optimal energy resource utilization plan based on the predicted amount of snow and ice produced and the collected water level data. This makes it possible to integrate the meteorological data and water level data and formulate an optimal energy resource utilization plan in real time.

[1598] "Weather Data" refers to information about climatic conditions such as temperature, precipitation, wind speed, and weather forecasts.

[1599] "Snow and ice generation forecasting" refers to the use of machine learning models and algorithms to estimate future snow and ice volumes.

[1600] A "sensor" refers to a device that detects physical conditions or changes and acquires them as data.

[1601] "Water level data" refers to information showing the water height of rivers and dams.

[1602] "Server" refers to a computer system that collects, analyzes, stores, and distributes data.

[1603] "Optimal energy resource utilization plan" refers to a plan for efficient use of energy resources based on collected data.

[1604] "Warm water generated after cooling computing resources" refers to heated water generated during the cooling process of computers and servers.

[1605] "Hot water disinfection" refers to a method of using heated water to kill microorganisms and pathogens.

[1606] A "reservoir" refers to an artificial storage facility for storing water.

[1607] "Hydroelectric power" refers to a method of generating electricity using the power of flowing water.

[1608] "Database" refers to a system for efficiently managing, storing, and retrieving data.

[1609] "Energy mix" refers to a plan for using an appropriate combination of different types of energy sources.

[1610] "Emotional data" refers to information about the emotional state of a user analyzed from facial expressions and voice.

[1611] "Energy usage feedback" refers to providing information and advice on energy usage based on the user's behavior and situation.

[1612] MODE FOR CARRYING OUT THE INVENTION

[1613] The present invention is a system for efficiently utilizing natural resources as an energy source, and includes the following elements:

[1614] Meteorological data acquisition and analysis

[1615] The server sends an HTTP GET request to the API of an external weather data provider and collects the JSON-formatted weather data received as a response. This weather data includes information such as temperature, precipitation, wind speed, and weather forecast. The server uses machine learning models such as TensorFlow to predict the amount of snow and ice that will be generated from this data. Specifically, the server collects data from "https: / / api.weather.com / data" and inputs it into the machine learning model to predict snowfall.

[1616] Example prompt sentence:

[1617] "How does the server collect weather data and use machine learning models to predict snowfall?"

[1618] Real-time monitoring of water level data

[1619] The device obtains water level data in real time from IoT sensors installed in rivers and dams. The sensor and device communicate, and the device reads the latest water level data. This data is sent to the server via an HTTP POST request using the sensor ID (e.g., "sensor_001"). Specifically, the data is sent to "https: / / server.endpoint / waterlevels."

[1620] Example prompt sentence:

[1621] "Please explain how the device will send the water level data obtained from the IoT sensor to the server in real time."

[1622] Optimizing energy use

[1623] The server integrates the collected weather and water level data and creates an optimal energy resource utilization plan based on this data. The server analyzes the data using Python libraries (e.g., SciPy, Pandas) and executes energy optimization algorithms. The generated energy utilization plan is saved in JSON format.

[1624] Example prompt sentence:

[1625] "Explain how weather and water level data collected by a server can be used to optimize energy usage planning."

[1626] Use of hot water and hot water disinfection

[1627] The server collects temperature data from the hot water generated during the cooling process of computing resources and creates a hot water disinfection plan.The server calculates temperature conditions using Matplotlib based on the data obtained from the cooling system and carries out hot water disinfection based on that plan.Specifically, it controls the process of sterilizing rice seeds without using pesticides.

[1628] Example prompt sentence:

[1629] "Please tell me how to use the hot water generated by the server after cooling the computing resources to perform hot water disinfection."

[1630] Hydroelectric power generation

[1631] The server calculates the amount of power generated based on the water level data stored in the reservoir and issues instructions for operating the hydroelectric power plant.The server then sends operation instructions to the PLC using the MODBUS protocol to optimize the power generation equipment.

[1632] Example prompt sentence:

[1633] "Explain how the server generates hydroelectric power based on reservoir water level data."

[1634] Accumulating data and optimizing for the next season

[1635] The server stores the collected weather and energy usage data in a database (e.g., MongoDB or AWS DynamoDB). This data is analyzed using R or Python to run algorithms that optimize the energy mix for the next season.

[1636] Example prompt sentence:

[1637] "Please tell me how the data collected by the server will be used to optimize energy usage plans for the next season."

[1638] Emotion engine collaboration

[1639] The device uses an emotion engine (e.g., Affectiva SDK) to collect the user's emotion data. The device analyzes the user's facial expressions and voice and transmits the emotion data to the server in real time. The analyzed emotion data is then sent to the server using WebSocket.

[1640] Example prompt sentence:

[1641] "Please tell me how the server uses the emotional data collected by the device's emotion engine to reflect in the energy usage plan."

[1642] Energy Use Feedback

[1643] The server provides users with feedback on energy usage based on the emotional data obtained from the emotion engine. Based on the analysis results, the server suggests optimal energy usage methods according to the user's emotional state. The feedback is provided to users via a mobile app or web portal.

[1644] Example prompt sentence:

[1645] "Explain how the server provides feedback on energy usage based on data obtained from the emotion engine."

[1646] As described above, the present invention provides a system for efficiently utilizing natural resources as energy sources through the collection, analysis, and optimization of various data.

[1647] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1648] Step 1:

[1649] The server collects weather data. Specifically, it periodically sends HTTP GET requests from "https: / / api.weather.com / data" and obtains weather data in JSON format as a response. This data includes temperature, precipitation, wind speed, weather forecast, etc. The HTTP request is required as input, and the obtained weather data in JSON format is obtained as output. The server analyzes the obtained data and stores it in a database.

[1650] Step 2:

[1651] The server uses the weather data acquired to predict the amount of snow and ice produced using a machine learning model. Specifically, preprocessed data is input into the model using TensorFlow to obtain the predicted results. JSON data and a machine learning model are required as input, and the predicted amount of snow and ice produced is obtained as output. Furthermore, the prediction results are stored in a database for use in the next step.

[1652] Step 3:

[1653] The device retrieves water level data from an IoT sensor. Specifically, it uses a sensor ID, such as "sensor_001", to collect water level data in real time. As input, it requires the sensor ID and communication protocol, and as output, it gets the latest water level data. This data is sent to the server via an HTTP POST request.

[1654] Step 4:

[1655] The server receives water level data, stores it in a database, and integrates it with weather data to optimize the energy usage plan. Specifically, it integrates the data using Python's Pandas library and runs an optimization algorithm using SciPy. Weather and water level data are required as input, and the optimized energy usage plan is obtained as output. This plan is saved in JSON format.

[1656] Step 5:

[1657] The server collects the hot water temperature data from the cooling system. Specifically, it obtains the data from the temperature sensor provided by the cooling system and creates a hot water disinfection plan. The input is the temperature data, and the output is the temperature conditions suitable for disinfection and a specific operation plan. Based on this plan, a signal to control the disinfection process is sent.

[1658] Step 6:

[1659] The server calculates the amount of power generation based on the water level data stored in the Hyper Dam and issues instructions for operating the hydroelectric power plant. Specifically, it uses the MODBUS protocol to send operation instructions to the PLC to optimize the power generation equipment. The inputs required are water level data and a power generation plan, and the output is the actual power generation operation instructions.

[1660] Step 7:

[1661] The server stores all collected data and energy usage data in a database. Specifically, it uses MongoDB or AWS DynamoDB to store the data and then proposes the optimal energy mix for the next season. All collected data is required as input, and the optimized energy mix plan is obtained as output.

[1662] Step 8:

[1663] The device collects the user's emotional data and sends it to the server. Specifically, it uses an emotion engine such as the Affectiva SDK to analyze the user's facial expressions and voice. The input is the user's facial and voice data, and the output is the emotion analysis results. This data is sent to the server in real time.

[1664] Step 9:

[1665] The server provides users with feedback on energy usage based on the emotional data. Specifically, it analyzes the collected emotional and energy usage data and proposes optimal energy usage methods. The inputs are the emotional and energy usage data, and the output is feedback information provided to the user. The user receives the feedback through a mobile app or web portal.

[1666] In this manner, the system of the present invention specifically implements process steps for efficiently utilizing natural resources as energy sources.

[1667] (Application example 2)

[1668] 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."

[1669] Conventional energy usage planning systems have limited means for optimizing energy consumption while efficiently utilizing weather and water level data, making energy management particularly difficult in physical stores. Furthermore, they are unable to dynamically adjust energy usage plans based on user sentiment. This has resulted in insufficient optimization of the user experience and efficient energy consumption.

[1670] 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.

[1671] In this invention, the server includes: means for collecting weather data and predicting the amount of snow and ice generated based on the weather data; means for monitoring water level data of rivers and dams in real time using IoT sensors and transmitting the data to the server; means for formulating an optimal energy resource utilization plan based on the predicted generation amount and the collected water level data; means for formulating a hot water disinfection plan using hot water generated after cooling the computing resources; means for performing hot water disinfection; means for generating hydroelectric power using water stored in a hyper dam; means for storing the collected data and energy utilization data in a database and proposing an optimal energy mix for the next season; means for optimizing energy consumption and automatically controlling the utilization plan using smart glasses to operate the store; means for automatically adjusting temperature control and lighting in the store in cooperation with weather data and water level data; and means for collecting customer emotion data using an emotion engine and adjusting the energy utilization plan. This enables an energy management system that can efficiently consume energy in physical stores while providing a comfortable environment for customers.

[1672] "Weather data" refers to data relating to weather conditions such as weather forecasts, temperature, precipitation, and wind speed.

[1673] "Snow and ice production" refers to the amount of snow and ice production predicted based on collected meteorological data.

[1674] An "IoT sensor" is a sensor that connects to the Internet and measures and monitors environmental conditions, machine operation, and other factors in real time.

[1675] "Water level data" refers to data relating to the water level of bodies of water such as rivers and dams.

[1676] "Energy resources" are energy supply sources such as electricity, gas, and oil.

[1677] "Optimal energy resource utilization planning" is a concept that indicates planning the efficient utilization of energy resources based on collected data.

[1678] "Hot water disinfection" is a method of disinfecting microorganisms such as pathogens and viruses using hot water.

[1679] A "hyper dam" is a large dam that stores a large amount of water to generate hydroelectric power.

[1680] "Hydroelectric power generation" is a method of generating electricity by utilizing the potential energy of water flow and elevation.

[1681] "Smart glasses" are glasses-type devices worn by users that can display augmented reality (AR) and other information.

[1682] The "emotion engine" is a system for analyzing the user's emotional state from facial expressions, voice, etc.

[1683] "Energy mix" is a concept that refers to optimizing overall energy supply by combining different energy sources.

[1684] A "database" is a system for efficiently storing, searching, managing, and updating various types of data.

[1685] A "server" is a computer system that stores, processes, and transmits data over a network.

[1686] The system of the present invention collects meteorological data and water level data, and based on this data, formulates an optimal energy resource utilization plan. This system is realized through the following steps.

[1687] First, the server collects weather data by periodically issuing an HTTP GET request to the weather data provider's API and obtaining the JSON data in response. This data includes information such as weather forecast, temperature, precipitation, and wind speed. Next, the server uses this data to apply machine learning models to predict the amount of snow and ice formation.

[1688] Next, the device uses IoT sensors to obtain real-time water level data from rivers and dams. This data is then sent to the server using an HTTP POST request. The server then integrates the obtained weather and water level data and uses them to create an optimal energy resource utilization plan.

[1689] The system also includes a plan for hot water disinfection using the hot water generated after the computing resources are cooled. The hot water disinfection plan is calculated on the server based on the hot water temperature data from the cooling system. Specifically, it controls the process of sterilizing rice seeds without using pesticides.

[1690] Hydroelectric power generation using water stored in a hyper dam is also an important element of this invention. The server calculates the amount of power to be generated based on the dam's water level data and issues instructions for the actual power generation operation.

[1691] The server also stores all collected data and energy usage data in a database and runs algorithms to suggest an optimized energy mix for the next season.

[1692] Furthermore, this system utilizes smart glasses and an emotion engine to automatically optimize energy consumption in physical store operations. The user device, equipped with smart glasses, collects customers' emotional data in real time and sends it to a server. Based on the collected emotional data, the server automatically adjusts the temperature and lighting in the store, providing a comfortable environment for customers and simultaneously optimizing energy management.

[1693] As a concrete example, a physical store can automatically adjust air conditioning and lighting settings based on weather data, according to predicted temperatures and weather changes. Also, an emotion engine analyzes customers' emotional data and sets the environment to make them feel comfortable. This makes it possible to achieve both efficient energy use and customer satisfaction.

[1694] The following example prompts are also set up as specific scenarios:

[1695] "Users visit the store

[1696] The emotion engine analyzes customer sentiment data in real time and uses it as input to optimize energy consumption.

[1697] It runs energy optimization algorithms based on weather and water level data.

[1698] Energy optimization plans are immediately reflected in store temperature settings, lighting, and air conditioning system adjustments.

[1699] The user receives a presentation of their emotional state through smart glasses.

[1700] Depending on your emotional state, lighting and background music settings in the store will be adjusted to support a comfortable stay.

[1701] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1702] Step 1:

[1703] A server collects weather data.

[1704] Input: Weather data provider's API.

[1705] Data processing: Issue an HTTP GET request and analyze the JSON data obtained as a response.

[1706] Output: Obtain weather information such as weather forecast, temperature, precipitation, wind speed, etc.

[1707] Specific operation: The server periodically calls the API and stores the acquired data in an internal database.

[1708] Step 2:

[1709] The server uses machine learning models to predict the amount of snow and ice that will form.

[1710] Input: Weather data obtained in step 1.

[1711] Data calculation: Based on the acquired weather data, a machine learning model is used to predict the amount of snow and ice that will be generated.

[1712] Output: Predicted snow and ice production.

[1713] Specific operation: The server inputs weather data into the model, obtains and stores the forecast results.

[1714] Step 3:

[1715] The device uses IoT sensors to obtain real-time water level data for rivers and dams.

[1716] Input: Water level data from IoT sensors.

[1717] Data processing: Water level data is sent to the server using an HTTP POST request.

[1718] Output: Real-time water level data.

[1719] Specific operation: The terminal communicates with the IoT sensor and sends the water level data it reads to the server.

[1720] Step 4:

[1721] The server integrates the weather and water level data collected to create an optimal plan for using energy resources.

[1722] Input: Weather data for step 2, water level data for step 3.

[1723] Data Computing: Integrates data and runs energy optimization algorithms.

[1724] Output: Energy resource utilization plan.

[1725] How it works: The server inputs weather and water level data into the algorithm, generates and saves an optimized plan.

[1726] Step 5:

[1727] A hot water disinfection plan is created using the hot water generated after the server cools down the computing resources.

[1728] Input: Hot water temperature data from the cooling system.

[1729] Data calculation: Calculates the optimal conditions for hot water disinfection based on hot water temperature data.

[1730] Output: Plan for hot water disinfection.

[1731] Specific operation: The server analyzes the hot water temperature data and sets appropriate disinfection conditions.

[1732] Step 6:

[1733] The device uses smart glasses to collect emotional data from customers in real time and transmits it to a server.

[1734] Input: Smart glasses emotion engine data.

[1735] Data processing: Emotion data is sent to the server using an HTTP POST request.

[1736] Output: Real-time sentiment data.

[1737] Specific operation: The device works in conjunction with smart glasses to read emotion data and send it to the server.

[1738] Step 7:

[1739] The server automatically adjusts temperature control and lighting in the store based on emotional data.

[1740] Input: Emotion data from step 6.

[1741] Data calculation: Analyzes emotional data and calculates optimal environment settings.

[1742] Output: Temperature and lighting settings in the store.

[1743] Specific operation: The server analyzes the emotional data and provides appropriate settings for temperature control and lighting systems.

[1744] Step 8:

[1745] The server stores all collected data and energy usage data in a database and proposes an optimized energy mix for the next season.

[1746] Input: Weather data, water level data, energy usage data, emotion data.

[1747] Data processing: Store data in a database and run analysis algorithms.

[1748] Output: Proposal for optimising the energy mix for the next season.

[1749] How it works: The server stores the data in a database and uses an algorithm to generate a plan for the next season.

[1750] 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.

[1751] 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.

[1752] 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 robot 414.

[1753] 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.

[1754] 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.

[1755] 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.

[1756] 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).

[1757] 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.

[1758] 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."

[1759] 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.

[1760] 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).

[1761] 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.

[1762] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1763] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1764] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1765] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1766] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1767] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1768] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1769] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1770] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1771] The following is further disclosed regarding the above embodiment.

[1772] (Claim 1)

[1773] a means for collecting meteorological data and forecasting snow and ice formation based on the meteorological data;

[1774] A method for monitoring water level data of rivers and dams in real time using IoT sensors and sending that data to a server.

[1775] a means for formulating an optimal energy resource utilization plan based on the predicted production amount and the collected water level data;

[1776] A means for planning hot water disinfection using hot water generated after cooling down the computing resources;

[1777] A means for carrying out hot water disinfection according to the plan;

[1778] A means of generating hydroelectric power using water stored in a hyper dam,

[1779] a means for storing the collected data and energy usage data in a database and proposing an optimal energy mix for the next season;

[1780] A system including:

[1781] (Claim 2)

[1782] In the system of claim 1,

[1783] means for integrating the weather data and water level data to optimize energy resource utilization planning;

[1784] A system including a means for optimizing energy usage plans that is performed on the applicant's server.

[1785] (Claim 3)

[1786] In the system of claim 1,

[1787] and an operating means for executing the hot water utilization plan and the hydroelectric power generation plan.

[1788] A system having means for communicating with sensors and actuators.

[1789] "Example 1"

[1790] (Claim 1)

[1791] a means for collecting meteorological data and forecasting snow and ice formation based on the meteorological data;

[1792] A means to monitor water level data of rivers and dams in real time using sensors and send that data to a server.

[1793] a means for formulating an optimal energy resource utilization plan based on the predicted production amount and the collected water level data;

[1794] a means for scheduling hot water disinfection using hot water generated after cooling of the computing device;

[1795] A means for carrying out hot water disinfection according to the plan;

[1796] A means of generating hydroelectric power using water stored in dams,

[1797] a means for storing the collected data and energy usage data in a storage device and proposing an optimal energy mix for the next season;

[1798] A system including:

[1799] (Claim 2)

[1800] means for integrating said data and water level data to optimize energy resource utilization planning;

[1801] The system according to claim 1, further comprising an optimization means for energy usage plans executed on a server of the generative model.

[1802] (Claim 3)

[1803] and an operating means for executing the hot water utilization plan and the hydroelectric power generation plan.

[1804] 10. The system of claim 1, further comprising means for communicating with sensors and actuators.

[1805] "Application Example 1"

[1806] (Claim 1)

[1807] a means for collecting meteorological data and forecasting snow and ice formation based on the meteorological data;

[1808] A method for monitoring water level data of rivers and dams in real time using IoT sensors and sending that data to a server.

[1809] a means for formulating an optimal energy resource utilization plan based on the predicted production amount and the collected water level data;

[1810] A means for planning hot water disinfection using hot water generated after cooling down the computing resources;

[1811] A means for carrying out hot water disinfection according to the plan;

[1812] A means of generating hydroelectric power using water stored in dams,

[1813] a means for storing the collected data and energy usage data in a database and proposing an optimal energy mix for the next season;

[1814] Measures to optimize the energy use of equipment installed in the factory;

[1815] A means for executing an optimal energy utilization plan and hot water disinfection using a smart terminal;

[1816] a means for performing energy management based on the optimized energy usage plan;

[1817] A system including:

[1818] (Claim 2)

[1819] means for integrating the weather data and water level data to optimize energy resource utilization planning;

[1820] Includes means for analyzing and predicting weather and water level data using generative AI models

[1821] 10. The system of claim 1.

[1822] (Claim 3)

[1823] and an operating means for executing the hot water utilization plan and the hydroelectric power generation plan.

[1824] a system having means for communicating with sensors and actuators;

[1825] means for generating prompt sentences and inputting them into the generative AI model;

[1826] 10. The system of claim 1.

[1827] "Example 2: Combining Emotion Engines"

[1828] (Claim 1)

[1829] a means for collecting meteorological data and forecasting snow and ice formation based on the meteorological data;

[1830] A means to monitor water level data of rivers and dams in real time using sensors and send that data to a server.

[1831] a means for formulating an optimal energy resource utilization plan based on the predicted production amount and the collected water level data;

[1832] A means for planning hot water disinfection using hot water generated after cooling down the computing resources;

[1833] A means for carrying out hot water disinfection according to the plan;

[1834] A means for generating hydroelectric power using water stored in a reservoir;

[1835] a means for storing the collected data and energy usage data in a database and proposing an optimal energy mix for the next season;

[1836] a means for collecting user emotion data and reflecting the data in an energy usage plan;

[1837] means for providing energy usage feedback based on said emotion data;

[1838] A system including:

[1839] (Claim 2)

[1840] 10. The system of claim 1, further comprising means for integrating the weather data and water level data to optimize energy resource utilization planning.

[1841] (Claim 3)

[1842] 2. The system according to claim 1, further comprising an operating means for executing the hot water utilization plan and the hydroelectric power generation plan, and means for communicating with sensors and actuators.

[1843] "Application example 2 when combining emotion engines"

[1844] (Claim 1)

[1845] a means for collecting meteorological data and forecasting snow and ice formation based on the meteorological data;

[1846] A method for monitoring water level data of rivers and dams in real time using IoT sensors and sending that data to a server.

[1847] a means for formulating an optimal energy resource utilization plan based on the predicted production amount and the collected water level data;

[1848] A means for planning hot water disinfection using hot water generated after cooling down the computing resources;

[1849] A means for carrying out hot water disinfection according to the plan;

[1850] A means of generating hydroelectric power using water stored in a hyper dam,

[1851] a means for storing the collected data and energy usage data in a database and proposing an optimal energy mix for the next season;

[1852] A means for automatically controlling store operations to optimize energy consumption and plan its use using smart glasses;

[1853] It will link with weather and water level data to automatically control temperature and lighting in stores,

[1854] A means for collecting emotional data of customers using an emotion engine and adjusting energy usage plans;

[1855] A system including:

[1856] (Claim 2)

[1857] a means for integrating weather and water level data to optimize energy resource utilization planning;

[1858] 10. The system of claim 1, further comprising means for optimizing energy consumption and providing feedback based on emotion data using the smart glasses.

[1859] (Claim 3)

[1860] and an operating means for executing the hot water utilization plan and the hydroelectric power generation plan.

[1861] means for communicating with sensors and actuators;

[1862] The system according to claim 1 further comprises an emotion engine that analyzes the emotions of customers and optimizes the in-store environment. [Explanation of symbols]

[1863] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for collecting meteorological data and forecasting snow and ice formation based on the meteorological data; A method for monitoring water level data of rivers and dams in real time using IoT sensors and sending that data to a server. a means for formulating an optimal energy resource utilization plan based on the predicted production amount and the collected water level data; A means for planning hot water disinfection using hot water generated after cooling down the computing resources; A means for carrying out hot water disinfection according to the plan; A means of generating hydroelectric power using water stored in a hyper dam, a means for storing the collected data and energy usage data in a database and proposing an optimal energy mix for the next season; A system including:

2. 10. The system of claim 1, means for integrating the weather data and water level data to optimize energy resource utilization planning; A system including a means for optimizing energy usage plans that is performed on the applicant's server.

3. 10. The system of claim 1, and an operating means for executing the hot water utilization plan and the hydroelectric power generation plan. A system having means for communicating with sensors and actuators.

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