system

Abstract

The system according to this embodiment aims to optimize the energy consumption of a mobile phone base station and achieve energy savings. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, an operation unit, a renewable energy analysis unit, and a renewable energy proposal unit. The data collection unit collects energy consumption data of mobile phone base stations. The analysis unit analyzes the data collected by the data collection unit and proposes an optimal operation method for energy saving. The operation unit executes the operation method proposed by the analysis unit. The renewable energy analysis unit analyzes the utilization status of renewable energy. The renewable energy proposal unit proposes an operation method based on the results analyzed by the renewable energy analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, an operation method for optimizing the energy consumption of a mobile phone base station and achieving energy saving has not been fully established, and there is room for improvement.

[0005] The system according to the embodiment aims to optimize the energy consumption of a mobile phone base station and achieve energy saving.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an operation unit, a renewable energy analysis unit, and a renewable energy proposal unit. The data collection unit collects energy consumption data from mobile phone base stations. The analysis unit analyzes the data collected by the data collection unit and proposes an optimal operation method for energy conservation. The operation unit executes the operation method proposed by the analysis unit. The renewable energy analysis unit analyzes the utilization status of renewable energy. The renewable energy proposal unit proposes an operation method based on the results analyzed by the renewable energy analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can optimize the energy consumption of a mobile phone base station and achieve energy savings. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The system according to an embodiment of the present invention, which entrusts energy conservation and renewable energy utilization of a mobile phone base station to a generating AI, collects energy consumption data from the mobile phone base station, and the generating AI analyzes this data to propose an optimal operation method for energy conservation and an operation method to maximize the use of renewable energy. The system, which entrusts energy conservation and renewable energy utilization of a mobile phone base station to a generating AI, collects energy consumption data from the mobile phone base station, and the generating AI analyzes this data to propose an optimal operation method for energy conservation. Furthermore, the generating AI proposes an operation method to maximize the use of renewable energy. This mechanism improves the energy efficiency of the mobile phone base station and reduces its environmental impact. For example, energy consumption data from the mobile phone base station is collected. At this time, detailed data such as the operating status of the base station, energy consumption, and the status of renewable energy utilization are collected. For example, data such as how much energy the base station consumes at what time of day and what percentage of renewable energy is used is collected. This makes it possible to understand the energy consumption pattern of the base station. Next, the generating AI analyzes the collected data. Based on the collected data, the generating AI proposes an optimal operation method for energy conservation. For example, the system proposes operational methods such as concentrating base station operations during periods of low energy consumption and increasing base station operations during periods of high renewable energy utilization. This improves the energy efficiency of base stations. Furthermore, the generating AI proposes operational methods to maximize the use of renewable energy. For example, it analyzes the usage status of renewable energy sources such as solar and wind power and proposes the optimal operational method. This maximizes the use of renewable energy and reduces the environmental impact. This mechanism improves the energy efficiency of mobile phone base stations and reduces their environmental impact. For example, by concentrating base station operations during periods of low energy consumption, energy consumption can be reduced. Also, by increasing base station operations during periods of high renewable energy utilization, the use of renewable energy can be maximized. This makes the operation of mobile phone base stations more efficient and results in environmentally friendly operations.This means that a system that entrusts energy conservation and the utilization of renewable energy in mobile phone base stations to generating AI can improve the energy efficiency of mobile phone base stations and reduce their environmental impact.

[0029] The system according to this embodiment, which entrusts energy conservation and renewable energy utilization of a mobile phone base station to a generating AI, comprises a data collection unit, an analysis unit, an operation unit, a renewable energy analysis unit, and a renewable energy proposal unit. The data collection unit collects energy consumption data of the mobile phone base station. The data collection unit collects detailed data such as the operating status of the base station, energy consumption, and the utilization status of renewable energy. For example, the data collection unit collects data such as how much energy the base station consumes at what time of day and what percentage of renewable energy is used. For example, the data collection unit can collect detailed data to understand the energy consumption pattern of the base station. The analysis unit analyzes the collected data and proposes the optimal operation method for energy conservation. For example, the analysis unit proposes an operation method that concentrates the operation of the base station during times when energy consumption is low. For example, the analysis unit proposes an operation method that increases the operation of the base station during times when renewable energy is used to a high degree. For example, the analysis unit can propose the optimal operation method considering the energy consumption reduction rate and cost efficiency. The operation unit executes the operation method proposed by the analysis unit. The operations department adjusts the base station's operating schedule based on the proposed operating method. The operations department can, for example, adjust equipment and change schedules. The operations department can, for example, improve the energy efficiency of base stations by implementing the proposed operating method. The renewable energy analysis department analyzes the utilization status of renewable energy. The renewable energy analysis department analyzes the utilization status of renewable energy such as solar power and wind power. The renewable energy analysis department can, for example, analyze the amount of renewable energy generated and its operating time. The renewable energy proposal department proposes an operating method based on the results analyzed by the renewable energy analysis department. The renewable energy proposal department proposes an operating method to maximize the utilization of renewable energy. The renewable energy proposal department can, for example, propose an optimal operating method considering the utilization status of renewable energy.As a result, the system according to this embodiment, which entrusts energy conservation and the utilization of renewable energy in mobile phone base stations to a generating AI, can improve the energy efficiency of mobile phone base stations and reduce the environmental impact.

[0030] The data collection unit collects energy consumption data from mobile phone base stations. For example, it collects detailed data such as the operating status, energy consumption, and renewable energy utilization of the base stations. Specifically, it acquires data in real time from various sensors and meters installed at the base stations and transmits it to a central database. This allows for the collection of detailed data, such as how much energy the base station consumes at different times of the day and the percentage of renewable energy used. For instance, the data collection unit can collect detailed data to understand the energy consumption patterns of base stations. This includes power consumption, operating hours, and renewable energy supply. Furthermore, the data collection unit can also collect external data such as weather data and regional power supply conditions to help predict and optimize energy consumption. This allows the data collection unit to build a comprehensive dataset on base station energy consumption, enabling the analysis and operations units to efficiently utilize the data.

[0031] The analysis unit analyzes the collected data and proposes optimal operating methods for energy conservation. For example, the analysis unit proposes an operating method that concentrates base station operation during periods of low energy consumption. Specifically, it uses generative AI to analyze the collected data in real time and identify energy consumption patterns and trends. Based on past data, the AI ​​predicts future energy consumption and generates an optimal operating schedule. For example, by concentrating maintenance work during nighttime hours when energy consumption is low, overall energy consumption can be reduced. It also proposes an operating method that increases base station operation during periods when renewable energy is used to a high degree. This allows the analysis unit to propose optimal operating methods that take into account the rate of energy consumption reduction and cost efficiency. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual energy consumption patterns early and respond quickly. This allows the analysis unit to provide concrete and actionable operating methods to improve the energy efficiency of base stations.

[0032] The operations department implements the operational methods proposed by the analysis department. For example, the operations department adjusts the base station operating schedule based on the proposed operational methods. Specifically, it operates a system that automatically adjusts the operating hours and output of base stations according to the schedule proposed by the generating AI. For example, to concentrate maintenance work during periods of low energy consumption, some base stations may be shut down at night. Also, to increase base station operation during periods of high renewable energy use, the output of base stations may be maximized during peak times for solar and wind power generation. The operations department can adjust equipment and change schedules. By implementing the proposed operational methods, the energy efficiency of base stations can be improved. Furthermore, the operations department monitors the effectiveness of the implemented operational methods and makes adjustments as needed. For example, it regularly evaluates the rate of reduction in energy consumption and cost efficiency and identifies areas for improvement in the operational methods. This allows the operations department to continuously optimize the energy efficiency of base stations and reduce the environmental impact.

[0033] The Renewable Energy Analysis Department analyzes the utilization status of renewable energy. For example, it analyzes the utilization status of renewable energy sources such as solar and wind power. Specifically, it uses a generative AI to analyze the amount of renewable energy generated and its operating time. Based on weather data and power generation equipment operating data, the AI ​​predicts renewable energy supply patterns and proposes optimal utilization methods. For example, it maximizes renewable energy utilization by increasing base station operation during peak solar power generation periods. It also improves energy efficiency by analyzing wind power generation operating conditions and adjusting base station output during periods of strong winds. The Renewable Energy Analysis Department can analyze the amount of renewable energy generated and its operating time. This allows the Renewable Energy Analysis Department to gain a detailed understanding of renewable energy utilization and provide fundamental data for proposing optimal operational methods.

[0034] The Renewable Energy Proposal Department proposes operational methods based on the results analyzed by the Renewable Energy Analysis Department. For example, the Renewable Energy Proposal Department proposes operational methods to maximize the use of renewable energy. Specifically, it uses a generation AI to integrate the supply patterns of renewable energy and the energy consumption patterns of base stations to generate an optimal operational schedule. For example, it maximizes the use of renewable energy by increasing the operation of base stations during peak solar power generation periods. It also proposes operational methods that adjust the output of base stations during periods of strong winds, taking into account the operating status of wind power generation. The Renewable Energy Proposal Department can propose optimal operational methods considering the usage status of renewable energy. As a result, the Renewable Energy Proposal Department can provide specific operational methods to maximize the use of renewable energy and improve the energy efficiency of base stations. Furthermore, the Renewable Energy Proposal Department monitors the effectiveness of the proposed operational methods and makes adjustments as needed. As a result, the Renewable Energy Proposal Department can continuously optimize the energy efficiency of base stations and reduce the environmental impact.

[0035] The data collection unit can collect detailed data such as the operating status of base stations, energy consumption, and renewable energy utilization. For example, the data collection unit can monitor the operating status of base stations and record operating hours and utilization rates. For example, the data collection unit can measure energy consumption and record power consumption and energy consumption. For example, the data collection unit can monitor the utilization of renewable energy and record power generation and utilization rates. This makes it possible to understand the energy consumption patterns of base stations. Detailed data includes, but is not limited to, operating hours, power consumption, and weather data. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can acquire the operating status and energy consumption of base stations using sensors and analyze the data using AI.

[0036] The analysis unit can propose optimal operating methods for energy conservation based on the collected data. For example, the analysis unit can propose an operating method that concentrates base station operation during periods of low energy consumption. For example, the analysis unit can propose an operating method that increases base station operation during periods of high renewable energy utilization. For example, the analysis unit can propose an optimal operating method that considers factors such as the rate of energy consumption reduction and cost efficiency. An optimal operating method includes, but is not limited to, the rate of energy consumption reduction and cost efficiency. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs the collected data into the generating AI, and the generating AI proposes an optimal operating method. For example, the generating AI analyzes energy consumption data and outputs an optimal operating method.

[0037] The operations department can implement the proposed operational methods. For example, the operations department can adjust the base station's operating schedule based on the proposed operational methods. The operations department can, for example, adjust equipment or change the schedule. For example, the operations department can improve the energy efficiency of the base station by implementing the proposed operational methods. The proposed operational methods include, but are not limited to, changes in the schedule or adjustments to equipment. Some or all of the above processes in the operations department may be performed using AI or not. For example, the operations department inputs the proposed operational methods into the AI, and the AI ​​executes the operational methods.

[0038] The Renewable Energy Analysis Unit can analyze the utilization status of renewable energy sources such as solar power and wind power. For example, the Renewable Energy Analysis Unit can analyze the amount of electricity generated by solar power and the operating hours of wind power. For example, the Renewable Energy Analysis Unit monitors the utilization status of renewable energy and records the amount of electricity generated and the utilization rate. For example, by analyzing the utilization status of renewable energy, the Renewable Energy Analysis Unit can provide data to propose optimal operating methods. The utilization status of renewable energy includes, but is not limited to, the amount of electricity generated by solar power and the operating hours of wind power. Some or all of the above processing in the Renewable Energy Analysis Unit is performed using a Generating AI. For example, the Renewable Energy Analysis Unit inputs the utilization status of renewable energy into the Generating AI, and the Generating AI outputs the analysis results.

[0039] The Renewable Energy Proposal Department can propose operational methods to maximize the use of renewable energy. For example, the Renewable Energy Proposal Department analyzes the usage status of renewable energy such as solar power and wind power and proposes the optimal operational method. For example, the Renewable Energy Proposal Department can propose the optimal operational method considering the usage status of renewable energy. Operational methods include, but are not limited to, energy consumption reduction rates and cost efficiency. Some or all of the above processing in the Renewable Energy Proposal Department is performed using a Generative AI. For example, the Renewable Energy Proposal Department inputs the usage status of renewable energy into the Generative AI, and the Generative AI proposes the optimal operational method. For example, the Generative AI analyzes renewable energy usage data and outputs the optimal operational method.

[0040] The data collection unit can analyze historical energy consumption data from base stations and select the optimal data collection method. For example, the data collection unit can enhance data collection during peak times based on historical energy consumption data. For example, the data collection unit can identify periods of low energy consumption from historical data and concentrate data collection during those periods. For example, the data collection unit can analyze historical data and focus data collection on periods when anomalies occurred. This allows the unit to select the optimal data collection method based on historical energy consumption data. The optimal data collection method includes, but is not limited to, the frequency of data collection and the type of data to be collected. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs historical energy consumption data into a generating AI, and the generating AI selects the optimal data collection method.

[0041] The data collection unit can filter data based on the geographical and meteorological conditions of the base station during data collection. For example, the data collection unit can enhance data collection in specific areas such as mountainous or urban areas based on geographical conditions. For example, the data collection unit can collect data under different meteorological conditions, such as sunny or rainy weather, based on meteorological conditions. For example, the data collection unit can combine geographical and meteorological conditions to collect energy consumption data under specific conditions. This enables data collection based on geographical and meteorological conditions. Geographical and meteorological conditions include, but are not limited to, altitude, temperature, and precipitation. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs geographical and meteorological conditions into a generating AI, which then performs filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data based on the base station's operating schedule during data collection. For example, the data collection unit can prioritize the collection of data before and after maintenance in accordance with the base station's maintenance schedule. For example, the data collection unit can prioritize the collection of peak data based on the base station's operating schedule. For example, the data collection unit can prioritize the collection of data during times when anomalies are likely to occur, taking into account the base station's operating schedule. This allows for the priority collection of highly relevant data based on the base station's operating schedule. The operating schedule includes, but is not limited to, operating hours and maintenance schedules. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the base station's operating schedule into a generating AI, and the generating AI prioritizes the collection of highly relevant data.

[0043] The data collection unit can analyze ambient environmental data around the base station and collect relevant data during data collection. For example, the data collection unit can identify factors affecting energy consumption based on ambient environmental data around the base station and collect that data. For example, the data collection unit can adjust the frequency and timing of data collection in response to changes in the ambient environment. For example, the data collection unit can analyze ambient environmental data and collect data to respond quickly in the event of an anomaly. This allows for the analysis of ambient environmental data around the base station and the collection of relevant data. Ambient environmental data includes, but is not limited to, temperature, humidity, and noise levels. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs ambient environmental data into a generating AI, and the generating AI collects relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of energy consumption. For example, the analysis unit performs a detailed analysis on energy consumption data with high importance. For example, the analysis unit performs a simplified analysis on energy consumption data with low importance. For example, the analysis unit adjusts the frequency and timing of the analysis according to the importance of energy consumption. This allows the level of detail of the analysis to be adjusted based on the importance of energy consumption. The importance of energy consumption includes, but is not limited to, the magnitude of power consumption and cost impact. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs energy consumption data into the generating AI, and the generating AI adjusts the level of detail of the analysis based on importance.

[0045] The analysis unit can apply different analysis algorithms depending on the energy consumption category during analysis. For example, the analysis unit can apply a specific analysis algorithm to communication energy consumption. For example, the analysis unit can apply a different analysis algorithm to cooling energy consumption. For example, the analysis unit can apply the optimal analysis algorithm to lighting energy consumption. This allows different analysis algorithms to be applied depending on the energy consumption category. Energy consumption categories include, but are not limited to, lighting, heating and cooling, and communication equipment. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs energy consumption data into the generating AI, and the generating AI applies different analysis algorithms depending on the category.

[0046] The analysis unit can determine the priority of analysis based on the timing of energy consumption. For example, the analysis unit may prioritize the analysis of peak energy consumption data. For example, it may postpone the analysis of off-peak energy consumption data. For example, if an anomaly occurs during a specific period, the analysis unit may prioritize the analysis of data from that period. This allows the analysis priority to be determined based on the timing of energy consumption. The timing of energy consumption includes, but is not limited to, seasons and time of day. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs energy consumption data into the generating AI, and the generating AI determines the priority of analysis based on the timing.

[0047] The analysis unit can adjust the order of analysis based on the relationships between energy consumption during the analysis. For example, the analysis unit determines the order of analysis by considering the relationship between communication energy consumption and cooling energy consumption. For example, the analysis unit adjusts the order of analysis by considering the relationship between lighting energy consumption and other energy consumption. For example, the analysis unit determines the optimal order of analysis based on the relationships between energy consumption. This allows the order of analysis to be adjusted based on the relationships between energy consumption. The relationships between energy consumption include, but are not limited to, the correlation and influence of power consumption. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs energy consumption data into the generating AI, and the generating AI adjusts the order of analysis based on the relationships.

[0048] The operations department can analyze past operational data of base stations during operation to select the optimal operating method. For example, the operations department can determine the optimal operating schedule based on past operational data. For example, the operations department can identify time periods when anomalies are likely to occur from past operational data and focus operations during those time periods. For example, the operations department can analyze past operational data to select the most efficient operating method. This allows for the selection of the optimal operating method based on past operational data. The optimal operating method includes, but is not limited to, energy consumption reduction rates and cost efficiency. Some or all of the above processes in the operations department may be performed using AI or not. For example, the operations department inputs past operational data into a generating AI, and the generating AI selects the optimal operating method.

[0049] The operations unit can customize the operational methods based on the current operational status of the base station during operation. For example, the operations unit can select the optimal operational method based on the current operational status. For example, the operations unit can adjust the operational methods in real time according to the current operational status. For example, the operations unit can analyze the current operational status and customize the operational methods to respond quickly in the event of an anomaly. This allows the operational methods to be customized based on the current operational status. The current operational status includes, but is not limited to, uptime and power consumption. Some or all of the above processing in the operations unit may be performed using AI or not. For example, the operations unit inputs the current operational status into a generating AI, and the generating AI customizes the operational methods.

[0050] The operations department can select the optimal operating method based on the geographical location information of base stations during operation. For example, the operations department can select an operating method for a specific area, such as a mountainous area or an urban area, based on geographical location information. For example, the operations department can strengthen the operating method in areas prone to anomalies based on geographical location information. For example, the operations department can determine the optimal operating schedule, taking geographical location information into consideration. This allows for the selection of the optimal operating method based on geographical location information. Geographical location information includes, but is not limited to, latitude, longitude, and altitude. Some or all of the above processing in the operations department may be performed using AI or not. For example, the operations department inputs geographical location information into a generating AI, and the generating AI selects the optimal operating method.

[0051] The operations unit can analyze ambient environmental data of the base station during operation and propose operational measures. For example, the operations unit can identify factors affecting energy consumption based on ambient environmental data and propose operational measures based on those factors. For example, the operations unit can adjust operational measures in real time in response to changes in the ambient environment. For example, the operations unit can analyze ambient environmental data and propose operational measures to respond quickly in the event of an anomaly. This allows for the analysis of ambient environmental data and the proposal of operational measures. Ambient environmental data includes, but is not limited to, temperature, humidity, and noise levels. Some or all of the above processing in the operations unit may be performed using AI or not. For example, the operations unit inputs ambient environmental data into a generating AI, and the generating AI proposes operational measures.

[0052] The renewable energy analysis unit can optimize its analysis algorithm by referring to past renewable energy data during the analysis of renewable energy. For example, the renewable energy analysis unit selects the optimal analysis algorithm based on past renewable energy data. For example, the renewable energy analysis unit identifies renewable energy usage patterns from past data and adjusts the analysis algorithm based on those patterns. For example, the renewable energy analysis unit analyzes past data and optimizes the analysis algorithm so that it can respond quickly if an anomaly occurs. This allows the analysis algorithm to be optimized based on past renewable energy data. Past renewable energy data includes, but is not limited to, power generation amount and operating hours. Some or all of the above processing in the renewable energy analysis unit is performed using a generating AI. For example, the renewable energy analysis unit inputs past renewable energy data into the generating AI, and the generating AI optimizes the analysis algorithm.

[0053] The renewable energy analysis unit can apply different analysis methods to each type of renewable energy during its analysis. For example, the renewable energy analysis unit can apply a specific analysis method to solar power generation. For example, it can apply a different analysis method to wind power generation. For example, it can apply the optimal analysis method to biomass power generation. This allows the application of the optimal analysis method to each type of renewable energy. Types of renewable energy include, but are not limited to, solar power generation and wind power generation. Some or all of the above processing in the renewable energy analysis unit is performed using a generating AI. For example, the renewable energy analysis unit inputs data for each type of renewable energy into the generating AI, and the generating AI applies different analysis methods.

[0054] The Renewable Energy Analysis Unit can perform analyses of renewable energy based on its geographical distribution. For example, the Renewable Energy Analysis Unit can analyze the utilization of renewable energy in a specific region based on its geographical distribution. For example, the Renewable Energy Analysis Unit can analyze the utilization of renewable energy in areas prone to anomalies based on its geographical distribution. For example, the Renewable Energy Analysis Unit can select the optimal analysis method considering the geographical distribution. This enables the analysis of renewable energy based on its geographical distribution. Geographical distribution includes, but is not limited to, the location of power plants and the amount of power generated in each region. Some or all of the above-described processes in the Renewable Energy Analysis Unit are performed using a Generative AI. For example, the Renewable Energy Analysis Unit inputs geographical distribution data into the Generative AI, and the Generative AI performs the analysis.

[0055] The renewable energy analysis unit can improve the accuracy of its analysis by referring to relevant literature on renewable energy. For example, the renewable energy analysis unit selects the optimal analysis method based on the relevant literature. For example, the renewable energy analysis unit identifies renewable energy utilization patterns from the relevant literature and adjusts the analysis method based on those patterns. For example, the renewable energy analysis unit optimizes the analysis method by referring to relevant literature to enable a rapid response in the event of anomalies. This allows the accuracy of the analysis to be improved by referring to relevant literature. Relevant literature includes, but is not limited to, academic papers and technical reports. Some or all of the above processes in the renewable energy analysis unit are performed using a generating AI. For example, the renewable energy analysis unit inputs relevant literature data into the generating AI, and the generating AI optimizes the analysis method.

[0056] The renewable energy proposal unit can improve the accuracy of its proposals by considering the interrelationships of renewable energy sources. For example, the renewable energy proposal unit can make optimal proposals by considering the interrelationships of solar power and wind power. For example, the renewable energy proposal unit can improve the accuracy of its proposals by considering the interrelationships of biomass power and other renewable energy sources. For example, the renewable energy proposal unit can analyze the interrelationships of renewable energy sources and make the most efficient proposals. This allows the accuracy of proposals to be improved by considering the interrelationships of renewable energy sources. Interrelationships of renewable energy sources include, but are not limited to, the interaction between solar power and wind power. Some or all of the above processing in the renewable energy proposal unit is performed using a generating AI. For example, the renewable energy proposal unit inputs renewable energy interrelationship data into the generating AI, and the generating AI improves the accuracy of the proposals.

[0057] The renewable energy proposal unit can make proposals considering the user's attribute information when proposing renewable energy. For example, the renewable energy proposal unit makes the optimal renewable energy proposal based on the user's attribute information. For example, the renewable energy proposal unit makes different proposals based on the user's attribute information. For example, the renewable energy proposal unit selects the optimal proposal method considering the user's attribute information. This allows the unit to make the optimal proposal considering the user's attribute information. User attribute information includes, but is not limited to, age, gender, and purpose of use. Some or all of the above processing in the renewable energy proposal unit is performed using a generation AI. For example, the renewable energy proposal unit inputs the user's attribute information into the generation AI, and the generation AI makes the optimal proposal.

[0058] The Renewable Energy Proposal Department can make proposals for renewable energy based on the geographical distribution of renewable energy. For example, the Renewable Energy Proposal Department can make renewable energy proposals for specific regions based on geographical distribution. For example, the Renewable Energy Proposal Department can make renewable energy proposals for regions prone to anomalies based on geographical distribution. For example, the Renewable Energy Proposal Department can select the optimal proposal method considering geographical distribution. This enables the department to make renewable energy proposals based on geographical distribution. Geographical distribution includes, but is not limited to, the location of power plants and the amount of power generated in each region. Some or all of the above processing in the Renewable Energy Proposal Department is performed using a Generative AI. For example, the Renewable Energy Proposal Department inputs geographical distribution data into the Generative AI, and the Generative AI makes proposals.

[0059] The renewable energy proposal unit can improve the accuracy of its proposals by referring to relevant renewable energy literature when proposing renewable energy. For example, the renewable energy proposal unit selects the optimal proposal method based on relevant literature. For example, the renewable energy proposal unit identifies renewable energy utilization patterns from relevant literature and adjusts the proposal method based on those patterns. For example, the renewable energy proposal unit optimizes the proposal method by referring to relevant literature to enable a rapid response in the event of anomalies. This allows the accuracy of proposals to be improved by referring to relevant literature. Relevant literature includes, but is not limited to, academic papers and technical reports. Some or all of the above processing in the renewable energy proposal unit is performed using a generation AI. For example, the renewable energy proposal unit inputs relevant literature data into the generation AI, and the generation AI optimizes the proposal method.

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

[0061] The data collection unit can collect energy consumption data from base stations while simultaneously collecting data on the surrounding environment. For example, it can collect weather data such as temperature, humidity, and wind speed around the base station and analyze this data in combination with energy consumption data. This allows for an understanding of the impact of weather conditions on the base station's energy consumption and enables the proposal of more accurate energy-saving operation methods. It can also collect surrounding traffic volume data and population density data and optimize the base station's operating schedule based on this data. Furthermore, it can collect data on the height and placement of surrounding buildings and optimize the base station's antenna placement based on this data. This further improves the energy efficiency of the base station.

[0062] The analysis unit can refer to past base station operation data when proposing optimal operation methods for energy conservation based on collected data. For example, it can identify peak and low energy consumption periods based on past energy consumption data and propose operation methods appropriate to those times. It can also detect anomalies from past data and analyze their causes to propose operation methods that prevent future anomalies. Furthermore, it can propose operation methods that adapt to seasonal and weather changes based on past data. This can further improve the energy efficiency of base stations.

[0063] The operations department can monitor the base station's operating status in real time and adjust the operation method as needed when implementing the proposed operation method. For example, if the base station's energy consumption is higher than expected, the operation method can be immediately changed to reduce energy consumption. Furthermore, if the use of renewable energy fluctuates, the operation method can be adjusted accordingly to maximize its use. In addition, if an abnormality occurs in the base station's equipment, the system can detect the abnormality and implement a rapid response operation method. This further improves the energy efficiency of the base station.

[0064] The Renewable Energy Analysis Unit can also refer to renewable energy generation forecast data when analyzing the utilization status of renewable energy. For example, based on solar power generation forecast data, it can propose operational methods to increase base station operation during periods of high power generation. Similarly, based on wind power generation forecast data, it can propose operational methods to increase base station operation during periods of strong winds. Furthermore, based on renewable energy generation forecast data, it can propose operational methods to reduce energy consumption during periods of low power generation. This maximizes the use of renewable energy and further improves the energy efficiency of base stations.

[0065] The Renewable Energy Proposal Department can also consider renewable energy cost data when proposing operational methods to maximize the use of renewable energy. For example, it can propose cost-effective operational methods based on cost data for solar and wind power generation. It can also propose operational methods that reduce energy consumption during less cost-effective times based on renewable energy cost data. Furthermore, it can propose operational methods that prioritize the use of cost-effective energy sources based on renewable energy cost data. This will maximize the use of renewable energy and further improve the energy efficiency of base stations.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects energy consumption data from mobile phone base stations. For example, it collects detailed data such as the operating status of the base stations, energy consumption, and the utilization of renewable energy. Specifically, it collects data such as how much energy the base stations consume at what times of day, and what percentage of renewable energy is used. Step 2: The analysis unit analyzes the collected data and proposes the optimal operating method for energy conservation. For example, it proposes an operating method that concentrates base station operation during periods of low energy consumption, or an operating method that increases base station operation during periods of high renewable energy utilization. Step 3: The operations department implements the operational methods proposed by the analysis department. For example, they adjust the base station operating schedule based on the proposed operational methods and make adjustments to equipment and schedule changes. Step 4: The Renewable Energy Analysis Unit analyzes the utilization status of renewable energy. For example, it analyzes the amount of electricity generated and the operating hours of renewable energy sources such as solar power and wind power. Step 5: The Renewable Energy Proposal Department proposes operational methods based on the results analyzed by the Renewable Energy Analysis Department. For example, it proposes operational methods to maximize the use of renewable energy.

[0068] (Example of form 2) The system according to an embodiment of the present invention, which entrusts energy conservation and renewable energy utilization of a mobile phone base station to a generating AI, collects energy consumption data from the mobile phone base station, and the generating AI analyzes this data to propose an optimal operation method for energy conservation and an operation method to maximize the use of renewable energy. The system, which entrusts energy conservation and renewable energy utilization of a mobile phone base station to a generating AI, collects energy consumption data from the mobile phone base station, and the generating AI analyzes this data to propose an optimal operation method for energy conservation. Furthermore, the generating AI proposes an operation method to maximize the use of renewable energy. This mechanism improves the energy efficiency of the mobile phone base station and reduces its environmental impact. For example, energy consumption data from the mobile phone base station is collected. At this time, detailed data such as the operating status of the base station, energy consumption, and the status of renewable energy utilization are collected. For example, data such as how much energy the base station consumes at what time of day and what percentage of renewable energy is used is collected. This makes it possible to understand the energy consumption pattern of the base station. Next, the generating AI analyzes the collected data. Based on the collected data, the generating AI proposes an optimal operation method for energy conservation. For example, the system proposes operational methods such as concentrating base station operations during periods of low energy consumption and increasing base station operations during periods of high renewable energy utilization. This improves the energy efficiency of base stations. Furthermore, the generating AI proposes operational methods to maximize the use of renewable energy. For example, it analyzes the usage status of renewable energy sources such as solar and wind power and proposes the optimal operational method. This maximizes the use of renewable energy and reduces the environmental impact. This mechanism improves the energy efficiency of mobile phone base stations and reduces their environmental impact. For example, by concentrating base station operations during periods of low energy consumption, energy consumption can be reduced. Also, by increasing base station operations during periods of high renewable energy utilization, the use of renewable energy can be maximized. This makes the operation of mobile phone base stations more efficient and results in environmentally friendly operations.This means that a system that entrusts energy conservation and the utilization of renewable energy in mobile phone base stations to generating AI can improve the energy efficiency of mobile phone base stations and reduce their environmental impact.

[0069] The system according to this embodiment, which entrusts energy conservation and renewable energy utilization of a mobile phone base station to a generating AI, comprises a data collection unit, an analysis unit, an operation unit, a renewable energy analysis unit, and a renewable energy proposal unit. The data collection unit collects energy consumption data of the mobile phone base station. The data collection unit collects detailed data such as the operating status of the base station, energy consumption, and the utilization status of renewable energy. For example, the data collection unit collects data such as how much energy the base station consumes at what time of day and what percentage of renewable energy is used. For example, the data collection unit can collect detailed data to understand the energy consumption pattern of the base station. The analysis unit analyzes the collected data and proposes the optimal operation method for energy conservation. For example, the analysis unit proposes an operation method that concentrates the operation of the base station during times when energy consumption is low. For example, the analysis unit proposes an operation method that increases the operation of the base station during times when renewable energy is used to a high degree. For example, the analysis unit can propose the optimal operation method considering the energy consumption reduction rate and cost efficiency. The operation unit executes the operation method proposed by the analysis unit. The operations department adjusts the base station's operating schedule based on the proposed operating method. The operations department can, for example, adjust equipment and change schedules. The operations department can, for example, improve the energy efficiency of base stations by implementing the proposed operating method. The renewable energy analysis department analyzes the utilization status of renewable energy. The renewable energy analysis department analyzes the utilization status of renewable energy such as solar power and wind power. The renewable energy analysis department can, for example, analyze the amount of renewable energy generated and its operating time. The renewable energy proposal department proposes an operating method based on the results analyzed by the renewable energy analysis department. The renewable energy proposal department proposes an operating method to maximize the utilization of renewable energy. The renewable energy proposal department can, for example, propose an optimal operating method considering the utilization status of renewable energy.As a result, the system according to this embodiment, which entrusts energy conservation and the utilization of renewable energy in mobile phone base stations to a generating AI, can improve the energy efficiency of mobile phone base stations and reduce the environmental impact.

[0070] The data collection unit collects energy consumption data from mobile phone base stations. For example, it collects detailed data such as the operating status, energy consumption, and renewable energy utilization of the base stations. Specifically, it acquires data in real time from various sensors and meters installed at the base stations and transmits it to a central database. This allows for the collection of detailed data, such as how much energy the base station consumes at different times of the day and the percentage of renewable energy used. For instance, the data collection unit can collect detailed data to understand the energy consumption patterns of base stations. This includes power consumption, operating hours, and renewable energy supply. Furthermore, the data collection unit can also collect external data such as weather data and regional power supply conditions to help predict and optimize energy consumption. This allows the data collection unit to build a comprehensive dataset on base station energy consumption, enabling the analysis and operations units to efficiently utilize the data.

[0071] The analysis unit analyzes the collected data and proposes optimal operating methods for energy conservation. For example, the analysis unit proposes an operating method that concentrates base station operation during periods of low energy consumption. Specifically, it uses generative AI to analyze the collected data in real time and identify energy consumption patterns and trends. Based on past data, the AI ​​predicts future energy consumption and generates an optimal operating schedule. For example, by concentrating maintenance work during nighttime hours when energy consumption is low, overall energy consumption can be reduced. It also proposes an operating method that increases base station operation during periods when renewable energy is used to a high degree. This allows the analysis unit to propose optimal operating methods that take into account the rate of energy consumption reduction and cost efficiency. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual energy consumption patterns early and respond quickly. This allows the analysis unit to provide concrete and actionable operating methods to improve the energy efficiency of base stations.

[0072] The operations department implements the operational methods proposed by the analysis department. For example, the operations department adjusts the base station operating schedule based on the proposed operational methods. Specifically, it operates a system that automatically adjusts the operating hours and output of base stations according to the schedule proposed by the generating AI. For example, to concentrate maintenance work during periods of low energy consumption, some base stations may be shut down at night. Also, to increase base station operation during periods of high renewable energy use, the output of base stations may be maximized during peak times for solar and wind power generation. The operations department can adjust equipment and change schedules. By implementing the proposed operational methods, the energy efficiency of base stations can be improved. Furthermore, the operations department monitors the effectiveness of the implemented operational methods and makes adjustments as needed. For example, it regularly evaluates the rate of reduction in energy consumption and cost efficiency and identifies areas for improvement in the operational methods. This allows the operations department to continuously optimize the energy efficiency of base stations and reduce the environmental impact.

[0073] The Renewable Energy Analysis Department analyzes the utilization status of renewable energy. For example, it analyzes the utilization status of renewable energy sources such as solar and wind power. Specifically, it uses a generative AI to analyze the amount of renewable energy generated and its operating time. Based on weather data and power generation equipment operating data, the AI ​​predicts renewable energy supply patterns and proposes optimal utilization methods. For example, it maximizes renewable energy utilization by increasing base station operation during peak solar power generation periods. It also improves energy efficiency by analyzing wind power generation operating conditions and adjusting base station output during periods of strong winds. The Renewable Energy Analysis Department can analyze the amount of renewable energy generated and its operating time. This allows the Renewable Energy Analysis Department to gain a detailed understanding of renewable energy utilization and provide fundamental data for proposing optimal operational methods.

[0074] The Renewable Energy Proposal Department proposes operational methods based on the results analyzed by the Renewable Energy Analysis Department. For example, the Renewable Energy Proposal Department proposes operational methods to maximize the use of renewable energy. Specifically, it uses a generation AI to integrate the supply patterns of renewable energy and the energy consumption patterns of base stations to generate an optimal operational schedule. For example, it maximizes the use of renewable energy by increasing the operation of base stations during peak solar power generation periods. It also proposes operational methods that adjust the output of base stations during periods of strong winds, taking into account the operating status of wind power generation. The Renewable Energy Proposal Department can propose optimal operational methods considering the usage status of renewable energy. As a result, the Renewable Energy Proposal Department can provide specific operational methods to maximize the use of renewable energy and improve the energy efficiency of base stations. Furthermore, the Renewable Energy Proposal Department monitors the effectiveness of the proposed operational methods and makes adjustments as needed. As a result, the Renewable Energy Proposal Department can continuously optimize the energy efficiency of base stations and reduce the environmental impact.

[0075] The data collection unit can collect detailed data such as the operating status of base stations, energy consumption, and renewable energy utilization. For example, the data collection unit can monitor the operating status of base stations and record operating hours and utilization rates. For example, the data collection unit can measure energy consumption and record power consumption and energy consumption. For example, the data collection unit can monitor the utilization of renewable energy and record power generation and utilization rates. This makes it possible to understand the energy consumption patterns of base stations. Detailed data includes, but is not limited to, operating hours, power consumption, and weather data. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can acquire the operating status and energy consumption of base stations using sensors and analyze the data using AI.

[0076] The analysis unit can propose optimal operating methods for energy conservation based on the collected data. For example, the analysis unit can propose an operating method that concentrates base station operation during periods of low energy consumption. For example, the analysis unit can propose an operating method that increases base station operation during periods of high renewable energy utilization. For example, the analysis unit can propose an optimal operating method that considers factors such as the rate of energy consumption reduction and cost efficiency. An optimal operating method includes, but is not limited to, the rate of energy consumption reduction and cost efficiency. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs the collected data into the generating AI, and the generating AI proposes an optimal operating method. For example, the generating AI analyzes energy consumption data and outputs an optimal operating method.

[0077] The operations department can implement the proposed operational methods. For example, the operations department can adjust the base station's operating schedule based on the proposed operational methods. The operations department can, for example, adjust equipment or change the schedule. For example, the operations department can improve the energy efficiency of the base station by implementing the proposed operational methods. The proposed operational methods include, but are not limited to, changes in the schedule or adjustments to equipment. Some or all of the above processes in the operations department may be performed using AI or not. For example, the operations department inputs the proposed operational methods into the AI, and the AI ​​executes the operational methods.

[0078] The Renewable Energy Analysis Unit can analyze the utilization status of renewable energy sources such as solar power and wind power. For example, the Renewable Energy Analysis Unit can analyze the amount of electricity generated by solar power and the operating hours of wind power. For example, the Renewable Energy Analysis Unit monitors the utilization status of renewable energy and records the amount of electricity generated and the utilization rate. For example, by analyzing the utilization status of renewable energy, the Renewable Energy Analysis Unit can provide data to propose optimal operating methods. The utilization status of renewable energy includes, but is not limited to, the amount of electricity generated by solar power and the operating hours of wind power. Some or all of the above processing in the Renewable Energy Analysis Unit is performed using a Generating AI. For example, the Renewable Energy Analysis Unit inputs the utilization status of renewable energy into the Generating AI, and the Generating AI outputs the analysis results.

[0079] The Renewable Energy Proposal Department can propose operational methods to maximize the use of renewable energy. For example, the Renewable Energy Proposal Department analyzes the usage status of renewable energy such as solar power and wind power and proposes the optimal operational method. For example, the Renewable Energy Proposal Department can propose the optimal operational method considering the usage status of renewable energy. Operational methods include, but are not limited to, energy consumption reduction rates and cost efficiency. Some or all of the above processing in the Renewable Energy Proposal Department is performed using a Generative AI. For example, the Renewable Energy Proposal Department inputs the usage status of renewable energy into the Generative AI, and the Generative AI proposes the optimal operational method. For example, the Generative AI analyzes renewable energy usage data and outputs the optimal operational method.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. For example, if the user is relaxed, the data collection unit can collect detailed data to improve accuracy. For example, if the user is in a hurry, the data collection unit can collect data quickly to prepare for immediate analysis. This allows the timing of data collection to be adjusted according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's facial expression data into the generative AI, the generative AI estimates the emotions, and adjusts the timing of data collection.

[0081] The data collection unit can analyze historical energy consumption data from base stations and select the optimal data collection method. For example, the data collection unit can enhance data collection during peak times based on historical energy consumption data. For example, the data collection unit can identify periods of low energy consumption from historical data and concentrate data collection during those periods. For example, the data collection unit can analyze historical data and focus data collection on periods when anomalies occurred. This allows the unit to select the optimal data collection method based on historical energy consumption data. The optimal data collection method includes, but is not limited to, the frequency of data collection and the type of data to be collected. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs historical energy consumption data into a generating AI, and the generating AI selects the optimal data collection method.

[0082] The data collection unit can filter data based on the geographical and meteorological conditions of the base station during data collection. For example, the data collection unit can enhance data collection in specific areas such as mountainous or urban areas based on geographical conditions. For example, the data collection unit can collect data under different meteorological conditions, such as sunny or rainy weather, based on meteorological conditions. For example, the data collection unit can combine geographical and meteorological conditions to collect energy consumption data under specific conditions. This enables data collection based on geographical and meteorological conditions. Geographical and meteorological conditions include, but are not limited to, altitude, temperature, and precipitation. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs geographical and meteorological conditions into a generating AI, which then performs filtering.

[0083] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only high-priority data. If the user is relaxed, for example, the data collection unit will collect detailed data to improve the accuracy of the analysis. If the user is in a hurry, for example, the data collection unit will prioritize data that can be collected quickly. This allows the priority of data to be collected to be determined according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit inputs the user's facial expression data into the generative AI, which estimates the emotions and determines the priority of data to collect.

[0084] The data collection unit can prioritize the collection of highly relevant data based on the base station's operating schedule during data collection. For example, the data collection unit can prioritize the collection of data before and after maintenance in accordance with the base station's maintenance schedule. For example, the data collection unit can prioritize the collection of peak data based on the base station's operating schedule. For example, the data collection unit can prioritize the collection of data during times when anomalies are likely to occur, taking into account the base station's operating schedule. This allows for the priority collection of highly relevant data based on the base station's operating schedule. The operating schedule includes, but is not limited to, operating hours and maintenance schedules. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the base station's operating schedule into a generating AI, and the generating AI prioritizes the collection of highly relevant data.

[0085] The data collection unit can analyze ambient environmental data around the base station and collect relevant data during data collection. For example, the data collection unit can identify factors affecting energy consumption based on ambient environmental data around the base station and collect that data. For example, the data collection unit can adjust the frequency and timing of data collection in response to changes in the ambient environment. For example, the data collection unit can analyze ambient environmental data and collect data to respond quickly in the event of an anomaly. This allows for the analysis of ambient environmental data around the base station and the collection of relevant data. Ambient environmental data includes, but is not limited to, temperature, humidity, and noise levels. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs ambient environmental data into a generating AI, and the generating AI collects relevant data.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit provides detailed analysis results to deepen understanding. If the user is in a hurry, the analysis unit provides concise analysis results that get straight to the point. This allows the presentation of the analysis to be adjusted according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit is performed using the generative AI. For example, the analysis unit inputs the user's facial expression data into the generative AI, the generative AI estimates the emotions, and adjusts the presentation of the analysis.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of energy consumption. For example, the analysis unit performs a detailed analysis on energy consumption data with high importance. For example, the analysis unit performs a simplified analysis on energy consumption data with low importance. For example, the analysis unit adjusts the frequency and timing of the analysis according to the importance of energy consumption. This allows the level of detail of the analysis to be adjusted based on the importance of energy consumption. The importance of energy consumption includes, but is not limited to, the magnitude of power consumption and cost impact. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs energy consumption data into the generating AI, and the generating AI adjusts the level of detail of the analysis based on importance.

[0088] The analysis unit can apply different analysis algorithms depending on the energy consumption category during analysis. For example, the analysis unit can apply a specific analysis algorithm to communication energy consumption. For example, the analysis unit can apply a different analysis algorithm to cooling energy consumption. For example, the analysis unit can apply the optimal analysis algorithm to lighting energy consumption. This allows different analysis algorithms to be applied depending on the energy consumption category. Energy consumption categories include, but are not limited to, lighting, heating and cooling, and communication equipment. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs energy consumption data into the generating AI, and the generating AI applies different analysis algorithms depending on the category.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. This allows the length of the analysis to be adjusted according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit is performed using the generative AI. For example, the analysis unit inputs the user's facial expression data into the generative AI, the generative AI estimates the emotions, and adjusts the length of the analysis.

[0090] The analysis unit can determine the priority of analysis based on the timing of energy consumption. For example, the analysis unit may prioritize the analysis of peak energy consumption data. For example, it may postpone the analysis of off-peak energy consumption data. For example, if an anomaly occurs during a specific period, the analysis unit may prioritize the analysis of data from that period. This allows the analysis priority to be determined based on the timing of energy consumption. The timing of energy consumption includes, but is not limited to, seasons and time of day. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs energy consumption data into the generating AI, and the generating AI determines the priority of analysis based on the timing.

[0091] The analysis unit can adjust the order of analysis based on the relationships between energy consumption during the analysis. For example, the analysis unit determines the order of analysis by considering the relationship between communication energy consumption and cooling energy consumption. For example, the analysis unit adjusts the order of analysis by considering the relationship between lighting energy consumption and other energy consumption. For example, the analysis unit determines the optimal order of analysis based on the relationships between energy consumption. This allows the order of analysis to be adjusted based on the relationships between energy consumption. The relationships between energy consumption include, but are not limited to, the correlation and influence of power consumption. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs energy consumption data into the generating AI, and the generating AI adjusts the order of analysis based on the relationships.

[0092] The operations unit can estimate the user's emotions and adjust the operation method based on the estimated emotions. For example, if the user is nervous, the operations unit provides a simple and highly visible operation method. For example, if the user is relaxed, the operations unit provides a detailed operation method. For example, if the user is in a hurry, the operations unit provides a concise operation method that gets straight to the point. This allows the operation method to be adjusted according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the operations unit may be performed using AI or not using AI. For example, the operations unit inputs the user's facial expression data into the generative AI, the generative AI estimates the emotions, and adjusts the operation method.

[0093] The operations department can analyze past operational data of base stations during operation to select the optimal operating method. For example, the operations department can determine the optimal operating schedule based on past operational data. For example, the operations department can identify time periods when anomalies are likely to occur from past operational data and focus operations during those time periods. For example, the operations department can analyze past operational data to select the most efficient operating method. This allows for the selection of the optimal operating method based on past operational data. The optimal operating method includes, but is not limited to, energy consumption reduction rates and cost efficiency. Some or all of the above processes in the operations department may be performed using AI or not. For example, the operations department inputs past operational data into a generating AI, and the generating AI selects the optimal operating method.

[0094] The operations unit can customize the operational methods based on the current operational status of the base station during operation. For example, the operations unit can select the optimal operational method based on the current operational status. For example, the operations unit can adjust the operational methods in real time according to the current operational status. For example, the operations unit can analyze the current operational status and customize the operational methods to respond quickly in the event of an anomaly. This allows the operational methods to be customized based on the current operational status. The current operational status includes, but is not limited to, uptime and power consumption. Some or all of the above processing in the operations unit may be performed using AI or not. For example, the operations unit inputs the current operational status into a generating AI, and the generating AI customizes the operational methods.

[0095] The operations department can estimate the user's emotions and determine operational priorities based on the estimated emotions. For example, if the user is stressed, the operations department will prioritize high-priority operations. If the user is relaxed, the operations department will perform detailed operations to improve accuracy. If the user is in a hurry, the operations department will prioritize operations that can be performed quickly. This allows operations priorities to be determined according to the user's emotions. The estimation of user emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the operations department may be performed using AI or not. For example, the operations department inputs user facial expression data into a generative AI, the generative AI estimates the emotions, and determines operational priorities.

[0096] The operations department can select the optimal operating method based on the geographical location information of base stations during operation. For example, the operations department can select an operating method for a specific area, such as a mountainous area or an urban area, based on geographical location information. For example, the operations department can strengthen the operating method in areas prone to anomalies based on geographical location information. For example, the operations department can determine the optimal operating schedule, taking geographical location information into consideration. This allows for the selection of the optimal operating method based on geographical location information. Geographical location information includes, but is not limited to, latitude, longitude, and altitude. Some or all of the above processing in the operations department may be performed using AI or not. For example, the operations department inputs geographical location information into a generating AI, and the generating AI selects the optimal operating method.

[0097] The operations unit can analyze ambient environmental data of the base station during operation and propose operational measures. For example, the operations unit can identify factors affecting energy consumption based on ambient environmental data and propose operational measures based on those factors. For example, the operations unit can adjust operational measures in real time in response to changes in the ambient environment. For example, the operations unit can analyze ambient environmental data and propose operational measures to respond quickly in the event of an anomaly. This allows for the analysis of ambient environmental data and the proposal of operational measures. Ambient environmental data includes, but is not limited to, temperature, humidity, and noise levels. Some or all of the above processing in the operations unit may be performed using AI or not. For example, the operations unit inputs ambient environmental data into a generating AI, and the generating AI proposes operational measures.

[0098] The Regenerative Energy Analysis Unit can estimate the user's emotions and adjust the Regenerative Energy Analysis Method based on the estimated user emotions. For example, if the user is tense, the Regenerative Energy Analysis Unit provides simple and highly visual analysis results. For example, if the user is relaxed, the Regenerative Energy Analysis Unit provides detailed analysis results to deepen understanding. For example, if the user is in a hurry, the Regenerative Energy Analysis Unit provides concise analysis results that get straight to the point. This allows the Regenerative Energy Analysis Method to be adjusted according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the Regenerative Energy Analysis Unit is performed using the generative AI. For example, the Regenerative Energy Analysis Unit inputs the user's facial expression data into the generative AI, which estimates the emotions and adjusts the analysis method.

[0099] The renewable energy analysis unit can optimize its analysis algorithm by referring to past renewable energy data during the analysis of renewable energy. For example, the renewable energy analysis unit selects the optimal analysis algorithm based on past renewable energy data. For example, the renewable energy analysis unit identifies renewable energy usage patterns from past data and adjusts the analysis algorithm based on those patterns. For example, the renewable energy analysis unit analyzes past data and optimizes the analysis algorithm so that it can respond quickly if an anomaly occurs. This allows the analysis algorithm to be optimized based on past renewable energy data. Past renewable energy data includes, but is not limited to, power generation amount and operating hours. Some or all of the above processing in the renewable energy analysis unit is performed using a generating AI. For example, the renewable energy analysis unit inputs past renewable energy data into the generating AI, and the generating AI optimizes the analysis algorithm.

[0100] The renewable energy analysis unit can apply different analysis methods to each type of renewable energy during its analysis. For example, the renewable energy analysis unit can apply a specific analysis method to solar power generation. For example, it can apply a different analysis method to wind power generation. For example, it can apply the optimal analysis method to biomass power generation. This allows the application of the optimal analysis method to each type of renewable energy. Types of renewable energy include, but are not limited to, solar power generation and wind power generation. Some or all of the above processing in the renewable energy analysis unit is performed using a generating AI. For example, the renewable energy analysis unit inputs data for each type of renewable energy into the generating AI, and the generating AI applies different analysis methods.

[0101] The Regenerative Energy Analysis Unit can estimate the user's emotions and adjust the display method of the Regenerative Energy Analysis Results based on the estimated user emotions. For example, if the user is tense, the Regenerative Energy Analysis Unit provides a simple and highly visible display method. For example, if the user is relaxed, the Regenerative Energy Analysis Unit provides a display method that includes detailed information. For example, if the user is in a hurry, the Regenerative Energy Analysis Unit provides a display method that gets straight to the point. This allows the display method of the Regenerative Energy Analysis Results to be adjusted according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the Regenerative Energy Analysis Unit is performed using the generative AI. For example, the Regenerative Energy Analysis Unit inputs the user's facial expression data into the generative AI, the generative AI estimates the emotions, and adjusts the display method.

[0102] The Renewable Energy Analysis Unit can perform analyses of renewable energy based on its geographical distribution. For example, the Renewable Energy Analysis Unit can analyze the utilization of renewable energy in a specific region based on its geographical distribution. For example, the Renewable Energy Analysis Unit can analyze the utilization of renewable energy in areas prone to anomalies based on its geographical distribution. For example, the Renewable Energy Analysis Unit can select the optimal analysis method considering the geographical distribution. This enables the analysis of renewable energy based on its geographical distribution. Geographical distribution includes, but is not limited to, the location of power plants and the amount of power generated in each region. Some or all of the above-described processes in the Renewable Energy Analysis Unit are performed using a Generative AI. For example, the Renewable Energy Analysis Unit inputs geographical distribution data into the Generative AI, and the Generative AI performs the analysis.

[0103] The renewable energy analysis unit can improve the accuracy of its analysis by referring to relevant literature on renewable energy. For example, the renewable energy analysis unit selects the optimal analysis method based on the relevant literature. For example, the renewable energy analysis unit identifies renewable energy utilization patterns from the relevant literature and adjusts the analysis method based on those patterns. For example, the renewable energy analysis unit optimizes the analysis method by referring to relevant literature to enable a rapid response in the event of anomalies. This allows the accuracy of the analysis to be improved by referring to relevant literature. Relevant literature includes, but is not limited to, academic papers and technical reports. Some or all of the above processes in the renewable energy analysis unit are performed using a generating AI. For example, the renewable energy analysis unit inputs relevant literature data into the generating AI, and the generating AI optimizes the analysis method.

[0104] The renewable energy suggestion unit can estimate the user's emotions and determine the priority of renewable energy suggestions based on the estimated emotions. For example, if the user is stressed, the renewable energy suggestion unit will prioritize suggestions for high-priority renewable energy. For example, if the user is relaxed, the renewable energy suggestion unit will provide detailed suggestions for renewable energy. For example, if the user is in a hurry, the renewable energy suggestion unit will prioritize suggestions for renewable energy that can be quickly implemented. This allows the priority of renewable energy to be determined according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the renewable energy suggestion unit is performed using the generative AI. For example, the renewable energy suggestion unit inputs the user's facial expression data into the generative AI, which estimates the emotions and determines the priority of renewable energy suggestions.

[0105] The renewable energy proposal unit can improve the accuracy of its proposals by considering the interrelationships of renewable energy sources. For example, the renewable energy proposal unit can make optimal proposals by considering the interrelationships of solar power and wind power. For example, the renewable energy proposal unit can improve the accuracy of its proposals by considering the interrelationships of biomass power and other renewable energy sources. For example, the renewable energy proposal unit can analyze the interrelationships of renewable energy sources and make the most efficient proposals. This allows the accuracy of proposals to be improved by considering the interrelationships of renewable energy sources. Interrelationships of renewable energy sources include, but are not limited to, the interaction between solar power and wind power. Some or all of the above processing in the renewable energy proposal unit is performed using a generating AI. For example, the renewable energy proposal unit inputs renewable energy interrelationship data into the generating AI, and the generating AI improves the accuracy of the proposals.

[0106] The renewable energy proposal unit can make proposals considering the user's attribute information when proposing renewable energy. For example, the renewable energy proposal unit makes the optimal renewable energy proposal based on the user's attribute information. For example, the renewable energy proposal unit makes different proposals based on the user's attribute information. For example, the renewable energy proposal unit selects the optimal proposal method considering the user's attribute information. This allows the unit to make the optimal proposal considering the user's attribute information. User attribute information includes, but is not limited to, age, gender, and purpose of use. Some or all of the above processing in the renewable energy proposal unit is performed using a generation AI. For example, the renewable energy proposal unit inputs the user's attribute information into the generation AI, and the generation AI makes the optimal proposal.

[0107] The renewable energy suggestion unit can estimate the user's emotions and adjust the display method of the suggested renewable energy based on the estimated user emotions. For example, if the user is tense, the renewable energy suggestion unit provides a simple and highly visible display method. For example, if the user is relaxed, the renewable energy suggestion unit provides a display method that includes detailed information. For example, if the user is in a hurry, the renewable energy suggestion unit provides a display method that gets straight to the point. This allows the display method of renewable energy to be adjusted according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the renewable energy suggestion unit is performed using the generative AI. For example, the renewable energy suggestion unit inputs the user's facial expression data into the generative AI, the generative AI estimates the emotions, and adjusts the display method.

[0108] The Renewable Energy Proposal Department can make proposals for renewable energy based on the geographical distribution of renewable energy. For example, the Renewable Energy Proposal Department can make renewable energy proposals for specific regions based on geographical distribution. For example, the Renewable Energy Proposal Department can make renewable energy proposals for regions prone to anomalies based on geographical distribution. For example, the Renewable Energy Proposal Department can select the optimal proposal method considering geographical distribution. This enables the department to make renewable energy proposals based on geographical distribution. Geographical distribution includes, but is not limited to, the location of power plants and the amount of power generated in each region. Some or all of the above processing in the Renewable Energy Proposal Department is performed using a Generative AI. For example, the Renewable Energy Proposal Department inputs geographical distribution data into the Generative AI, and the Generative AI makes proposals.

[0109] The renewable energy proposal unit can improve the accuracy of its proposals by referring to relevant renewable energy literature when proposing renewable energy. For example, the renewable energy proposal unit selects the optimal proposal method based on relevant literature. For example, the renewable energy proposal unit identifies renewable energy utilization patterns from relevant literature and adjusts the proposal method based on those patterns. For example, the renewable energy proposal unit optimizes the proposal method by referring to relevant literature to enable a rapid response in the event of anomalies. This allows the accuracy of proposals to be improved by referring to relevant literature. Relevant literature includes, but is not limited to, academic papers and technical reports. Some or all of the above processing in the renewable energy proposal unit is performed using a generation AI. For example, the renewable energy proposal unit inputs relevant literature data into the generation AI, and the generation AI optimizes the proposal method.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The data collection unit can collect energy consumption data from base stations while simultaneously collecting data on the surrounding environment. For example, it can collect weather data such as temperature, humidity, and wind speed around the base station and analyze this data in combination with energy consumption data. This allows for an understanding of the impact of weather conditions on the base station's energy consumption and enables the proposal of more accurate energy-saving operation methods. It can also collect surrounding traffic volume data and population density data and optimize the base station's operating schedule based on this data. Furthermore, it can collect data on the height and placement of surrounding buildings and optimize the base station's antenna placement based on this data. This further improves the energy efficiency of the base station.

[0112] The analysis unit can refer to past base station operation data when proposing optimal operation methods for energy conservation based on collected data. For example, it can identify peak and low energy consumption periods based on past energy consumption data and propose operation methods appropriate to those times. It can also detect anomalies from past data and analyze their causes to propose operation methods that prevent future anomalies. Furthermore, it can propose operation methods that adapt to seasonal and weather changes based on past data. This can further improve the energy efficiency of base stations.

[0113] The operations department can monitor the base station's operating status in real time and adjust the operation method as needed when implementing the proposed operation method. For example, if the base station's energy consumption is higher than expected, the operation method can be immediately changed to reduce energy consumption. Furthermore, if the use of renewable energy fluctuates, the operation method can be adjusted accordingly to maximize its use. In addition, if an abnormality occurs in the base station's equipment, the system can detect the abnormality and implement a rapid response operation method. This further improves the energy efficiency of the base station.

[0114] The Renewable Energy Analysis Unit can also refer to renewable energy generation forecast data when analyzing the utilization status of renewable energy. For example, based on solar power generation forecast data, it can propose operational methods to increase base station operation during periods of high power generation. Similarly, based on wind power generation forecast data, it can propose operational methods to increase base station operation during periods of strong winds. Furthermore, based on renewable energy generation forecast data, it can propose operational methods to reduce energy consumption during periods of low power generation. This maximizes the use of renewable energy and further improves the energy efficiency of base stations.

[0115] The Renewable Energy Proposal Department can also consider renewable energy cost data when proposing operational methods to maximize the use of renewable energy. For example, it can propose cost-effective operational methods based on cost data for solar and wind power generation. It can also propose operational methods that reduce energy consumption during less cost-effective times based on renewable energy cost data. Furthermore, it can propose operational methods that prioritize the use of cost-effective energy sources based on renewable energy cost data. This will maximize the use of renewable energy and further improve the energy efficiency of base stations.

[0116] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the burden. If the user is relaxed, detailed data collection can be performed to improve accuracy. If the user is in a hurry, data can be collected quickly to prepare for immediate analysis. This allows the timing of data collection to be adjusted according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's facial expression data into the generative AI, the generative AI estimates the emotions, and the timing of data collection is adjusted.

[0117] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, it provides a simple and easy-to-understand analysis result. If the user is relaxed, it provides a detailed analysis result to deepen understanding. If the user is in a hurry, it provides a concise analysis result that gets straight to the point. This allows the presentation of the analysis to be adjusted according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit inputs the user's facial expression data into the generative AI, the generative AI estimates the emotions, and adjusts the presentation of the analysis.

[0118] The operations department can estimate the user's emotions and adjust the operation method based on the estimated emotions. For example, if the user is nervous, a simple and highly visible operation method is provided. If the user is relaxed, a detailed operation method is provided. If the user is in a hurry, a concise operation method that gets straight to the point is provided. This allows the operation method to be adjusted according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the operations department may be performed using AI or not using AI. For example, the operations department inputs the user's facial expression data into a generative AI, the generative AI estimates the emotions, and adjusts the operation method.

[0119] The Regenerative Energy Analysis Unit can estimate the user's emotions and adjust the Regenerative Energy analysis method based on the estimated user emotions. For example, if the user is tense, it provides a simple and easy-to-understand analysis result. If the user is relaxed, it provides a detailed analysis result to deepen understanding. If the user is in a hurry, it provides a concise analysis result that gets straight to the point. This allows the Regenerative Energy analysis method to be adjusted according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the Regenerative Energy Analysis Unit is performed using the generative AI. For example, the Regenerative Energy Analysis Unit inputs the user's facial expression data into the generative AI, which estimates the emotions and adjusts the analysis method.

[0120] The renewable energy suggestion unit can estimate the user's emotions and determine the priority of renewable energy suggestions based on those emotions. For example, if the user is stressed, it prioritizes suggestions for high-priority renewable energy. If the user is relaxed, it provides detailed suggestions for renewable energy. If the user is in a hurry, it prioritizes suggestions for renewable energy that can be quickly implemented. This allows the system to prioritize renewable energy according to the user's emotions. User emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the renewable energy suggestion unit is performed using generative AI. For example, the renewable energy suggestion unit inputs user facial expression data into the generative AI, which estimates the emotions and determines the priority of renewable energy suggestions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects energy consumption data from mobile phone base stations. For example, it collects detailed data such as the operating status of the base stations, energy consumption, and the utilization of renewable energy. Specifically, it collects data such as how much energy the base stations consume at what times of day, and what percentage of renewable energy is used. Step 2: The analysis unit analyzes the collected data and proposes the optimal operating method for energy conservation. For example, it proposes an operating method that concentrates base station operation during periods of low energy consumption, or an operating method that increases base station operation during periods of high renewable energy utilization. Step 3: The operations department implements the operational methods proposed by the analysis department. For example, they adjust the base station operating schedule based on the proposed operational methods and make adjustments to equipment and schedule changes. Step 4: The Renewable Energy Analysis Unit analyzes the utilization status of renewable energy. For example, it analyzes the amount of electricity generated and the operating hours of renewable energy sources such as solar power and wind power. Step 5: The Renewable Energy Proposal Department proposes operational methods based on the results analyzed by the Renewable Energy Analysis Department. For example, it proposes operational methods to maximize the use of renewable energy.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0126] Each of the multiple elements described above, including the data collection unit, analysis unit, operation unit, renewable energy analysis unit, and renewable energy proposal unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects energy consumption data of the base station using the camera 42 and sensors of the smart device 14. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to propose an optimal operation method for energy saving. The operation unit is implemented, for example, by the control unit 46A of the smart device 14, and adjusts the operating schedule of the base station based on the proposed operation method. The renewable energy analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the utilization status of renewable energy. The renewable energy proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes an operation method to maximize the utilization of renewable energy. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the data collection unit, analysis unit, operation unit, renewable energy analysis unit, and renewable energy proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects energy consumption data of the base station using the camera 42 and sensors of the smart glasses 214. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to propose an optimal operation method for energy saving. The operation unit is implemented, for example, by the control unit 46A of the smart glasses 214, and adjusts the operating schedule of the base station based on the proposed operation method. The renewable energy analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the utilization status of renewable energy. The renewable energy proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes an operation method to maximize the utilization of renewable energy. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the data collection unit, analysis unit, operation unit, renewable energy analysis unit, and renewable energy proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects energy consumption data of the base station using the camera 42 and sensors of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to propose an optimal operation method for energy saving. The operation unit is implemented in the control unit 46A of the headset terminal 314, for example, and adjusts the base station's operating schedule based on the proposed operation method. The renewable energy analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the utilization status of renewable energy. The renewable energy proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes an operation method to maximize the utilization of renewable energy. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the data collection unit, analysis unit, operation unit, renewable energy analysis unit, and renewable energy proposal unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects energy consumption data of the base station using the camera 42 and sensors of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and proposes an optimal operation method for energy saving. The operation unit is implemented, for example, by the control unit 46A of the robot 414, which adjusts the operating schedule of the base station based on the proposed operation method. The renewable energy analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the utilization status of renewable energy. The renewable energy proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes an operation method to maximize the utilization of renewable energy. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A data collection unit that collects energy consumption data from mobile phone base stations, An analysis unit analyzes the data collected by the aforementioned data collection unit and proposes an optimal operating method for energy saving, An operation unit that executes the operation method proposed by the analysis unit, The renewable energy analysis unit analyzes the status of renewable energy utilization, The system includes a renewable energy proposal unit that proposes an operation method based on the results of analysis performed by the renewable energy analysis unit. A system characterized by the following features. (Note 2) The aforementioned data acquisition unit is Collect detailed data such as the operating status of base stations, energy consumption, and the utilization of renewable energy. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, we propose the optimal operating methods for energy conservation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned operations unit, Implement the proposed operational method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned renewable energy analysis unit, Analyze the utilization status of renewable energy sources such as solar and wind power. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned renewable energy proposal unit, We propose operational methods to maximize the use of renewable energy. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned data acquisition unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned data acquisition unit is Analyze historical energy consumption data from base stations to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned data acquisition unit is When collecting data, filtering is performed based on the geographical and weather conditions of the base station. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned data acquisition unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned data acquisition unit is During data collection, the system prioritizes the collection of highly relevant data based on the base station's operating schedule. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned data acquisition unit is During data collection, the system analyzes the surrounding environment data of the base station and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, adjust the level of detail based on the importance of energy consumption. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the energy consumption category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on the timing of energy consumption. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the order of analysis is adjusted based on the relationship between energy consumption. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned operations unit, It estimates user sentiment and adjusts operational methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned operations unit, During operation, the optimal operating method is selected by analyzing past operational data of the base station. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned operations unit, During operation, the operational methods are customized based on the current operational status of the base station. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned operations unit, The system estimates user sentiment and determines operational priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned operations unit, During operation, the optimal operating method is selected based on the geographical location information of the base station. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned operations unit, During operation, we analyze the surrounding environment data of the base station and propose operational methods. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned renewable energy analysis unit, We estimate the user's emotions and adjust the renewable energy analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned renewable energy analysis unit, When analyzing renewable energy, we optimize the analysis algorithm by referring to past renewable energy data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned renewable energy analysis unit, When analyzing renewable energy, different analytical methods are applied depending on the type of renewable energy. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned renewable energy analysis unit, The system estimates the user's emotions and adjusts how the renewable energy analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned renewable energy analysis unit, When analyzing renewable energy, the analysis is based on the geographical distribution of renewable energy sources. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned renewable energy analysis unit, When analyzing renewable energy, refer to relevant literature on renewable energy to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned renewable energy proposal unit, It estimates the user's emotions and determines the priority of renewable energy options based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned renewable energy proposal unit, When proposing renewable energy solutions, consider the interrelationships between different renewable energy sources to improve the accuracy of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned renewable energy proposal unit, When proposing renewable energy solutions, consider the attribute information of the renewable energy users. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned renewable energy proposal unit, We estimate the user's emotions and adjust the way renewable energy is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned renewable energy proposal unit, When proposing renewable energy solutions, the proposal should be based on the geographical distribution of renewable energy sources. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned renewable energy proposal unit, When proposing renewable energy solutions, refer to relevant literature on renewable energy to improve the accuracy of the proposal. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects energy consumption data from mobile phone base stations, An analysis unit analyzes the data collected by the aforementioned data collection unit and proposes an optimal operating method for energy saving, An operation unit that executes the operation method proposed by the analysis unit, The renewable energy analysis unit analyzes the status of renewable energy utilization, The system includes a renewable energy proposal unit that proposes an operation method based on the results of analysis performed by the renewable energy analysis unit. A system characterized by the following features.

2. The aforementioned data acquisition unit is Collect detailed data such as the operating status of base stations, energy consumption, and the utilization of renewable energy. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, we propose the optimal operating methods for energy conservation. The system according to feature 1.

4. The aforementioned operations unit, Implement the proposed operational method. The system according to feature 1.

5. The aforementioned renewable energy analysis unit, Analyze the utilization status of renewable energy sources such as solar and wind power. The system according to feature 1.

6. The aforementioned renewable energy proposal unit, We propose operational methods to maximize the use of renewable energy. The system according to feature 1.

7. The aforementioned data acquisition unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned data acquisition unit is Analyze historical energy consumption data from base stations to select the optimal data collection method. The system according to feature 1.

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