system

Abstract

The system according to the embodiment aims to optimally adjust the orientation and angle of solar panels based on weather data, and to perform anomaly detection and power supply management during disasters. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a control unit, a detection unit, and a charging management unit. The data collection unit collects weather data. The analysis unit analyzes the data collected by the data collection unit and predicts future weather patterns. The control unit optimally adjusts the orientation, angle, and area of ​​the solar panels based on the weather patterns predicted by the analysis unit. The detection unit analyzes images of the solar panels in real time and detects abnormalities. The charging management unit charges EVs using electricity generated by solar power and manages power supply to disaster areas and base stations in the event of a disaster.

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 and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to optimally adjust the orientation and angle of a solar panel based on meteorological data, and there is also room for improvement in abnormal detection and power supply management during disasters.

[0005] The system according to the embodiment aims to optimally adjust the orientation and angle of a solar panel based on meteorological data and perform abnormal detection and power supply management during disasters.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a control unit, a detection unit, and a charging management unit. The data collection unit collects weather data. The analysis unit analyzes the data collected by the data collection unit and predicts future weather patterns. The control unit optimally adjusts the orientation, angle, and area of ​​the solar panels based on the weather patterns predicted by the analysis unit. The detection unit analyzes images of the solar panels in real time and detects abnormalities. The charging management unit charges EVs using electricity generated by solar power and manages power supply to disaster areas and base stations in the event of a disaster. [Effects of the Invention]

[0007] The system according to the embodiment can optimally adjust the orientation and angle of solar panels based on weather data, enabling anomaly detection and power supply management during disasters. [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, a labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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 solar power generation system according to an embodiment of the present invention is a system that automatically optimizes the orientation, angle, and area of ​​a slide-adjustable solar panel using weather pattern learning and real-time AI image analysis. This system aims to improve power generation prediction and energy efficiency, thereby reducing electricity costs. It also aims to reduce the environmental burden by detecting anomalies caused by typhoons and snowfall, and reducing damage to and disposal of solar panels. Furthermore, by using it in conjunction with EV charging facilities, it enables power supply to disaster-stricken areas and base stations in the event of a disaster. For example, the solar power generation system learns weather patterns. The AI ​​learns past weather data and predicts future weather patterns. Based on this prediction, it optimally adjusts the orientation, angle, and area of ​​the solar panel. For example, if sunny weather is predicted, the solar panel is adjusted to track the sun at the optimal angle. On the other hand, if a typhoon or strong winds are predicted, the solar panel is positioned horizontally to minimize the effects of the wind. Next, the solar power generation system performs real-time AI image analysis. The AI ​​analyzes images of the solar panel in real time and detects anomalies. For example, if snowfall is detected, the solar panels are positioned vertically to prevent snow accumulation. Also, if an anomaly is detected, the system automatically issues a warning and takes necessary measures. Furthermore, the solar power generation system is integrated with EV charging facilities. This system utilizes solar power to charge EVs. This enables power supply to disaster-stricken areas and base stations during emergencies. For example, in the event of a typhoon or earthquake, EVs can be used to supply power to the affected areas. This allows the solar power generation system to improve predictability of power generation and energy efficiency, resulting in reduced electricity costs. Additionally, by detecting anomalies caused by typhoons and snowfall, damage to solar panels and disposal are reduced, minimizing the environmental burden. Furthermore, by integrating with EV charging facilities, power supply to disaster-stricken areas and base stations during emergencies is possible, improving the stability of regional energy supply. This enables the solar power generation system to improve predictability of power generation and energy efficiency, resulting in reduced electricity costs. Additionally, by detecting anomalies caused by typhoons and snowfall, damage to solar panels and disposal are reduced, minimizing the environmental burden.Furthermore, by using EV charging facilities in conjunction with this system, it becomes possible to supply power to disaster-stricken areas and base stations during emergencies, thereby improving the stability of regional energy supply.

[0029] The photovoltaic power generation system according to this embodiment comprises a data collection unit, an analysis unit, a control unit, a detection unit, and a charge management unit. The data collection unit collects weather data. The data collection unit can collect weather data such as temperature, humidity, wind speed, and precipitation. The data collection unit can also collect historical and real-time weather data. For example, the data collection unit can obtain historical weather data from a database and real-time weather data from sensors. The analysis unit analyzes the data collected by the data collection unit and predicts future weather patterns. The analysis unit can analyze weather data using, for example, a machine learning algorithm to predict future weather patterns. The analysis unit can also analyze historical and real-time weather data in combination. For example, the analysis unit can supplement real-time data with historical weather data to improve prediction accuracy. The control unit optimally adjusts the orientation, angle, and area of ​​the solar panels based on the weather patterns predicted by the analysis unit. The control unit, for example, controls a motor for adjusting the orientation of the solar panels and adjusts it to track the sun at the optimal angle. The control unit can also control a sliding mechanism for adjusting the area of ​​the solar panels. For example, the control unit operates a sliding mechanism to maximize the area of ​​the solar panels. The detection unit analyzes images of the solar panels in real time and detects abnormalities. The detection unit can analyze images of the solar panels using, for example, AI image analysis technology to detect abnormalities. The detection unit can also detect abnormalities such as snowfall or damage. For example, if snowfall is detected, the detection unit will position the solar panels vertically to prevent snow accumulation. The charging management unit charges EVs using electricity generated by solar power and manages power supply to disaster-stricken areas and base stations in the event of a disaster. The charging management unit manages facilities that charge EVs using electricity generated by solar power. The charging management unit can also manage power supply to disaster-stricken areas and base stations in the event of a disaster. For example, if a disaster such as a typhoon or earthquake occurs, the charging management unit will use EVs to supply power to the disaster-stricken area. As a result, the solar power generation system according to this embodiment can efficiently collect, analyze, control, detect abnormalities, and manage charging using weather data.

[0030] The data collection unit collects meteorological data. For example, it can collect data such as temperature, humidity, wind speed, and precipitation. Specifically, the unit acquires this data in real time using multiple sensors. Temperature sensors accurately measure ambient temperature, humidity sensors detect moisture content in the air, wind speed sensors measure wind strength and direction, and precipitation sensors measure the amount of rain or snow. These sensors are installed around the solar panels to constantly collect the latest meteorological data. The data collection unit can also retrieve historical meteorological data from a database. Historical meteorological data is useful for analyzing long-term weather patterns and is important for improving the accuracy of future predictions. For example, analyzing weather data from the past few years can reveal seasonal weather patterns and the frequency of extreme weather events. Furthermore, the data collection unit can also obtain real-time weather information from the Japan Meteorological Agency and other weather data providers via the internet. This allows the data collection unit to collect diverse meteorological data from a wide range of data sources, improving the overall accuracy and reliability of the system.

[0031] The analysis unit analyzes data collected by the data collection unit and predicts future weather patterns. For example, the analysis unit can use machine learning algorithms to analyze weather data and predict future weather patterns. Specifically, the analysis unit combines historical and real-time weather data for analysis. Machine learning algorithms have the ability to learn from large amounts of data and predict variations in weather patterns. For example, they can improve prediction accuracy by supplementing real-time data with historical weather data. The analysis unit uses data such as temperature, humidity, wind speed, and precipitation as input to build a model that predicts future weather conditions. This model can learn from historical data and predict future weather patterns with high accuracy. Furthermore, the analysis unit can also assess the risk of extreme weather events. For example, based on historical data, the analysis unit calculates the probability of extreme weather events occurring under specific conditions and performs a risk assessment. This allows the analysis unit to predict future weather patterns and provide information to optimize the overall system operation.

[0032] The control unit optimally adjusts the orientation, angle, and area of ​​the solar panels based on the weather patterns predicted by the analysis unit. Specifically, the control unit controls the motors for adjusting the orientation of the solar panels, adjusting them to track the sun at the optimal angle. This ensures that the solar panels always receive the maximum amount of sunlight, improving power generation efficiency. The control unit can also control the sliding mechanism for adjusting the area of ​​the solar panels. For example, the control unit operates the sliding mechanism to maximize the area of ​​the solar panels, thereby maximizing the power generation capacity of the solar panels. Furthermore, the control unit can also optimize the placement of the solar panels according to weather conditions. For example, if strong winds are predicted, the control unit adjusts the solar panels to an angle less susceptible to wind damage to prevent panel breakage. Also, if snowfall is predicted, the control unit positions the solar panels vertically to prevent snow accumulation. In this way, the control unit can optimize the placement of the solar panels according to weather conditions, improving the overall efficiency and safety of the system.

[0033] The detection unit analyzes images of solar panels in real time to detect abnormalities. Specifically, the detection unit can analyze images of solar panels using AI image analysis technology to detect abnormalities. For example, the detection unit acquires images of solar panels using a camera and analyzes the images using an AI algorithm. The AI ​​algorithm detects whether there are any abnormalities on the panel surface and issues an alert if an abnormality is detected. The detection unit can also detect abnormalities such as snowfall and damage. For example, if snowfall is detected, the detection unit will position the solar panels vertically to prevent snow accumulation. Also, if cracks or dirt are detected on the panel surface, the detection unit will notify that maintenance is required. In this way, the detection unit can constantly monitor the condition of the solar panels and respond quickly when an abnormality occurs. Furthermore, the detection unit can also suggest appropriate countermeasures depending on the type and severity of the abnormality. For example, it may recommend cleaning for minor dirt and recommend panel replacement for serious damage. In this way, the detection unit can detect abnormalities in solar panels early and take appropriate measures, thereby improving the reliability and efficiency of the entire system.

[0034] The Charging Management Unit uses solar power to charge EVs and manages power supply to disaster-stricken areas and base stations during emergencies. Specifically, the Charging Management Unit manages facilities that charge EVs using solar power. The Charging Management Unit monitors EV charging stations and can grasp the progress of charging in real time. For example, the Charging Management Unit monitors the power supply status of each charging station and optimizes power distribution as needed. Furthermore, the Charging Management Unit can also manage power supply to disaster-stricken areas and base stations during emergencies. For example, in the event of a disaster such as a typhoon or earthquake, the Charging Management Unit uses EVs to supply power to the affected areas. The Charging Management Unit can grasp the power demand of the affected areas in real time and formulate an optimal power supply plan. This allows the Charging Management Unit to supply power quickly and efficiently even during disasters and support the recovery of the affected areas. In addition, the Charging Management Unit can set power supply priorities and supply power preferentially to important facilities and infrastructure. For example, it can supply power preferentially to important facilities such as hospitals and evacuation centers to ensure the safety and health of disaster victims. This allows the charging management unit to efficiently manage EV charging during normal times and to supply power quickly and appropriately during disasters, thereby improving the overall reliability and flexibility of the system.

[0035] The data collection unit can collect historical and real-time weather data. For example, the data collection unit can obtain historical weather data from a database and real-time weather data from sensors. The data collection unit can also improve prediction accuracy by supplementing real-time data with historical weather data. For example, the data collection unit can analyze historical weather data and combine it with real-time data to improve prediction accuracy. In this way, prediction accuracy is improved by collecting historical and real-time weather data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input historical weather data into AI, and the AI ​​can analyze the data to improve prediction accuracy.

[0036] The analysis unit can analyze collected weather data and predict future weather patterns. For example, the analysis unit can use machine learning algorithms to analyze weather data and predict future weather patterns. The analysis unit can also combine and analyze historical weather data with real-time weather data. For example, the analysis unit can use historical weather data to complement real-time data and improve prediction accuracy. This makes it possible to predict future weather patterns through the analysis of weather data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input collected weather data into a generative AI, which can then analyze the data and predict future weather patterns.

[0037] The control unit can optimally adjust the orientation, angle, and area of ​​the solar panels based on predicted weather patterns. For example, the control unit controls a motor for adjusting the orientation of the solar panels, adjusting it to track the sun at the optimal angle. The control unit can also control a sliding mechanism for adjusting the area of ​​the solar panels. For example, the control unit operates the sliding mechanism to maximize the area of ​​the solar panels. This improves power generation efficiency by optimally adjusting the solar panels based on weather patterns. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the control unit can input a predicted weather pattern into a generative AI, and the generative AI can output the optimal adjustment method for the solar panels.

[0038] The detection unit can analyze images of solar panels in real time and detect abnormalities. For example, the detection unit can analyze images of solar panels using AI image analysis technology to detect abnormalities. The detection unit can also detect abnormalities such as snowfall or damage. For example, if snowfall is detected, the detection unit will position the solar panels vertically to prevent snow accumulation. This enables a rapid response by detecting abnormalities in real time. Some or all of the above processing in the detection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the detection unit can input image data of the solar panels acquired in real time into a generating AI, which can then detect abnormalities.

[0039] The charging management unit can charge EVs using electricity generated by solar power and manage power supply to disaster-stricken areas and base stations in the event of a disaster. For example, the charging management unit manages facilities that charge EVs using electricity generated by solar power. The charging management unit can also manage power supply to disaster-stricken areas and base stations in the event of a disaster. For example, in the event of a disaster such as a typhoon or earthquake, the charging management unit can use EVs to supply electricity to the disaster-stricken area. This makes it possible to supply power to disaster-stricken areas and base stations even in the event of a disaster. Some or all of the above processing in the charging management unit may be performed using, for example, a generating AI, or it may be performed without using a generating AI. For example, the charging management unit can input the status of electricity supply from solar power generation into a generating AI, and the generating AI can output the optimal charging management method.

[0040] The data collection unit can collect more accurate data by combining historical and real-time weather data. For example, the data collection unit can improve prediction accuracy by supplementing real-time data with historical weather data. The data collection unit can also detect anomalies by comparing real-time weather data with historical data. The data collection unit can also optimize the timing of real-time data collection by referring to past weather patterns. This improves prediction accuracy by combining historical and real-time data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input historical and real-time weather data into a generating AI, which can then analyze the data to improve prediction accuracy.

[0041] The data collection unit can prioritize the collection of data for specific weather conditions when collecting weather data. For example, when a typhoon is approaching, the data collection unit will prioritize the collection of wind speed and precipitation data. When heavy rain is predicted, the data collection unit can also prioritize the collection of precipitation and river water level data. When extreme weather is occurring, the data collection unit can also concentrate on collecting weather data for that area. This improves the accuracy of extreme weather predictions by prioritizing the collection of data for specific weather conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data related to specific weather conditions into a generating AI, which can then analyze the data to improve the accuracy of extreme weather predictions.

[0042] The data collection unit can prioritize the collection of data from specific regions, taking geographical characteristics into consideration when collecting meteorological data. For example, the data collection unit can prioritize the collection of meteorological data from mountainous areas to improve the accuracy of weather forecasts in mountainous regions. The data collection unit can also prioritize the collection of meteorological data from urban areas to analyze meteorological phenomena specific to cities. The data collection unit can also prioritize the collection of meteorological data from coastal areas to improve the accuracy of ocean weather forecasts. In this way, the accuracy of weather forecasts for specific regions is improved by taking geographical characteristics into consideration. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input meteorological data from a specific region into a generating AI, which can then analyze the data to improve the accuracy of weather forecasts.

[0043] The data collection unit can analyze information from social media and collect relevant weather data when collecting weather data. For example, the data collection unit can analyze posts on social media to supplement real-time weather information. The data collection unit can also collect posts on social media about extreme weather and compare them with weather data. The data collection unit can also collect regional weather information from social media to improve data accuracy. This allows real-time weather information to be supplemented by utilizing information from social media. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information from social media into a generating AI, which can then analyze the information to supplement weather data.

[0044] The analysis unit can predict future weather patterns by comparing past weather patterns with current data during analysis. For example, the analysis unit predicts future weather patterns by comparing current data with past weather data. The analysis unit can also predict the occurrence of extreme weather events by analyzing past extreme weather data and comparing it with current data. The analysis unit can also perform long-term weather forecasts by analyzing current data with reference to past weather patterns. This improves the accuracy of future weather pattern predictions by comparing past weather patterns with current data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input past weather data and current data into a generative AI, which can analyze the data and predict future weather patterns.

[0045] The analysis unit can apply algorithms to improve prediction accuracy for specific weather conditions during analysis. For example, the analysis unit can apply algorithms to improve typhoon prediction accuracy. The analysis unit can also apply algorithms to improve heavy rainfall prediction accuracy. The analysis unit can also apply algorithms to improve extreme weather prediction accuracy. This improves prediction accuracy for specific weather conditions. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on specific weather conditions into a generative AI, and the generative AI can analyze the data to improve prediction accuracy.

[0046] The analysis unit can prioritize predicting weather patterns for specific regions by considering geographical characteristics during analysis. For example, the analysis unit can prioritize predicting weather patterns in mountainous areas to improve the accuracy of weather forecasts in mountainous regions. The analysis unit can also prioritize predicting weather patterns in urban areas to analyze weather phenomena specific to cities. The analysis unit can also prioritize predicting weather patterns in coastal areas to improve the accuracy of ocean weather forecasts. In this way, the accuracy of weather forecasts for specific regions is improved by considering geographical characteristics. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input weather data for a specific region into a generating AI, and the generating AI can analyze the data to improve the accuracy of weather forecasts.

[0047] The analysis unit can improve the accuracy of its analysis by referring to relevant scientific literature during the analysis process. For example, the analysis unit can improve its analysis algorithm by referring to the latest scientific literature. The analysis unit can also verify the analysis results by referring to past scientific literature. The analysis unit can also introduce new methods to improve the accuracy of the analysis based on relevant scientific literature. As a result, the accuracy of the analysis is improved by referring to relevant scientific literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input scientific literature data into a generating AI, which can then analyze the data to improve the accuracy of the analysis.

[0048] The control unit can select the optimal adjustment method by referring to past adjustment history during control. For example, the control unit selects the optimal adjustment method based on past adjustment history. The control unit can also analyze past adjustment history and identify areas for improvement in the adjustment method. The control unit can also optimize the adjustment method by referring to past adjustment history. In this way, the optimal adjustment method can be selected by referring to past adjustment history. Some or all of the above processes in the control unit may be performed using AI, for example, or without using AI. For example, the control unit can input past adjustment history into a generating AI, and the generating AI can analyze the data and select the optimal adjustment method.

[0049] The control unit can customize the adjustment method for specific weather conditions during control. For example, the control unit can customize the adjustment method to arrange the solar panels horizontally when a typhoon is approaching. The control unit can also customize the adjustment method to arrange the solar panels vertically when heavy rain is predicted. The control unit can also customize the adjustment method for the solar panels when extreme weather is occurring. By customizing the adjustment method for specific weather conditions, the adjustment accuracy is improved. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input data on specific weather conditions into a generating AI, which can then analyze the data and customize the adjustment method.

[0050] The control unit can prioritize adjusting solar panels in specific areas, taking geographical characteristics into account during control. For example, the control unit can prioritize adjusting solar panels in mountainous areas to improve power generation efficiency in mountainous regions. The control unit can also prioritize adjusting solar panels in urban areas to address weather phenomena specific to cities. The control unit can also prioritize adjusting solar panels in coastal areas to address marine weather. This improves the accuracy of adjusting solar panels in specific areas by taking geographical characteristics into account. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input weather data for a specific area into a generating AI, which can then analyze the data to determine how to adjust the solar panels.

[0051] The control unit can improve the accuracy of the adjustment by referring to relevant technical literature during control. For example, the control unit can improve the adjustment algorithm by referring to the latest technical literature. The control unit can also verify adjustment methods by referring to past technical literature. The control unit can also introduce new methods to improve the accuracy of the adjustment based on relevant technical literature. Thus, the accuracy of the adjustment is improved by referring to relevant technical literature. Some or all of the above processes in the control unit may be performed using AI, for example, or not using AI. For example, the control unit can input data from technical literature into a generating AI, and the generating AI can analyze the data to improve the adjustment accuracy.

[0052] The detection unit can improve the accuracy of anomaly detection by referring to past anomaly data when an anomaly is detected. For example, the detection unit can improve the anomaly detection algorithm based on past anomaly data. The detection unit can also analyze past anomaly data and identify anomaly detection patterns. The detection unit can also improve the accuracy of anomaly detection by referring to past anomaly data. As a result, the accuracy of anomaly detection is improved by referring to past anomaly data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input past anomaly data into a generating AI, and the generating AI can analyze the data to improve the accuracy of anomaly detection.

[0053] The detection unit can customize its detection method for specific anomaly conditions when detection occurs. For example, when a typhoon is approaching, the detection unit will prioritize detecting anomalies in wind speed and precipitation. When heavy rain is predicted, the detection unit can also prioritize detecting anomalies in precipitation and river water levels. When extreme weather is occurring, the detection unit can also concentrate on detecting anomalies in that area. By customizing the detection method for specific anomaly conditions, the accuracy of anomaly detection is improved. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data related to specific anomaly conditions into a generating AI, which can then analyze the data and customize the detection method.

[0054] The detection unit can prioritize detecting anomalies in specific areas, taking geographical characteristics into consideration. For example, the detection unit can prioritize detecting anomalies in mountainous areas to ensure safety in mountainous regions. The detection unit can also prioritize detecting anomalies in urban areas to respond to anomalies specific to cities. The detection unit can also prioritize detecting anomalies in coastal areas to respond to anomalies in marine weather. By considering geographical characteristics, the accuracy of anomaly detection in specific areas is improved. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input weather data for a specific area into a generating AI, which can then analyze the data to detect anomalies.

[0055] The detection unit can improve the accuracy of detection by referring to relevant technical literature when detection occurs. For example, the detection unit can improve the anomaly detection algorithm by referring to the latest technical literature. The detection unit can also verify anomaly detection methods by referring to past technical literature. The detection unit can also introduce new methods to improve the accuracy of anomaly detection based on relevant technical literature. As a result, the accuracy of detection is improved by referring to relevant technical literature. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data from technical literature into a generating AI, and the generating AI can analyze the data to improve detection accuracy.

[0056] The charging management unit can select the optimal charging method by referring to past charging history during charging management. For example, the charging management unit selects the optimal charging method based on past charging history. The charging management unit can also analyze past charging history and identify areas for improvement in the charging method. The charging management unit can also optimize the charging method by referring to past charging history. In this way, the optimal charging method can be selected by referring to past charging history. Some or all of the above processes in the charging management unit may be performed using AI, for example, or without using AI. For example, the charging management unit can input past charging history into a generating AI, and the generating AI can analyze the data and select the optimal charging method.

[0057] The charging management unit can customize the charging method for specific disaster conditions during charging management. For example, if a typhoon is approaching, the charging management unit may suspend charging to ensure safety. If heavy rain is predicted, the charging management unit may also temporarily suspend charging to prioritize equipment protection. If extreme weather occurs, the charging management unit may also customize the charging method to ensure safety. This improves safety by customizing the charging method for specific disaster conditions. Some or all of the above processes in the charging management unit may be performed using AI, for example, or not using AI. For example, the charging management unit may input data on specific disaster conditions into a generating AI, which can then analyze the data to customize the charging method.

[0058] The charging management unit can prioritize charging in specific areas, taking geographical characteristics into account during charging management. For example, the charging management unit can prioritize charging in mountainous areas to ensure energy supply in mountainous regions. It can also prioritize charging in urban areas to address the unique energy demands of cities. Furthermore, it can prioritize charging in coastal areas to address marine weather conditions. This improves the accuracy of charging management in specific areas by considering geographical characteristics. Some or all of the above processing in the charging management unit may be performed using AI, for example, or without AI. For example, the charging management unit can input data for a specific area into a generating AI, which can then analyze the data to determine a charging management method.

[0059] The charging management unit can improve the accuracy of charging management by referring to relevant technical literature during charging management. For example, the charging management unit can improve the charging management algorithm by referring to the latest technical literature. The charging management unit can also verify charging management methods by referring to past technical literature. The charging management unit can also introduce new methods to improve the accuracy of charging management based on relevant technical literature. As a result, the accuracy of charging management is improved by referring to relevant technical literature. Some or all of the above processes in the charging management unit may be performed using AI, for example, or without AI. For example, the charging management unit can input data from technical literature into a generating AI, and the generating AI can analyze the data to improve the accuracy of charging management.

[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] Solar power generation systems can improve prediction accuracy by referring to past power generation data when forecasting power generation. For example, power generation can be predicted by combining past power generation data with current weather data. Past abnormal power generation data can also be analyzed and compared with current data to predict the occurrence of abnormal power generation. Long-term power generation forecasts can also be made by analyzing current data based on past power generation patterns. This improves the accuracy of power generation forecasts by comparing past and current power generation data. Some or all of the above processes in power generation forecasting may be performed using, for example, generative AI, or without generative AI. For example, power generation forecasting can be performed by inputting past and current power generation data into a generative AI, which then analyzes the data and predicts power generation.

[0062] Solar power generation systems can apply algorithms to improve the accuracy of predictions for specific weather conditions when forecasting power generation. For example, algorithms can be applied to improve the accuracy of typhoon predictions. Algorithms can also be applied to improve the accuracy of heavy rain predictions. Algorithms can also be applied to improve the accuracy of extreme weather predictions. This improves the accuracy of predictions for specific weather conditions. Some or all of the above processes in power generation forecasting may be performed using, for example, generative AI, or without generative AI. For example, power generation forecasting can be performed by inputting data on specific weather conditions into a generative AI, which then analyzes the data to improve prediction accuracy.

[0063] Solar power generation systems can prioritize predicting power generation in specific regions by considering geographical characteristics when forecasting power generation. For example, they can prioritize predicting power generation in mountainous areas to improve power generation efficiency in mountainous regions. They can also prioritize predicting power generation in urban areas to address weather phenomena specific to cities. They can also prioritize predicting power generation in coastal areas to address marine weather. In this way, considering geographical characteristics improves the accuracy of power generation forecasts for specific regions. Some or all of the above processing in power generation forecasting may be performed using, for example, generative AI, or without generative AI. For example, power generation forecasting can be performed by inputting weather data for a specific region into a generative AI, which then analyzes the data and predicts power generation.

[0064] Solar power generation systems can improve the accuracy of their predictions by referring to relevant scientific literature when forecasting power generation. For example, they can improve their prediction algorithms by referring to the latest scientific literature. They can also verify prediction results by referring to past scientific literature. They can also introduce new methods to improve prediction accuracy based on relevant scientific literature. In this way, the accuracy of predictions is improved by referring to relevant scientific literature. Some or all of the above processes in power generation forecasting may be performed using, for example, generative AI, or not using generative AI. For example, power generation forecasting can be done by inputting data from scientific literature into a generative AI, which then analyzes the data to improve prediction accuracy.

[0065] Solar power generation systems can analyze information from social media and collect relevant weather data when predicting power generation. For example, they can analyze posts on social media to supplement real-time weather information. They can also collect posts on social media about extreme weather and compare them with weather data. They can also collect regional weather information from social media to improve data accuracy. This allows real-time weather information to be supplemented by utilizing information from social media. Some or all of the above processes in predicting power generation may be performed using AI, for example, or not using AI. For example, power generation prediction can be performed by inputting information from social media into a generating AI, which then analyzes the information to supplement weather data.

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

[0067] Step 1: The collection unit collects weather data. The collection unit can collect weather data such as temperature, humidity, wind speed, and precipitation. The collection unit can also collect historical weather data and real-time weather data. For example, the collection unit can obtain historical weather data from a database and real-time weather data from sensors. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts future weather patterns. The analysis unit can analyze weather data using machine learning algorithms and predict future weather patterns. The analysis unit can also analyze historical weather data in combination with real-time weather data. For example, the analysis unit can improve prediction accuracy by supplementing real-time data with historical weather data. Step 3: The control unit optimally adjusts the orientation, angle, and area of ​​the solar panels based on the weather patterns predicted by the analysis unit. The control unit controls the motors for adjusting the orientation of the solar panels and adjusts them to track the sun at the optimal angle. The control unit can also control the sliding mechanism for adjusting the area of ​​the solar panels. For example, the control unit operates the sliding mechanism to maximize the area of ​​the solar panels. Step 4: The detection unit analyzes images of the solar panels in real time and detects abnormalities. The detection unit can analyze images of the solar panels using AI image analysis technology and detect abnormalities. The detection unit can also detect abnormalities such as snowfall or damage. For example, if snowfall is detected, the detection unit will position the solar panels vertically to prevent snow accumulation. Step 5: The charging management unit charges EVs using electricity generated by solar power and manages power supply to disaster-stricken areas and base stations in the event of a disaster. The charging management unit manages facilities that charge EVs using electricity generated by solar power. The charging management unit can also manage power supply to disaster-stricken areas and base stations in the event of a disaster. For example, in the event of a disaster such as a typhoon or earthquake, the charging management unit will use EVs to supply electricity to the affected area.

[0068] (Example of form 2) The solar power generation system according to an embodiment of the present invention is a system that automatically optimizes the orientation, angle, and area of ​​a slide-adjustable solar panel using weather pattern learning and real-time AI image analysis. This system aims to improve power generation prediction and energy efficiency, thereby reducing electricity costs. It also aims to reduce the environmental burden by detecting anomalies caused by typhoons and snowfall, and reducing damage to and disposal of solar panels. Furthermore, by using it in conjunction with EV charging facilities, it enables power supply to disaster-stricken areas and base stations in the event of a disaster. For example, the solar power generation system learns weather patterns. The AI ​​learns past weather data and predicts future weather patterns. Based on this prediction, it optimally adjusts the orientation, angle, and area of ​​the solar panel. For example, if sunny weather is predicted, the solar panel is adjusted to track the sun at the optimal angle. On the other hand, if a typhoon or strong winds are predicted, the solar panel is positioned horizontally to minimize the effects of the wind. Next, the solar power generation system performs real-time AI image analysis. The AI ​​analyzes images of the solar panel in real time and detects anomalies. For example, if snowfall is detected, the solar panels are positioned vertically to prevent snow accumulation. Also, if an anomaly is detected, the system automatically issues a warning and takes necessary measures. Furthermore, the solar power generation system is integrated with EV charging facilities. This system utilizes solar power to charge EVs. This enables power supply to disaster-stricken areas and base stations during emergencies. For example, in the event of a typhoon or earthquake, EVs can be used to supply power to the affected areas. This allows the solar power generation system to improve predictability of power generation and energy efficiency, resulting in reduced electricity costs. Additionally, by detecting anomalies caused by typhoons and snowfall, damage to solar panels and disposal are reduced, minimizing the environmental burden. Furthermore, by integrating with EV charging facilities, power supply to disaster-stricken areas and base stations during emergencies is possible, improving the stability of regional energy supply. This enables the solar power generation system to improve predictability of power generation and energy efficiency, resulting in reduced electricity costs. Additionally, by detecting anomalies caused by typhoons and snowfall, damage to solar panels and disposal are reduced, minimizing the environmental burden.Furthermore, by using EV charging facilities in conjunction with this system, it becomes possible to supply power to disaster-stricken areas and base stations during emergencies, thereby improving the stability of regional energy supply.

[0069] The photovoltaic power generation system according to this embodiment comprises a data collection unit, an analysis unit, a control unit, a detection unit, and a charge management unit. The data collection unit collects weather data. The data collection unit can collect weather data such as temperature, humidity, wind speed, and precipitation. The data collection unit can also collect historical and real-time weather data. For example, the data collection unit can obtain historical weather data from a database and real-time weather data from sensors. The analysis unit analyzes the data collected by the data collection unit and predicts future weather patterns. The analysis unit can analyze weather data using, for example, a machine learning algorithm to predict future weather patterns. The analysis unit can also analyze historical and real-time weather data in combination. For example, the analysis unit can supplement real-time data with historical weather data to improve prediction accuracy. The control unit optimally adjusts the orientation, angle, and area of ​​the solar panels based on the weather patterns predicted by the analysis unit. The control unit, for example, controls a motor for adjusting the orientation of the solar panels and adjusts it to track the sun at the optimal angle. The control unit can also control a sliding mechanism for adjusting the area of ​​the solar panels. For example, the control unit operates a sliding mechanism to maximize the area of ​​the solar panels. The detection unit analyzes images of the solar panels in real time and detects abnormalities. The detection unit can analyze images of the solar panels using, for example, AI image analysis technology to detect abnormalities. The detection unit can also detect abnormalities such as snowfall or damage. For example, if snowfall is detected, the detection unit will position the solar panels vertically to prevent snow accumulation. The charging management unit charges EVs using electricity generated by solar power and manages power supply to disaster-stricken areas and base stations in the event of a disaster. The charging management unit manages facilities that charge EVs using electricity generated by solar power. The charging management unit can also manage power supply to disaster-stricken areas and base stations in the event of a disaster. For example, if a disaster such as a typhoon or earthquake occurs, the charging management unit will use EVs to supply power to the disaster-stricken area. As a result, the solar power generation system according to this embodiment can efficiently collect, analyze, control, detect abnormalities, and manage charging using weather data.

[0070] The data collection unit collects meteorological data. For example, it can collect data such as temperature, humidity, wind speed, and precipitation. Specifically, the unit acquires this data in real time using multiple sensors. Temperature sensors accurately measure ambient temperature, humidity sensors detect moisture content in the air, wind speed sensors measure wind strength and direction, and precipitation sensors measure the amount of rain or snow. These sensors are installed around the solar panels to constantly collect the latest meteorological data. The data collection unit can also retrieve historical meteorological data from a database. Historical meteorological data is useful for analyzing long-term weather patterns and is important for improving the accuracy of future predictions. For example, analyzing weather data from the past few years can reveal seasonal weather patterns and the frequency of extreme weather events. Furthermore, the data collection unit can also obtain real-time weather information from the Japan Meteorological Agency and other weather data providers via the internet. This allows the data collection unit to collect diverse meteorological data from a wide range of data sources, improving the overall accuracy and reliability of the system.

[0071] The analysis unit analyzes data collected by the data collection unit and predicts future weather patterns. For example, the analysis unit can use machine learning algorithms to analyze weather data and predict future weather patterns. Specifically, the analysis unit combines historical and real-time weather data for analysis. Machine learning algorithms have the ability to learn from large amounts of data and predict variations in weather patterns. For example, they can improve prediction accuracy by supplementing real-time data with historical weather data. The analysis unit uses data such as temperature, humidity, wind speed, and precipitation as input to build a model that predicts future weather conditions. This model can learn from historical data and predict future weather patterns with high accuracy. Furthermore, the analysis unit can also assess the risk of extreme weather events. For example, based on historical data, the analysis unit calculates the probability of extreme weather events occurring under specific conditions and performs a risk assessment. This allows the analysis unit to predict future weather patterns and provide information to optimize the overall system operation.

[0072] The control unit optimally adjusts the orientation, angle, and area of ​​the solar panels based on the weather patterns predicted by the analysis unit. Specifically, the control unit controls the motors for adjusting the orientation of the solar panels, adjusting them to track the sun at the optimal angle. This ensures that the solar panels always receive the maximum amount of sunlight, improving power generation efficiency. The control unit can also control the sliding mechanism for adjusting the area of ​​the solar panels. For example, the control unit operates the sliding mechanism to maximize the area of ​​the solar panels, thereby maximizing the power generation capacity of the solar panels. Furthermore, the control unit can also optimize the placement of the solar panels according to weather conditions. For example, if strong winds are predicted, the control unit adjusts the solar panels to an angle less susceptible to wind damage to prevent panel breakage. Also, if snowfall is predicted, the control unit positions the solar panels vertically to prevent snow accumulation. In this way, the control unit can optimize the placement of the solar panels according to weather conditions, improving the overall efficiency and safety of the system.

[0073] The detection unit analyzes images of solar panels in real time to detect abnormalities. Specifically, the detection unit can analyze images of solar panels using AI image analysis technology to detect abnormalities. For example, the detection unit acquires images of solar panels using a camera and analyzes the images using an AI algorithm. The AI ​​algorithm detects whether there are any abnormalities on the panel surface and issues an alert if an abnormality is detected. The detection unit can also detect abnormalities such as snowfall and damage. For example, if snowfall is detected, the detection unit will position the solar panels vertically to prevent snow accumulation. Also, if cracks or dirt are detected on the panel surface, the detection unit will notify that maintenance is required. In this way, the detection unit can constantly monitor the condition of the solar panels and respond quickly when an abnormality occurs. Furthermore, the detection unit can also suggest appropriate countermeasures depending on the type and severity of the abnormality. For example, it may recommend cleaning for minor dirt and recommend panel replacement for serious damage. In this way, the detection unit can detect abnormalities in solar panels early and take appropriate measures, thereby improving the reliability and efficiency of the entire system.

[0074] The Charging Management Unit uses solar power to charge EVs and manages power supply to disaster-stricken areas and base stations during emergencies. Specifically, the Charging Management Unit manages facilities that charge EVs using solar power. The Charging Management Unit monitors EV charging stations and can grasp the progress of charging in real time. For example, the Charging Management Unit monitors the power supply status of each charging station and optimizes power distribution as needed. Furthermore, the Charging Management Unit can also manage power supply to disaster-stricken areas and base stations during emergencies. For example, in the event of a disaster such as a typhoon or earthquake, the Charging Management Unit uses EVs to supply power to the affected areas. The Charging Management Unit can grasp the power demand of the affected areas in real time and formulate an optimal power supply plan. This allows the Charging Management Unit to supply power quickly and efficiently even during disasters and support the recovery of the affected areas. In addition, the Charging Management Unit can set power supply priorities and supply power preferentially to important facilities and infrastructure. For example, it can supply power preferentially to important facilities such as hospitals and evacuation centers to ensure the safety and health of disaster victims. This allows the charging management unit to efficiently manage EV charging during normal times and to supply power quickly and appropriately during disasters, thereby improving the overall reliability and flexibility of the system.

[0075] The data collection unit can collect historical and real-time weather data. For example, the data collection unit can obtain historical weather data from a database and real-time weather data from sensors. The data collection unit can also improve prediction accuracy by supplementing real-time data with historical weather data. For example, the data collection unit can analyze historical weather data and combine it with real-time data to improve prediction accuracy. In this way, prediction accuracy is improved by collecting historical and real-time weather data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input historical weather data into AI, and the AI ​​can analyze the data to improve prediction accuracy.

[0076] The analysis unit can analyze collected weather data and predict future weather patterns. For example, the analysis unit can use machine learning algorithms to analyze weather data and predict future weather patterns. The analysis unit can also combine and analyze historical weather data with real-time weather data. For example, the analysis unit can use historical weather data to complement real-time data and improve prediction accuracy. This makes it possible to predict future weather patterns through the analysis of weather data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input collected weather data into a generative AI, which can then analyze the data and predict future weather patterns.

[0077] The control unit can optimally adjust the orientation, angle, and area of ​​the solar panels based on predicted weather patterns. For example, the control unit controls a motor for adjusting the orientation of the solar panels, adjusting it to track the sun at the optimal angle. The control unit can also control a sliding mechanism for adjusting the area of ​​the solar panels. For example, the control unit operates the sliding mechanism to maximize the area of ​​the solar panels. This improves power generation efficiency by optimally adjusting the solar panels based on weather patterns. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the control unit can input a predicted weather pattern into a generative AI, and the generative AI can output the optimal adjustment method for the solar panels.

[0078] The detection unit can analyze images of solar panels in real time and detect abnormalities. For example, the detection unit can analyze images of solar panels using AI image analysis technology to detect abnormalities. The detection unit can also detect abnormalities such as snowfall or damage. For example, if snowfall is detected, the detection unit will position the solar panels vertically to prevent snow accumulation. This enables a rapid response by detecting abnormalities in real time. Some or all of the above processing in the detection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the detection unit can input image data of the solar panels acquired in real time into a generating AI, which can then detect abnormalities.

[0079] The charging management unit can charge EVs using electricity generated by solar power and manage power supply to disaster-stricken areas and base stations in the event of a disaster. For example, the charging management unit manages facilities that charge EVs using electricity generated by solar power. The charging management unit can also manage power supply to disaster-stricken areas and base stations in the event of a disaster. For example, in the event of a disaster such as a typhoon or earthquake, the charging management unit can use EVs to supply electricity to the disaster-stricken area. This makes it possible to supply power to disaster-stricken areas and base stations even in the event of a disaster. Some or all of the above processing in the charging management unit may be performed using, for example, a generating AI, or it may be performed without using a generating AI. For example, the charging management unit can input the status of electricity supply from solar power generation into a generating AI, and the generating AI can output the optimal charging management method.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of weather data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of weather data collection to alleviate the system load. If the user is relaxed, the data collection unit can also increase the frequency of weather data collection to provide more detailed data. If the user is in a hurry, the data collection unit can prioritize the collection of only important weather data. This reduces the system load by adjusting the timing of weather data collection according to the user's emotions. 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the timing of weather data collection.

[0081] The data collection unit can collect more accurate data by combining historical and real-time weather data. For example, the data collection unit can improve prediction accuracy by supplementing real-time data with historical weather data. The data collection unit can also detect anomalies by comparing real-time weather data with historical data. The data collection unit can also optimize the timing of real-time data collection by referring to past weather patterns. This improves prediction accuracy by combining historical and real-time data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input historical and real-time weather data into a generating AI, which can then analyze the data to improve prediction accuracy.

[0082] The data collection unit can prioritize the collection of data for specific weather conditions when collecting weather data. For example, when a typhoon is approaching, the data collection unit will prioritize the collection of wind speed and precipitation data. When heavy rain is predicted, the data collection unit can also prioritize the collection of precipitation and river water level data. When extreme weather is occurring, the data collection unit can also concentrate on collecting weather data for that area. This improves the accuracy of extreme weather predictions by prioritizing the collection of data for specific weather conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data related to specific weather conditions into a generating AI, which can then analyze the data to improve the accuracy of extreme weather predictions.

[0083] The data collection unit can estimate the user's emotions and determine the priority of weather data to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting safety-related weather data. If the user is excited, the data collection unit may also prioritize collecting data on interesting weather phenomena. If the user is relaxed, the data collection unit may also collect general weather data in a balanced manner. This allows for the priority collection of important data by determining the priority of weather data according to the user's emotions. 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 data collection unit may be performed using AI or not. For example, the data collection unit can input the user's emotion data into a generative AI, which can then determine the priority of weather data.

[0084] The data collection unit can prioritize the collection of data from specific regions, taking geographical characteristics into consideration when collecting meteorological data. For example, the data collection unit can prioritize the collection of meteorological data from mountainous areas to improve the accuracy of weather forecasts in mountainous regions. The data collection unit can also prioritize the collection of meteorological data from urban areas to analyze meteorological phenomena specific to cities. The data collection unit can also prioritize the collection of meteorological data from coastal areas to improve the accuracy of ocean weather forecasts. In this way, the accuracy of weather forecasts for specific regions is improved by taking geographical characteristics into consideration. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input meteorological data from a specific region into a generating AI, which can then analyze the data to improve the accuracy of weather forecasts.

[0085] The data collection unit can analyze information from social media and collect relevant weather data when collecting weather data. For example, the data collection unit can analyze posts on social media to supplement real-time weather information. The data collection unit can also collect posts on social media about extreme weather and compare them with weather data. The data collection unit can also collect regional weather information from social media to improve data accuracy. This allows real-time weather information to be supplemented by utilizing information from social media. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information from social media into a generating AI, which can then analyze the information to supplement weather data.

[0086] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can adjust the analysis algorithm to prioritize safety. If the user is relaxed, the analysis unit can also adjust the analysis algorithm to perform detailed data analysis. If the user is in a hurry, the analysis unit can also adjust the analysis algorithm to produce results quickly. By adjusting the analysis algorithm according to the user's emotions, the accuracy of the analysis results is improved. Emotion estimation is achieved using an emotion estimation function, for example, using 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the analysis algorithm.

[0087] The analysis unit can predict future weather patterns by comparing past weather patterns with current data during analysis. For example, the analysis unit predicts future weather patterns by comparing current data with past weather data. The analysis unit can also predict the occurrence of extreme weather events by analyzing past extreme weather data and comparing it with current data. The analysis unit can also perform long-term weather forecasts by analyzing current data with reference to past weather patterns. This improves the accuracy of future weather pattern predictions by comparing past weather patterns with current data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input past weather data and current data into a generative AI, which can analyze the data and predict future weather patterns.

[0088] The analysis unit can apply algorithms to improve prediction accuracy for specific weather conditions during analysis. For example, the analysis unit can apply algorithms to improve typhoon prediction accuracy. The analysis unit can also apply algorithms to improve heavy rainfall prediction accuracy. The analysis unit can also apply algorithms to improve extreme weather prediction accuracy. This improves prediction accuracy for specific weather conditions. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on specific weather conditions into a generative AI, and the generative AI can analyze the data to improve prediction accuracy.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. This improves visibility by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI, and the generative AI can adjust the display method of the analysis results.

[0090] The analysis unit can prioritize predicting weather patterns for specific regions by considering geographical characteristics during analysis. For example, the analysis unit can prioritize predicting weather patterns in mountainous areas to improve the accuracy of weather forecasts in mountainous regions. The analysis unit can also prioritize predicting weather patterns in urban areas to analyze weather phenomena specific to cities. The analysis unit can also prioritize predicting weather patterns in coastal areas to improve the accuracy of ocean weather forecasts. In this way, the accuracy of weather forecasts for specific regions is improved by considering geographical characteristics. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input weather data for a specific region into a generating AI, and the generating AI can analyze the data to improve the accuracy of weather forecasts.

[0091] The analysis unit can improve the accuracy of its analysis by referring to relevant scientific literature during the analysis process. For example, the analysis unit can improve its analysis algorithm by referring to the latest scientific literature. The analysis unit can also verify the analysis results by referring to past scientific literature. The analysis unit can also introduce new methods to improve the accuracy of the analysis based on relevant scientific literature. As a result, the accuracy of the analysis is improved by referring to relevant scientific literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input scientific literature data into a generating AI, which can then analyze the data to improve the accuracy of the analysis.

[0092] The control unit can estimate the user's emotions and change the adjustment method of the solar panels based on the estimated user emotions. For example, if the user is feeling anxious, the control unit can change the adjustment of the solar panels to prioritize safety. If the user is relaxed, the control unit can also change the adjustment of the solar panels to prioritize efficiency. If the user is in a hurry, the control unit can also change the adjustment of the solar panels to perform quickly. This allows for more appropriate adjustments by changing the adjustment method of the solar panels according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not using AI. For example, the control unit can input user emotion data into a generative AI, and the generative AI can change the adjustment method of the solar panels.

[0093] The control unit can select the optimal adjustment method by referring to past adjustment history during control. For example, the control unit selects the optimal adjustment method based on past adjustment history. The control unit can also analyze past adjustment history and identify areas for improvement in the adjustment method. The control unit can also optimize the adjustment method by referring to past adjustment history. In this way, the optimal adjustment method can be selected by referring to past adjustment history. Some or all of the above processes in the control unit may be performed using AI, for example, or without using AI. For example, the control unit can input past adjustment history into a generating AI, and the generating AI can analyze the data and select the optimal adjustment method.

[0094] The control unit can customize the adjustment method for specific weather conditions during control. For example, the control unit can customize the adjustment method to arrange the solar panels horizontally when a typhoon is approaching. The control unit can also customize the adjustment method to arrange the solar panels vertically when heavy rain is predicted. The control unit can also customize the adjustment method for the solar panels when extreme weather is occurring. By customizing the adjustment method for specific weather conditions, the adjustment accuracy is improved. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input data on specific weather conditions into a generating AI, which can then analyze the data and customize the adjustment method.

[0095] The control unit can estimate the user's emotions and determine the frequency of adjustments to the solar panels based on the estimated emotions. For example, if the user is feeling anxious, the control unit can reduce the frequency of adjustments to the solar panels, thereby reducing the system load. If the user is relaxed, the control unit can also increase the frequency of adjustments to improve efficiency. If the user is in a hurry, the control unit can prioritize only important adjustments. This reduces the system load by determining the frequency of adjustments to the solar panels according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 control unit may be performed using AI or not using AI. For example, the control unit can input user emotion data into a generative AI, which can then determine the frequency of adjustments to the solar panels.

[0096] The control unit can prioritize adjusting solar panels in specific areas, taking geographical characteristics into account during control. For example, the control unit can prioritize adjusting solar panels in mountainous areas to improve power generation efficiency in mountainous regions. The control unit can also prioritize adjusting solar panels in urban areas to address weather phenomena specific to cities. The control unit can also prioritize adjusting solar panels in coastal areas to address marine weather. This improves the accuracy of adjusting solar panels in specific areas by taking geographical characteristics into account. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input weather data for a specific area into a generating AI, which can then analyze the data to determine how to adjust the solar panels.

[0097] The control unit can improve the accuracy of the adjustment by referring to relevant technical literature during control. For example, the control unit can improve the adjustment algorithm by referring to the latest technical literature. The control unit can also verify adjustment methods by referring to past technical literature. The control unit can also introduce new methods to improve the accuracy of the adjustment based on relevant technical literature. Thus, the accuracy of the adjustment is improved by referring to relevant technical literature. Some or all of the above processes in the control unit may be performed using AI, for example, or not using AI. For example, the control unit can input data from technical literature into a generating AI, and the generating AI can analyze the data to improve the adjustment accuracy.

[0098] The detection unit can estimate the user's emotions and adjust the anomaly detection criteria based on the estimated user emotions. For example, if the user is feeling anxious, the detection unit may set the anomaly detection criteria more strictly. If the user is relaxed, the detection unit may also set the anomaly detection criteria more loosely. If the user is in a hurry, the detection unit may also prioritize detecting only important anomalies. This improves the accuracy of anomaly detection by adjusting the anomaly detection criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 detection unit may be performed using AI, for example, or not using AI. For example, the detection unit can input user emotion data into a generative AI, which can then adjust the anomaly detection criteria.

[0099] The detection unit can improve the accuracy of anomaly detection by referring to past anomaly data when an anomaly is detected. For example, the detection unit can improve the anomaly detection algorithm based on past anomaly data. The detection unit can also analyze past anomaly data and identify anomaly detection patterns. The detection unit can also improve the accuracy of anomaly detection by referring to past anomaly data. As a result, the accuracy of anomaly detection is improved by referring to past anomaly data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input past anomaly data into a generating AI, and the generating AI can analyze the data to improve the accuracy of anomaly detection.

[0100] The detection unit can customize its detection method for specific anomaly conditions when detection occurs. For example, when a typhoon is approaching, the detection unit will prioritize detecting anomalies in wind speed and precipitation. When heavy rain is predicted, the detection unit can also prioritize detecting anomalies in precipitation and river water levels. When extreme weather is occurring, the detection unit can also concentrate on detecting anomalies in that area. By customizing the detection method for specific anomaly conditions, the accuracy of anomaly detection is improved. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data related to specific anomaly conditions into a generating AI, which can then analyze the data and customize the detection method.

[0101] The detection unit can estimate the user's emotions and adjust the order in which anomaly detection results are displayed based on the estimated user emotions. For example, if the user is feeling anxious, the detection unit will display important anomalies first. If the user is relaxed, the detection unit can also sequentially display detailed anomaly information. If the user is in a hurry, the detection unit can also prioritize displaying concise anomaly information. This improves readability by adjusting the order in which anomaly detection results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into the generative AI, which can then adjust the order in which anomaly detection results are displayed.

[0102] The detection unit can prioritize detecting anomalies in specific areas, taking geographical characteristics into consideration. For example, the detection unit can prioritize detecting anomalies in mountainous areas to ensure safety in mountainous regions. The detection unit can also prioritize detecting anomalies in urban areas to respond to anomalies specific to cities. The detection unit can also prioritize detecting anomalies in coastal areas to respond to anomalies in marine weather. By considering geographical characteristics, the accuracy of anomaly detection in specific areas is improved. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input weather data for a specific area into a generating AI, which can then analyze the data to detect anomalies.

[0103] The detection unit can improve the accuracy of detection by referring to relevant technical literature when detection occurs. For example, the detection unit can improve the anomaly detection algorithm by referring to the latest technical literature. The detection unit can also verify anomaly detection methods by referring to past technical literature. The detection unit can also introduce new methods to improve the accuracy of anomaly detection based on relevant technical literature. As a result, the accuracy of detection is improved by referring to relevant technical literature. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data from technical literature into a generating AI, and the generating AI can analyze the data to improve detection accuracy.

[0104] The charging management unit can estimate the user's emotions and adjust the charging management method based on the estimated emotions. For example, if the user is feeling anxious, the charging management unit may adjust the charging management to prioritize safety. If the user is relaxed, the charging management unit may also adjust the charging management to prioritize efficiency. If the user is in a hurry, the charging management unit may also adjust the charging to proceed quickly. This allows for more appropriate charging management by adjusting the charging management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the charging management unit may be performed using AI, for example, or not using AI. For example, the charging management unit can input user emotion data into a generative AI, which can then adjust the charging management method.

[0105] The charging management unit can select the optimal charging method by referring to past charging history during charging management. For example, the charging management unit selects the optimal charging method based on past charging history. The charging management unit can also analyze past charging history and identify areas for improvement in the charging method. The charging management unit can also optimize the charging method by referring to past charging history. In this way, the optimal charging method can be selected by referring to past charging history. Some or all of the above processes in the charging management unit may be performed using AI, for example, or without using AI. For example, the charging management unit can input past charging history into a generating AI, and the generating AI can analyze the data and select the optimal charging method.

[0106] The charging management unit can customize the charging method for specific disaster conditions during charging management. For example, if a typhoon is approaching, the charging management unit may suspend charging to ensure safety. If heavy rain is predicted, the charging management unit may also temporarily suspend charging to prioritize equipment protection. If extreme weather occurs, the charging management unit may also customize the charging method to ensure safety. This improves safety by customizing the charging method for specific disaster conditions. Some or all of the above processes in the charging management unit may be performed using AI, for example, or not using AI. For example, the charging management unit may input data on specific disaster conditions into a generating AI, which can then analyze the data to customize the charging method.

[0107] The charging management unit can estimate the user's emotions and determine the priority of charging management based on the estimated emotions. For example, if the user is feeling anxious, the charging management unit will prioritize important charging. If the user is relaxed, the charging management unit can also balance overall charging. If the user is in a hurry, the charging management unit can also perform rapid charging. In this way, important charging can be prioritized by determining the priority of charging management according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the charging management unit may be performed using AI or not using AI. For example, the charging management unit can input user emotion data into a generative AI, and the generative AI can determine the priority of charging management.

[0108] The charging management unit can prioritize charging in specific areas, taking geographical characteristics into account during charging management. For example, the charging management unit can prioritize charging in mountainous areas to ensure energy supply in mountainous regions. It can also prioritize charging in urban areas to address the unique energy demands of cities. Furthermore, it can prioritize charging in coastal areas to address marine weather conditions. This improves the accuracy of charging management in specific areas by considering geographical characteristics. Some or all of the above processing in the charging management unit may be performed using AI, for example, or without AI. For example, the charging management unit can input data for a specific area into a generating AI, which can then analyze the data to determine a charging management method.

[0109] The charging management unit can improve the accuracy of charging management by referring to relevant technical literature during charging management. For example, the charging management unit can improve the charging management algorithm by referring to the latest technical literature. The charging management unit can also verify charging management methods by referring to past technical literature. The charging management unit can also introduce new methods to improve the accuracy of charging management based on relevant technical literature. As a result, the accuracy of charging management is improved by referring to relevant technical literature. Some or all of the above processes in the charging management unit may be performed using AI, for example, or without AI. For example, the charging management unit can input data from technical literature into a generating AI, and the generating AI can analyze the data to improve the accuracy of charging management.

[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] A solar power generation system can estimate the user's emotions and adjust its power generation forecast based on those emotions. For example, if the user is stressed, the power generation forecast can be conservative, prioritizing system stability. If the user is relaxed, the power generation forecast can be more aggressive, maximizing energy efficiency. If the user is in a hurry, the forecast results can be provided quickly. This improves the system's flexibility by adjusting the power generation forecast according to the user's emotions. 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-described processes in power generation forecasting may be performed using AI or not. For example, power generation forecasting can be performed by inputting user emotion data into a generative AI, which can then adjust the forecasting method.

[0112] Solar power generation systems can improve prediction accuracy by referring to past power generation data when forecasting power generation. For example, power generation can be predicted by combining past power generation data with current weather data. Past abnormal power generation data can also be analyzed and compared with current data to predict the occurrence of abnormal power generation. Long-term power generation forecasts can also be made by analyzing current data based on past power generation patterns. This improves the accuracy of power generation forecasts by comparing past and current power generation data. Some or all of the above processes in power generation forecasting may be performed using, for example, generative AI, or without generative AI. For example, power generation forecasting can be performed by inputting past and current power generation data into a generative AI, which then analyzes the data and predicts power generation.

[0113] A solar power generation system can estimate the user's emotions and adjust the display method of the power generation forecast results based on the estimated emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that gets straight to the point can be provided. This improves visibility by adjusting the display method of the power generation forecast results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in displaying the power generation forecast results may be performed using AI, for example, or without AI. For example, the display of the power generation forecast results can be performed by inputting user emotion data into a generative AI, which can then adjust the display method.

[0114] Solar power generation systems can apply algorithms to improve the accuracy of predictions for specific weather conditions when forecasting power generation. For example, algorithms can be applied to improve the accuracy of typhoon predictions. Algorithms can also be applied to improve the accuracy of heavy rain predictions. Algorithms can also be applied to improve the accuracy of extreme weather predictions. This improves the accuracy of predictions for specific weather conditions. Some or all of the above processes in power generation forecasting may be performed using, for example, generative AI, or without generative AI. For example, power generation forecasting can be performed by inputting data on specific weather conditions into a generative AI, which then analyzes the data to improve prediction accuracy.

[0115] A solar power generation system can estimate the user's emotions and adjust the frequency of power generation predictions based on those emotions. For example, if the user is feeling anxious, the frequency of power generation predictions can be reduced to lessen the system load. If the user is relaxed, the frequency of power generation predictions can be increased to provide more detailed data. If the user is in a hurry, only important predictions can be prioritized. This reduces the system load by adjusting the frequency of power generation predictions according to the user's emotions. 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 power generation prediction frequency may be performed using AI or not. For example, the power generation prediction frequency can be adjusted by inputting user emotion data into a generative AI, which can then adjust the prediction frequency.

[0116] Solar power generation systems can prioritize predicting power generation in specific regions by considering geographical characteristics when forecasting power generation. For example, they can prioritize predicting power generation in mountainous areas to improve power generation efficiency in mountainous regions. They can also prioritize predicting power generation in urban areas to address weather phenomena specific to cities. They can also prioritize predicting power generation in coastal areas to address marine weather. In this way, considering geographical characteristics improves the accuracy of power generation forecasts for specific regions. Some or all of the above processing in power generation forecasting may be performed using, for example, generative AI, or without generative AI. For example, power generation forecasting can be performed by inputting weather data for a specific region into a generative AI, which then analyzes the data and predicts power generation.

[0117] A solar power generation system can estimate the user's emotions and adjust its power generation prediction algorithm based on those emotions. For example, if the user is feeling anxious, the prediction algorithm can be adjusted to prioritize safety. If the user is relaxed, the prediction algorithm can be adjusted for detailed data analysis. If the user is in a hurry, the prediction algorithm can be adjusted to provide results quickly. By adjusting the prediction algorithm according to the user's emotions, the accuracy of the prediction results is improved. 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 power generation prediction algorithm may be performed using AI or not. For example, the power generation prediction algorithm can input user emotion data into a generative AI, which can then adjust the algorithm.

[0118] Solar power generation systems can improve the accuracy of their predictions by referring to relevant scientific literature when forecasting power generation. For example, they can improve their prediction algorithms by referring to the latest scientific literature. They can also verify prediction results by referring to past scientific literature. They can also introduce new methods to improve prediction accuracy based on relevant scientific literature. In this way, the accuracy of predictions is improved by referring to relevant scientific literature. Some or all of the above processes in power generation forecasting may be performed using, for example, generative AI, or not using generative AI. For example, power generation forecasting can be done by inputting data from scientific literature into a generative AI, which then analyzes the data to improve prediction accuracy.

[0119] A solar power generation system can estimate the user's emotions and prioritize the predicted power generation results based on those emotions. For example, if the user is feeling anxious, safety-related prediction results can be displayed first. If the user is relaxed, a balanced display of general prediction results can be shown. If the user is in a hurry, only important prediction results can be displayed first. This allows for the priority of important information to be provided by prioritizing the predicted power generation results according to the user's emotions. 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 prioritizing the predicted power generation results may be performed using AI or not. For example, the priority of the predicted power generation results can be determined by inputting user emotion data into a generative AI, which then determines the priority.

[0120] Solar power generation systems can analyze information from social media and collect relevant weather data when predicting power generation. For example, they can analyze posts on social media to supplement real-time weather information. They can also collect posts on social media about extreme weather and compare them with weather data. They can also collect regional weather information from social media to improve data accuracy. This allows real-time weather information to be supplemented by utilizing information from social media. Some or all of the above processes in predicting power generation may be performed using AI, for example, or not using AI. For example, power generation prediction can be performed by inputting information from social media into a generating AI, which then analyzes the information to supplement weather data.

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

[0122] Step 1: The collection unit collects weather data. The collection unit can collect weather data such as temperature, humidity, wind speed, and precipitation. The collection unit can also collect historical weather data and real-time weather data. For example, the collection unit can obtain historical weather data from a database and real-time weather data from sensors. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts future weather patterns. The analysis unit can analyze weather data using machine learning algorithms and predict future weather patterns. The analysis unit can also analyze historical weather data in combination with real-time weather data. For example, the analysis unit can improve prediction accuracy by supplementing real-time data with historical weather data. Step 3: The control unit optimally adjusts the orientation, angle, and area of ​​the solar panels based on the weather patterns predicted by the analysis unit. The control unit controls the motors for adjusting the orientation of the solar panels and adjusts them to track the sun at the optimal angle. The control unit can also control the sliding mechanism for adjusting the area of ​​the solar panels. For example, the control unit operates the sliding mechanism to maximize the area of ​​the solar panels. Step 4: The detection unit analyzes images of the solar panels in real time and detects abnormalities. The detection unit can analyze images of the solar panels using AI image analysis technology and detect abnormalities. The detection unit can also detect abnormalities such as snowfall or damage. For example, if snowfall is detected, the detection unit will position the solar panels vertically to prevent snow accumulation. Step 5: The charging management unit charges EVs using electricity generated by solar power and manages power supply to disaster-stricken areas and base stations in the event of a disaster. The charging management unit manages facilities that charge EVs using electricity generated by solar power. The charging management unit can also manage power supply to disaster-stricken areas and base stations in the event of a disaster. For example, in the event of a disaster such as a typhoon or earthquake, the charging management unit will use EVs to supply electricity to the affected area.

[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, control unit, detection unit, and charging management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects weather data using the sensors of the smart device 14 or the database 24 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to predict future weather patterns. The control unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12 and optimally adjusts the orientation, angle, and area of ​​the solar panels. The detection unit is implemented by the camera 42 of the smart device 14 or the specific processing unit 290 of the data processing unit 12 and analyzes images of the solar panels in real time to detect abnormalities. The charging management unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12 and charges the EV using electricity generated by solar power, and manages power supply to disaster areas and base stations in the event of a disaster. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications 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 commands and other instructions from the user by receiving voice signals. 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, control unit, detection unit, and charging management unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects weather data using the sensors of the smart glasses 214 or the database 24 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and predicts future weather patterns. The control unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, which optimally adjusts the orientation, angle, and area of ​​the solar panels. The detection unit is implemented by the camera 42 of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, which analyzes images of the solar panels in real time and detects abnormalities. The charging management unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, which charges the EV using electricity generated by solar power and manages power supply to disaster areas and base stations in the event of a disaster. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications 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 commands and other instructions from the user by receiving voice signals. 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, control unit, detection unit, and charging management 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 weather data using the sensors of the headset terminal 314 and the database 24 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and predicts future weather patterns. The control unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, which optimally adjusts the orientation, angle, and area of ​​the solar panels. The detection unit is implemented by the camera 42 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, which analyzes images of the solar panels in real time and detects abnormalities. The charging management unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, which charges the EV using electricity generated by solar power and manages power supply to disaster areas and base stations in the event of a disaster. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications 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 commands and other instructions from the user by receiving voice signals. 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, control unit, detection unit, and charging management unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit collects weather data using the sensors of the robot 414 or the database 24 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and predicts future weather patterns. The control unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, which optimally adjusts the orientation, angle, and area of ​​the solar panels. The detection unit is implemented by the camera 42 of the robot 414 or the specific processing unit 290 of the data processing unit 12, which analyzes images of the solar panels in real time and detects abnormalities. The charging management unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, which charges the EV using electricity generated by solar power and manages power supply to disaster areas and base stations in the event of a disaster. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications 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) The collection unit collects weather data, An analysis unit analyzes the data collected by the aforementioned collection unit and predicts future weather patterns, A control unit that optimally adjusts the orientation, angle, and area of ​​the solar panels based on the weather patterns predicted by the analysis unit, A detection unit analyzes images of solar panels in real time to detect abnormalities, It includes a charging management unit that uses solar power to charge EVs and manages power supply to disaster-stricken areas and base stations in the event of a disaster. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect historical and real-time weather data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze collected weather data to predict future weather patterns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The control unit, Based on predicted weather patterns, the orientation, angle, and area of ​​the solar panels are optimally adjusted. The system described in Appendix 1, characterized by the features described herein. (Note 5) The detection unit, It analyzes images of solar panels in real time to detect anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned charging management unit, The system uses solar power to charge electric vehicles and manages power supply to disaster-stricken areas and base stations during emergencies. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of weather data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is By combining historical weather data with real-time weather data, more accurate data can be collected. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting weather data, prioritize the collection of data for specific weather conditions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of weather data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting weather data, prioritize the collection of data from specific regions, taking geographical characteristics into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting weather data, we analyze information from social media and collect relevant weather data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, past weather patterns are compared with current data to predict future weather patterns. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, algorithms are applied to improve prediction accuracy for specific weather conditions. 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 how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the system prioritizes predicting weather patterns for specific regions, taking geographical characteristics into account. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant scientific literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The control unit, The system estimates the user's emotions and modifies the adjustment method of the solar panels based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The control unit, During control, the optimal adjustment method is selected by referring to past adjustment history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The control unit, During control, customize the adjustment method for specific weather conditions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The control unit, The system estimates the user's emotions and determines the frequency of adjustments to the solar panels based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The control unit, During control, the system prioritizes adjusting solar panels in specific areas, taking geographical characteristics into account. The system described in Appendix 1, characterized by the features described herein. (Note 24) The control unit, During control, refer to relevant technical literature to improve the accuracy of adjustments. The system described in Appendix 1, characterized by the features described herein. (Note 25) The detection unit, The system estimates the user's emotions and adjusts the anomaly detection criteria based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The detection unit, When detecting an anomaly, past anomaly data is referenced to improve the accuracy of anomaly detection. The system described in Appendix 1, characterized by the features described herein. (Note 27) The detection unit, When detection occurs, customize the detection method for specific anomaly conditions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The detection unit, It estimates the user's emotions and adjusts the order in which anomaly detection results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The detection unit, During detection, the system prioritizes detecting anomalies in specific areas, taking geographical characteristics into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The detection unit, During detection, we improve detection accuracy by referring to relevant technical literature. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned charging management unit, It estimates the user's emotions and adjusts the charging management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned charging management unit, During charging management, the system selects the optimal charging method by referring to past charging history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned charging management unit, When managing charging, customize the charging method for specific disaster conditions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned charging management unit, It estimates the user's emotions and determines charging management priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned charging management unit, When managing charging, prioritize charging in specific areas, taking geographical characteristics into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned charging management unit, When managing charging, refer to relevant technical literature to improve the accuracy of charging management. 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. The collection unit collects weather data, An analysis unit analyzes the data collected by the aforementioned collection unit and predicts future weather patterns, A control unit that optimally adjusts the orientation, angle, and area of ​​the solar panels based on the weather patterns predicted by the analysis unit, A detection unit analyzes images of solar panels in real time to detect abnormalities, It includes a charging management unit that uses solar power to charge EVs and manages power supply to disaster-stricken areas and base stations in the event of a disaster. A system characterized by the following features.

2. The aforementioned collection unit is Collect historical and real-time weather data. The system according to feature 1.

3. The aforementioned analysis unit, We analyze collected weather data to predict future weather patterns. The system according to feature 1.

4. The control unit, Based on predicted weather patterns, the orientation, angle, and area of ​​the solar panels are optimally adjusted. The system according to feature 1.

5. The detection unit is It analyzes images of solar panels in real time to detect anomalies. The system according to feature 1.

6. The aforementioned charging management unit, The system uses solar power to charge electric vehicles and manages power supply to disaster-stricken areas and base stations during emergencies. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of weather data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is By combining historical weather data with real-time weather data, more accurate data can be collected. The system according to feature 1.

Images