System

Abstract

An object of a system according to an embodiment is to optimize a supply-demand balance of energy in real time.SOLUTION: A system includes an energy consumption data collection unit, an analysis unit, a supply route proposal unit, and a supply execution unit. The energy consumption data collector collects energy consumption data. The analyzer analyzes the energy consumption data collected by the energy consumption data collector. The supply route proposal unit proposes an optimum supply route based on the data analyzed by the analysis unit. The supply execution unit supplies energy based on the route proposed by the supply route proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technologies, it is difficult to optimize the energy supply and demand balance in real time, which can result in energy waste.

[0005] The system according to the embodiment aims to optimize the balance between energy supply and demand in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes an energy consumption data collection unit, an analysis unit, a supply route proposal unit, and a supply execution unit. The energy consumption data collection unit collects energy consumption data. The analysis unit analyzes the energy consumption data collected by the energy consumption data collection unit. The supply route proposal unit proposes an optimal supply route based on the data analyzed by the analysis unit. The supply execution unit supplies energy based on the route proposed by the supply route proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can optimize the balance between energy supply and demand in real time. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The energy supply system according to the embodiment of the present invention utilizes generation AI to match energy supply and demand in real time and supply surplus energy to areas and buildings where there is a shortage. This enables the energy supply system to use energy efficiently and provide a supply service that does not require fixed liabilities such as those of power plants.

[0029] An energy supply system according to an embodiment includes an energy consumption data collection unit, an analysis unit, a supply route proposal unit, and a supply execution unit. The energy consumption data collection unit collects energy consumption data. For example, the energy consumption data collection unit collects electricity consumption data for each region or building using sensors. The energy consumption data collection unit can also collect gas consumption data using smart meters. The energy consumption data collection unit can also collect data in real time via the Internet. The analysis unit analyzes the collected energy consumption data. For example, the analysis unit can analyze energy consumption patterns using statistical analysis. The analysis unit can also predict energy consumption using a machine learning algorithm. The analysis unit can also detect abnormalities in energy consumption using data mining technology. The supply route proposal unit proposes an optimal supply route based on the analyzed data. For example, the supply route proposal unit proposes an optimal supply route taking cost efficiency into consideration. The supply route proposal unit can also propose a route that optimizes supply time. The supply route proposal unit can also propose a route that minimizes energy loss. The supply execution unit supplies energy based on the proposed route. For example, the supply execution unit controls infrastructure for supplying electricity. The supply execution unit can also control infrastructure for supplying gas. The supply execution unit can also perform control to optimize supply timing. This enables the energy supply system according to the embodiment to efficiently supply energy. For example, the energy supply system can reduce energy waste. The energy supply system can also reduce energy costs. The energy supply system can also improve the reliability of energy supply.

[0030] The analysis unit can analyze the energy consumption patterns of each region and building in detail and construct a prediction model that takes into account seasonal and weather fluctuations. The analysis unit, for example, collects energy consumption data of each region and building and constructs a prediction model that takes into account seasonal and weather fluctuations. For example, since heating demand increases in winter, heating demand is predicted based on past data. The analysis unit can also collect weather data such as temperature, precipitation, and sunshine hours and reflect this in energy consumption predictions. The analysis unit can also predict energy consumption using regression models and time series analysis models. This improves the accuracy of energy consumption predictions. For example, the energy supply system can optimize the energy supply and demand balance. The energy supply system can also reduce energy waste. The energy supply system can also reduce energy costs.

[0031] The analysis unit can analyze the behavioral patterns and lifestyles of energy consumers and perform individual demand predictions. The analysis unit, for example, analyzes the behavioral patterns of energy consumers and performs individual demand predictions. For example, an energy supply plan tailored to the lifestyle rhythms of residents is created based on household energy consumption data. The analysis unit can also analyze the lifestyles of energy consumers and perform individual demand predictions. For example, energy consumption is predicted taking into account the configuration and lifestyle habits of the household. The analysis unit can also analyze daily activities and consumption behaviors and perform individual demand predictions. This makes it possible to perform individual demand predictions. For example, the energy supply system can optimize the energy supply and demand balance. The energy supply system can also reduce energy waste. The energy supply system can also reduce energy costs.

[0032] The analysis unit can integrate not only energy consumption data but also traffic data and industrial activity data to perform more accurate supply and demand predictions. The analysis unit, for example, integrates energy consumption data and traffic data to perform supply and demand predictions. For example, since energy demand increases during times of heavy traffic, a supply plan tailored to those times is created. The analysis unit can also integrate energy consumption data and industrial activity data to perform supply and demand predictions. For example, since energy demand increases during times of heavy production, a supply plan tailored to those times is created. The analysis unit can also integrate energy consumption data, traffic data, and industrial activity data to perform supply and demand predictions. For example, the energy supply plan is adjusted according to fluctuations in traffic volume and production volume. This improves the accuracy of supply and demand predictions. For example, the energy supply system can optimize the energy supply and demand balance. The energy supply system can also reduce energy waste. The energy supply system can also reduce energy costs.

[0033] The analysis unit can consider the characteristics of different energy sources and propose an optimal energy mix. The analysis unit considers the characteristics of different energy sources, such as solar, wind, and hydroelectric power, and proposes an optimal energy mix. For example, in an area with a lot of solar power generation, solar power can be used preferentially. Furthermore, the analysis unit can preferentially use wind power in an area with a lot of wind power generation. Furthermore, the analysis unit can preferentially use hydroelectric power in an area with a lot of hydroelectric power generation. This makes it possible to propose an optimal energy mix. For example, the energy supply system can make efficient use of energy. Furthermore, the energy supply system can reduce energy costs. Furthermore, the energy supply system can improve the reliability of energy supply.

[0034] The supply execution unit can introduce a new transmission technology to maximize energy transmission efficiency in the energy supply route. The supply execution unit, for example, introduces a new transmission technology to maximize energy transmission efficiency in the energy supply route. For example, the supply execution unit transmits energy using highly efficient power line communication technology. The supply execution unit can also transmit energy using highly efficient cables. The supply execution unit can also transmit energy using wireless transmission technology. This improves energy transmission efficiency. For example, the energy supply system can minimize energy loss. The energy supply system can also reduce energy supply costs. The energy supply system can also improve the reliability of energy supply.

[0035] In the energy supply method, the supply execution unit can establish a real-time feedback system for optimizing the timing of energy supply. For example, in the energy supply method, the supply execution unit establishes a real-time feedback system for optimizing the timing of energy supply. For example, the supply execution unit adjusts the supply timing according to fluctuations in energy demand. The supply execution unit can also collect data in real time using a sensor network and optimize the supply timing. The supply execution unit can also analyze data in real time using a data analysis system and optimize the supply timing. This optimizes the timing of energy supply. For example, the energy supply system can reduce energy waste. The energy supply system can reduce energy costs. The energy supply system can also improve the reliability of energy supply.

[0036] The supply execution unit can automatically generate a backup route in an energy supply route to improve the stability of energy supply. The supply execution unit automatically generates a backup route in an energy supply route to improve the stability of energy supply. For example, the supply execution unit automatically selects an alternative route when a failure occurs in a main route. The supply execution unit can also set a redundant route to improve the stability of energy supply. The supply execution unit can also set an alternative route to improve the reliability of energy supply. This improves the stability of energy supply. For example, the energy supply system can prevent interruptions in energy supply. The energy supply system can also improve the reliability of energy supply. The energy supply system can also improve the efficiency of energy supply.

[0037] The supply execution unit can introduce a dynamic pricing model to minimize the cost of energy supply in the energy supply method. The supply execution unit, for example, introduces a dynamic pricing model to minimize the cost of energy supply in the energy supply method. For example, the supply execution unit can lower energy prices during times of low demand. The supply execution unit can also raise energy prices during times of high demand. The supply execution unit can also adjust prices in real time to minimize the cost of energy supply. This minimizes the cost of energy supply. For example, the energy supply system can reduce energy costs. The energy supply system can also improve the efficiency of energy supply. The energy supply system can also improve the reliability of energy supply.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The energy supply system may further include an energy storage unit. The energy storage unit may store surplus energy and supply it when demand increases. For example, the energy storage unit may store power using a battery system. Alternatively, the energy storage unit may store energy using a compressed air energy storage system. Alternatively, the energy storage unit may store energy using a flywheel energy storage system. This allows the energy supply system to more flexibly adjust the balance between energy supply and demand. For example, the energy supply system may meet peak energy demand. Alternatively, the energy supply system may improve the stability of the energy supply. Alternatively, the energy supply system may reduce energy costs.

[0040] The energy supply system may further include an energy generation unit. The energy generation unit may generate energy by utilizing renewable energy. For example, the energy generation unit may generate power by using a solar power generation system. The energy generation unit may also generate power by using a wind power generation system. The energy generation unit may also generate power by using a hydroelectric power generation system. This allows the energy supply system to efficiently utilize renewable energy. For example, the energy supply system may reduce environmental load. The energy supply system may also reduce energy costs. The energy supply system may also improve the reliability of energy supply.

[0041] The energy supply system may further include an energy efficiency improvement unit. The energy efficiency improvement unit may make suggestions for improving energy efficiency based on the energy consumption data. For example, the energy efficiency improvement unit may make suggestions for improving the insulation performance of a building. The energy efficiency improvement unit may also suggest the introduction of energy-efficient home appliances. The energy efficiency improvement unit may also suggest peak shifts in energy consumption. This allows the energy supply system to promote efficient energy use. For example, the energy supply system may reduce energy costs. The energy supply system may also reduce energy waste. The energy supply system may also improve the reliability of energy supply.

[0042] The energy supply system may further include a behavioral data collection unit that collects behavioral data of energy consumers. The behavioral data collection unit can collect the behavioral data of energy consumers and analyze energy consumption patterns. For example, the behavioral data collection unit can collect movement data of energy consumers and identify peak energy consumption times. The behavioral data collection unit can also collect purchasing data of energy consumers and analyze energy consumption trends. The behavioral data collection unit can also collect internet usage data of energy consumers and analyze energy consumption patterns. This allows the energy supply system to understand energy consumption patterns in more detail. For example, the energy supply system can optimize the energy supply and demand balance. The energy supply system can also reduce energy waste. The energy supply system can also reduce energy costs.

[0043] The energy supply system can further include a lifestyle proposal unit that proposes an energy supply method that matches the lifestyle of the energy consumer. The lifestyle proposal unit collects lifestyle data of the energy consumer and proposes an optimal energy supply method. For example, the lifestyle proposal unit creates an energy supply plan that matches the energy consumer's lifestyle rhythm. The lifestyle proposal unit can also propose an energy supply method that matches the hobbies and preferences of the energy consumer. The lifestyle proposal unit can also propose an energy supply method that matches the family structure and lifestyle of the energy consumer. This enables the energy supply system to supply energy that matches the lifestyle of the energy consumer. For example, the energy supply system can improve the satisfaction of the energy consumer. The energy supply system can also reduce energy waste. The energy supply system can also reduce energy costs.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The energy consumption data collection unit collects energy consumption data. For example, electricity consumption data for each region or building can be collected using sensors, and gas consumption data can be collected using smart meters. Data can also be collected in real time via the Internet. Step 2: The analysis unit analyzes the collected energy consumption data. For example, statistical analysis can be used to analyze energy consumption patterns, and machine learning algorithms can be used to predict energy consumption. Data mining techniques can also be used to detect abnormalities in energy consumption. Step 3: The supply route suggestion unit proposes the optimal supply route based on the analyzed data. For example, it can propose the optimal supply route taking into account cost efficiency, and it can also propose routes that optimize supply time and minimize energy loss. Step 4: The supply execution unit supplies energy based on the proposed route. For example, it can control the infrastructure for supplying electricity and the infrastructure for supplying gas. It can also control the supply timing to optimize it.

[0046] (Example 2) The energy supply system according to the embodiment of the present invention utilizes generation AI to match energy supply and demand in real time and supply surplus energy to areas and buildings where there is a shortage. This enables the energy supply system to use energy efficiently and provide a supply service that does not require fixed liabilities such as those of power plants.

[0047] An energy supply system according to an embodiment includes an energy consumption data collection unit, an analysis unit, a supply route proposal unit, and a supply execution unit. The energy consumption data collection unit collects energy consumption data. For example, the energy consumption data collection unit collects electricity consumption data for each region or building using sensors. The energy consumption data collection unit can also collect gas consumption data using smart meters. The energy consumption data collection unit can also collect data in real time via the Internet. The analysis unit analyzes the collected energy consumption data. For example, the analysis unit can analyze energy consumption patterns using statistical analysis. The analysis unit can also predict energy consumption using a machine learning algorithm. The analysis unit can also detect abnormalities in energy consumption using data mining technology. The supply route proposal unit proposes an optimal supply route based on the analyzed data. For example, the supply route proposal unit proposes an optimal supply route taking cost efficiency into consideration. The supply route proposal unit can also propose a route that optimizes supply time. The supply route proposal unit can also propose a route that minimizes energy loss. The supply execution unit supplies energy based on the proposed route. For example, the supply execution unit controls infrastructure for supplying electricity. The supply execution unit can also control infrastructure for supplying gas. The supply execution unit can also perform control to optimize supply timing. This enables the energy supply system according to the embodiment to efficiently supply energy. For example, the energy supply system can reduce energy waste. The energy supply system can also reduce energy costs. The energy supply system can also improve the reliability of energy supply.

[0048] The analysis unit can analyze the energy consumption patterns of each region and building in detail and construct a prediction model that takes into account seasonal and weather fluctuations. The analysis unit, for example, collects energy consumption data of each region and building and constructs a prediction model that takes into account seasonal and weather fluctuations. For example, since heating demand increases in winter, heating demand is predicted based on past data. The analysis unit can also collect weather data such as temperature, precipitation, and sunshine hours and reflect this in energy consumption predictions. The analysis unit can also predict energy consumption using regression models and time series analysis models. This improves the accuracy of energy consumption predictions. For example, the energy supply system can optimize the energy supply and demand balance. The energy supply system can also reduce energy waste. The energy supply system can also reduce energy costs.

[0049] The analysis unit can analyze the behavioral patterns and lifestyles of energy consumers and perform individual demand predictions. The analysis unit, for example, analyzes the behavioral patterns of energy consumers and performs individual demand predictions. For example, an energy supply plan tailored to the lifestyle rhythms of residents is created based on household energy consumption data. The analysis unit can also analyze the lifestyles of energy consumers and perform individual demand predictions. For example, energy consumption is predicted taking into account the configuration and lifestyle habits of the household. The analysis unit can also analyze daily activities and consumption behaviors and perform individual demand predictions. This makes it possible to perform individual demand predictions. For example, the energy supply system can optimize the energy supply and demand balance. The energy supply system can also reduce energy waste. The energy supply system can also reduce energy costs.

[0050] The analysis unit can use the emotion estimation function to analyze the emotional state of the energy consumer and propose an energy supply method for reducing stress and anxiety. The analysis unit, for example, can use the emotion estimation function to analyze the emotional state of the energy consumer and propose an energy supply method for reducing stress and anxiety. For example, the analysis unit can adjust the brightness of lighting during times when the energy consumer wants to relax. The analysis unit can also use the emotion estimation function to analyze the emotional state of the energy consumer and adjust the timing of energy supply. For example, stress can be reduced by supplying energy during times when the energy consumer is likely to feel stressed. The analysis unit can also use the emotion estimation function to analyze the emotional state of the energy consumer and adjust the amount of energy supply. For example, anxiety can be reduced by increasing the energy supply during times when the energy consumer is likely to feel anxious. This can reduce the stress and anxiety of the energy consumer. For example, the energy supply system can improve the satisfaction of the energy consumer. The energy supply system can also support the health of the energy consumer. The energy supply system can also improve the quality of life of the energy consumer.

[0051] The analysis unit can integrate not only energy consumption data but also traffic data and industrial activity data to perform more accurate supply and demand predictions. The analysis unit, for example, integrates energy consumption data and traffic data to perform supply and demand predictions. For example, since energy demand increases during times of heavy traffic, a supply plan tailored to those times is created. The analysis unit can also integrate energy consumption data and industrial activity data to perform supply and demand predictions. For example, since energy demand increases during times of heavy production, a supply plan tailored to those times is created. The analysis unit can also integrate energy consumption data, traffic data, and industrial activity data to perform supply and demand predictions. For example, the energy supply plan is adjusted according to fluctuations in traffic volume and production volume. This improves the accuracy of supply and demand predictions. For example, the energy supply system can optimize the energy supply and demand balance. The energy supply system can also reduce energy waste. The energy supply system can also reduce energy costs.

[0052] The analysis unit can consider the characteristics of different energy sources and propose an optimal energy mix. The analysis unit considers the characteristics of different energy sources, such as solar, wind, and hydroelectric power, and proposes an optimal energy mix. For example, in an area with a lot of solar power generation, solar power can be used preferentially. Furthermore, the analysis unit can preferentially use wind power in an area with a lot of wind power generation. Furthermore, the analysis unit can preferentially use hydroelectric power in an area with a lot of hydroelectric power generation. This makes it possible to propose an optimal energy mix. For example, the energy supply system can make efficient use of energy. Furthermore, the energy supply system can reduce energy costs. Furthermore, the energy supply system can improve the reliability of energy supply.

[0053] The analysis unit can use the emotion estimation function to analyze the emotional state of the energy supplier and propose a supply method that will most satisfy the supplier. For example, the analysis unit can use the emotion estimation function to analyze the emotional state of the energy supplier and propose a supply method that will most satisfy the supplier. For example, the analysis unit can adjust the supply plan so that the supplier does not feel stressed. The analysis unit can also use the emotion estimation function to analyze the emotional state of the energy supplier and adjust the supply timing. For example, energy is supplied during a time period when the supplier is able to relax. The analysis unit can also use the emotion estimation function to analyze the emotional state of the energy supplier and adjust the supply amount. For example, a supply amount that satisfies the supplier is provided. This improves the satisfaction of the energy supplier. For example, the energy supply system can reduce the stress of the energy supplier. The energy supply system can also improve the satisfaction of the energy supplier. The energy supply system can also improve the work efficiency of the energy supplier.

[0054] The supply execution unit can introduce a new transmission technology to maximize energy transmission efficiency in the energy supply route. The supply execution unit, for example, introduces a new transmission technology to maximize energy transmission efficiency in the energy supply route. For example, the supply execution unit transmits energy using highly efficient power line communication technology. The supply execution unit can also transmit energy using highly efficient cables. The supply execution unit can also transmit energy using wireless transmission technology. This improves energy transmission efficiency. For example, the energy supply system can minimize energy loss. The energy supply system can also reduce energy supply costs. The energy supply system can also improve the reliability of energy supply.

[0055] In the energy supply method, the supply execution unit can establish a real-time feedback system for optimizing the timing of energy supply. For example, in the energy supply method, the supply execution unit establishes a real-time feedback system for optimizing the timing of energy supply. For example, the supply execution unit adjusts the supply timing according to fluctuations in energy demand. The supply execution unit can also collect data in real time using a sensor network and optimize the supply timing. The supply execution unit can also analyze data in real time using a data analysis system and optimize the supply timing. This optimizes the timing of energy supply. For example, the energy supply system can reduce energy waste. The energy supply system can reduce energy costs. The energy supply system can also improve the reliability of energy supply.

[0056] The supply execution unit can use the emotion estimation function to analyze the emotional state of the residents at the energy supply destination and propose a supply timing at which the residents feel most comfortable. The supply execution unit, for example, can use the emotion estimation function to analyze the emotional state of the residents at the energy supply destination and propose a supply timing at which the residents feel most comfortable. For example, energy is supplied during a time period when the residents want to relax. The supply execution unit can also use the emotion estimation function to analyze the emotional state of the residents and adjust the supply timing. For example, energy is supplied during a time period when the residents do not feel stressed. The supply execution unit can also use the emotion estimation function to analyze the emotional state of the residents and adjust the supply amount. For example, a supply amount that the residents feel comfortable is provided. This makes it possible to propose a supply timing at which the residents feel comfortable. For example, the energy supply system can improve resident satisfaction. The energy supply system can also reduce resident stress. The energy supply system can also improve the quality of life of the residents.

[0057] The supply execution unit can automatically generate a backup route in an energy supply route to improve the stability of energy supply. The supply execution unit automatically generates a backup route in an energy supply route to improve the stability of energy supply. For example, the supply execution unit automatically selects an alternative route when a failure occurs in a main route. The supply execution unit can also set a redundant route to improve the stability of energy supply. The supply execution unit can also set an alternative route to improve the reliability of energy supply. This improves the stability of energy supply. For example, the energy supply system can prevent interruptions in energy supply. The energy supply system can also improve the reliability of energy supply. The energy supply system can also improve the efficiency of energy supply.

[0058] The supply execution unit can introduce a dynamic pricing model to minimize the cost of energy supply in the energy supply method. The supply execution unit, for example, introduces a dynamic pricing model to minimize the cost of energy supply in the energy supply method. For example, the supply execution unit can lower energy prices during times of low demand. The supply execution unit can also raise energy prices during times of high demand. The supply execution unit can also adjust prices in real time to minimize the cost of energy supply. This minimizes the cost of energy supply. For example, the energy supply system can reduce energy costs. The energy supply system can also improve the efficiency of energy supply. The energy supply system can also improve the reliability of energy supply.

[0059] The supply execution unit can use the emotion estimation function to analyze the emotional state of a company to which energy is supplied, and propose a supply method that will most satisfy the company. The supply execution unit, for example, can use the emotion estimation function to analyze the emotional state of a company to which energy is supplied, and propose a supply method that will most satisfy the company. For example, energy is supplied in accordance with the company's business hours. The supply execution unit can also use the emotion estimation function to analyze the company's emotional state and adjust the supply timing. For example, energy is supplied during times when the company is not feeling stressed. The supply execution unit can also use the emotion estimation function to analyze the company's emotional state and adjust the supply amount. For example, a supply amount that will satisfy the company can be provided. This makes it possible to propose a supply method that will most satisfy the company. For example, the energy supply system can improve company satisfaction. The energy supply system can also improve company business efficiency. The energy supply system can also reduce company stress.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The energy supply system may further include an energy storage unit. The energy storage unit may store surplus energy and supply it when demand increases. For example, the energy storage unit may store power using a battery system. Alternatively, the energy storage unit may store energy using a compressed air energy storage system. Alternatively, the energy storage unit may store energy using a flywheel energy storage system. This allows the energy supply system to more flexibly adjust the balance between energy supply and demand. For example, the energy supply system may meet peak energy demand. Alternatively, the energy supply system may improve the stability of the energy supply. Alternatively, the energy supply system may reduce energy costs.

[0062] The energy supply system may further include an energy generation unit. The energy generation unit may generate energy by utilizing renewable energy. For example, the energy generation unit may generate power by using a solar power generation system. The energy generation unit may also generate power by using a wind power generation system. The energy generation unit may also generate power by using a hydroelectric power generation system. This allows the energy supply system to efficiently utilize renewable energy. For example, the energy supply system may reduce environmental load. The energy supply system may also reduce energy costs. The energy supply system may also improve the reliability of energy supply.

[0063] The energy supply system may further include an energy efficiency improvement unit. The energy efficiency improvement unit may make suggestions for improving energy efficiency based on the energy consumption data. For example, the energy efficiency improvement unit may make suggestions for improving the insulation performance of a building. The energy efficiency improvement unit may also suggest the introduction of energy-efficient home appliances. The energy efficiency improvement unit may also suggest peak shifts in energy consumption. This allows the energy supply system to promote efficient energy use. For example, the energy supply system may reduce energy costs. The energy supply system may also reduce energy waste. The energy supply system may also improve the reliability of energy supply.

[0064] The energy supply system may further include a health management unit that monitors the health state of the energy consumer. The health management unit analyzes the health state of the energy consumer using the emotion estimation function and proposes an energy supply method to support the health. For example, the health management unit may adjust lighting and temperature to provide an environment in which the energy consumer can relax. The health management unit may also adjust the timing of energy supply to reduce stress in the energy consumer. The health management unit may also adjust the amount of energy supply to improve the quality of sleep in the energy consumer. This may support the health state of the energy consumer. For example, the energy supply system may improve the quality of life of the energy consumer. The energy supply system may also improve the satisfaction of the energy consumer. The energy supply system may also support the health of the energy consumer.

[0065] The energy supply system may further include a behavioral data collection unit that collects behavioral data of energy consumers. The behavioral data collection unit can collect the behavioral data of energy consumers and analyze energy consumption patterns. For example, the behavioral data collection unit can collect movement data of energy consumers and identify peak energy consumption times. The behavioral data collection unit can also collect purchasing data of energy consumers and analyze energy consumption trends. The behavioral data collection unit can also collect internet usage data of energy consumers and analyze energy consumption patterns. This allows the energy supply system to understand energy consumption patterns in more detail. For example, the energy supply system can optimize the energy supply and demand balance. The energy supply system can also reduce energy waste. The energy supply system can also reduce energy costs.

[0066] The energy supply system may further include an emotion analysis unit that analyzes the emotional state of the energy consumer. The emotion analysis unit analyzes the emotional state of the energy consumer using an emotion estimation function and proposes an energy supply method based on the emotion. For example, the emotion analysis unit may adjust the brightness of lighting during times when the energy consumer wants to relax. The emotion analysis unit may also adjust the timing of energy supply so that the energy consumer does not feel stressed. The emotion analysis unit may also adjust the amount of energy supply so as to provide a temperature that the energy consumer finds comfortable. This makes it possible to supply energy based on the emotional state of the energy consumer. For example, the energy supply system may improve the satisfaction of the energy consumer. The energy supply system may also reduce the stress of the energy consumer. The energy supply system may also improve the quality of life of the energy consumer.

[0067] The energy supply system can further include a lifestyle proposal unit that proposes an energy supply method that matches the lifestyle of the energy consumer. The lifestyle proposal unit collects lifestyle data of the energy consumer and proposes an optimal energy supply method. For example, the lifestyle proposal unit creates an energy supply plan that matches the energy consumer's lifestyle rhythm. The lifestyle proposal unit can also propose an energy supply method that matches the hobbies and preferences of the energy consumer. The lifestyle proposal unit can also propose an energy supply method that matches the family structure and lifestyle of the energy consumer. This enables the energy supply system to supply energy that matches the lifestyle of the energy consumer. For example, the energy supply system can improve the satisfaction of the energy consumer. The energy supply system can also reduce energy waste. The energy supply system can also reduce energy costs.

[0068] The energy supply system may further include an emotional comfort suggestion unit that analyzes the emotional state of the energy consumer and suggests an energy supply method that makes the energy consumer feel most comfortable. The emotional comfort suggestion unit analyzes the emotional state of the energy consumer using an emotion estimation function and suggests an energy supply method that maximizes comfort. For example, the emotional comfort suggestion unit adjusts the brightness of lighting during times when the energy consumer wants to relax. The emotional comfort suggestion unit may also adjust the timing of energy supply so that the energy consumer does not feel stressed. The emotional comfort suggestion unit may also adjust the amount of energy supply so as to provide a temperature that the energy consumer feels comfortable. This enables energy supply that maximizes the comfort of the energy consumer. For example, the energy supply system may improve the satisfaction of the energy consumer. The energy supply system may also reduce the stress of the energy consumer. The energy supply system may also improve the quality of life of the energy consumer.

[0069] The energy supply system may further include a relaxation suggestion unit that analyzes the emotional state of the energy consumer and suggests an energy supply method that allows the energy consumer to be most relaxed. The relaxation suggestion unit analyzes the emotional state of the energy consumer using the emotion estimation function and suggests an energy supply method that maximizes relaxation. For example, the relaxation suggestion unit adjusts the brightness of lighting during times when the energy consumer wants to relax. The relaxation suggestion unit may also adjust the timing of energy supply so that the energy consumer does not feel stressed. The relaxation suggestion unit may also adjust the amount of energy supply so as to provide a temperature that the energy consumer finds comfortable. This enables energy supply that maximizes relaxation for the energy consumer. For example, the energy supply system may improve the satisfaction of the energy consumer. The energy supply system may also reduce the stress of the energy consumer. The energy supply system may also improve the quality of life of the energy consumer.

[0070] The energy supply system may further include a stress reduction suggestion unit that analyzes the emotional state of the energy consumer and suggests an energy supply method that minimizes stress for the energy consumer. The stress reduction suggestion unit analyzes the emotional state of the energy consumer using an emotion estimation function and suggests an energy supply method that minimizes stress. For example, the stress reduction suggestion unit may adjust the brightness of lighting during times when the energy consumer wants to relax. The stress reduction suggestion unit may also adjust the timing of energy supply so that the energy consumer does not feel stressed. The stress reduction suggestion unit may also adjust the amount of energy supply to provide a temperature that the energy consumer finds comfortable. This enables energy supply that minimizes stress for the energy consumer. For example, the energy supply system may improve the satisfaction of the energy consumer. The energy supply system may also reduce stress for the energy consumer. The energy supply system may also improve the quality of life of the energy consumer.

[0071] The energy supply system may further include an emotional comfort suggestion unit that analyzes the emotional state of the energy consumer and suggests an energy supply method that makes the energy consumer feel most comfortable. The emotional comfort suggestion unit analyzes the emotional state of the energy consumer using an emotion estimation function and suggests an energy supply method that maximizes comfort. For example, the emotional comfort suggestion unit adjusts the brightness of lighting during times when the energy consumer wants to relax. The emotional comfort suggestion unit may also adjust the timing of energy supply so that the energy consumer does not feel stressed. The emotional comfort suggestion unit may also adjust the amount of energy supply so as to provide a temperature that the energy consumer feels comfortable. This enables energy supply that maximizes the comfort of the energy consumer. For example, the energy supply system may improve the satisfaction of the energy consumer. The energy supply system may also reduce the stress of the energy consumer. The energy supply system may also improve the quality of life of the energy consumer.

[0072] The energy supply system may further include a relaxation suggestion unit that analyzes the emotional state of the energy consumer and suggests an energy supply method that allows the energy consumer to be most relaxed. The relaxation suggestion unit analyzes the emotional state of the energy consumer using the emotion estimation function and suggests an energy supply method that maximizes relaxation. For example, the relaxation suggestion unit adjusts the brightness of lighting during times when the energy consumer wants to relax. The relaxation suggestion unit may also adjust the timing of energy supply so that the energy consumer does not feel stressed. The relaxation suggestion unit may also adjust the amount of energy supply so as to provide a temperature that the energy consumer finds comfortable. This enables energy supply that maximizes relaxation for the energy consumer. For example, the energy supply system may improve the satisfaction of the energy consumer. The energy supply system may also reduce the stress of the energy consumer. The energy supply system may also improve the quality of life of the energy consumer.

[0073] The energy supply system may further include a stress reduction suggestion unit that analyzes the emotional state of the energy consumer and suggests an energy supply method that minimizes stress for the energy consumer. The stress reduction suggestion unit analyzes the emotional state of the energy consumer using an emotion estimation function and suggests an energy supply method that minimizes stress. For example, the stress reduction suggestion unit may adjust the brightness of lighting during times when the energy consumer wants to relax. The stress reduction suggestion unit may also adjust the timing of energy supply so that the energy consumer does not feel stressed. The stress reduction suggestion unit may also adjust the amount of energy supply to provide a temperature that the energy consumer finds comfortable. This enables energy supply that minimizes stress for the energy consumer. For example, the energy supply system may improve the satisfaction of the energy consumer. The energy supply system may also reduce stress for the energy consumer. The energy supply system may also improve the quality of life of the energy consumer.

[0074] The processing flow of the second embodiment will be briefly explained below.

[0075] Step 1: The energy consumption data collection unit collects energy consumption data. For example, electricity consumption data for each region or building can be collected using sensors, and gas consumption data can be collected using smart meters. Data can also be collected in real time via the Internet. Step 2: The analysis unit analyzes the collected energy consumption data. For example, statistical analysis can be used to analyze energy consumption patterns, and machine learning algorithms can be used to predict energy consumption. Data mining techniques can also be used to detect abnormalities in energy consumption. Step 3: The supply route suggestion unit proposes the optimal supply route based on the analyzed data. For example, it can propose the optimal supply route taking into account cost efficiency, and it can also propose routes that optimize supply time and minimize energy loss. Step 4: The supply execution unit supplies energy based on the proposed route. For example, it can control the infrastructure for supplying electricity and the infrastructure for supplying gas. It can also control the supply timing to optimize it.

[0076] 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.

[0077] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0078] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0079] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0080] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0081] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0082] 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.

[0083] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0084] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0089] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0090] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0091] 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.

[0092] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0094] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0095] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0096] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0097] 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.

[0098] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0099] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0105] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0109] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0110] 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.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0112] 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.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] 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.

[0116] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0117] 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.

[0118] 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.

[0119] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] 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.

[0126] FIG. 9 illustrates 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0127] 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.

[0128] 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).

[0129] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0130] 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."

[0131] 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.

[0132] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, 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. A processor also includes 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.

[0137] The hardware resource that executes the specific process 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 process may be a single processor.

[0138] 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.

[0139] 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.

[0140] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0141] 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.

[0142] 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. [Explanation of symbols]

[0143] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an energy consumption data collection unit that collects energy consumption data; an analysis unit that analyzes the energy consumption data collected by the energy consumption data collection unit; a supply route proposal unit that proposes an optimal supply route based on the data analyzed by the analysis unit; a supply execution unit that supplies energy based on the route proposed by the supply route proposal unit. A system characterized by:

2. The analysis unit Conduct detailed analysis of energy consumption patterns in each region and building, and develop a predictive model that takes into account seasonal and weather fluctuations.

2. The system of claim 1.

3. The analysis unit Integrate not only energy consumption data but also traffic and industrial activity data to make more accurate supply and demand forecasts 2. The system of claim 1.

4. The supply execution unit Introduce new transmission technologies to maximize transmission efficiency along energy supply routes.

2. The system of claim 1.

5. The analysis unit Analyzing the emotional state of energy consumers and proposing energy supply methods to reduce stress and anxiety 2. The system of claim 1.

6. The analysis unit Considering the characteristics of different energy sources, we propose the optimal energy mix 2. The system of claim 1.

7. The supply execution unit Develop a real-time feedback system to optimize the timing of energy supply.

2. The system of claim 1.

8. The supply execution unit Analyze the emotional state of the energy supplier and propose the supply method that will satisfy them most.

2. The system of claim 1.

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