system

Abstract

The system according to the embodiment aims to optimize the angle and direction of the solar panel to improve power generation efficiency. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, a setting unit, and a maintenance unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The prediction unit predicts an angle and direction based on the data analyzed by the analysis unit. The setting unit sets the solar panel based on the angle and direction predicted by the prediction unit. The maintenance unit performs maintenance on the solar panel set by the setting unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately optimize the angle and direction of solar panels, leaving room for improvement in power generation efficiency.

[0005] The system according to the embodiment aims to optimize the angle and direction of the solar panel to improve power generation efficiency. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, a setting unit, and a maintenance unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The prediction unit predicts an angle and a direction based on the data analyzed by the analysis unit. The setting unit sets the solar panel based on the angle and direction predicted by the prediction unit. The maintenance unit performs maintenance on the solar panel set by the setting unit. [Effects of the Invention]

[0007] The system according to the embodiment can optimize the angle and direction of the solar panel to improve power generation efficiency. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A solar panel configuration system according to an embodiment of the present invention uses AI to predict the optimal angle and direction based on accumulated data and configures solar panels based on that data. This solar panel configuration system can increase power generation by approximately 1.5 to 2 times. Specifically, AI first analyzes accumulated data, such as past weather and sunshine data, to predict the optimal angle and direction for the solar panels. Next, the solar panels are configured based on the predicted angle and direction. This increases power generation by approximately 1.5 to 2 times. Furthermore, periodic maintenance of the solar panels is performed by a dedicated maintenance system. This includes, for example, cleaning, inspection, and repair of the solar panels. This maintenance system monitors the status of the solar panels and automatically performs necessary maintenance. This system improves the power generation efficiency of the solar panels and realizes a sustainable energy supply. For example, applying this system to a residential solar panel system can significantly reduce household power consumption. Furthermore, applying it to a large-scale solar farm can improve the efficiency of energy supply throughout the entire region. As a result, the solar panel configuration system can increase power generation by approximately 1.5 to 2 times. In addition, by automatically performing maintenance on solar panels, power generation efficiency can be maintained and a sustainable energy supply can be achieved.

[0029] A solar panel configuration system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, a configuration unit, and a maintenance unit. The collection unit collects data. The data includes, for example, past weather data, sunshine data, and power consumption data, but is not limited to these examples. The collection unit acquires, for example, past weather data from a weather database. The collection unit can also acquire sunshine data from a sunshine meter. The collection unit can also acquire power consumption data from a power company database. For example, the collection unit can automatically collect these data via an API. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit preprocesses the data and fills in missing values. The analysis unit can also extract features of the data and input them into an analysis model. The analysis unit can also analyze patterns in the data using a machine learning algorithm. The prediction unit predicts an angle and direction based on the data analyzed by the analysis unit. The prediction is performed using, for example, a prediction model or algorithm, but is not limited to these examples. For example, the prediction unit calculates the optimal angle and direction of the solar panel using an optimization algorithm. The prediction unit can also determine the optimal angle and direction based on simulation results. Furthermore, the prediction unit can improve prediction accuracy by combining past data and current data. The setting unit sets the solar panel based on the angle and direction predicted by the prediction unit. The setting includes, for example, but is not limited to, adjusting the installation position and angle of the solar panel. For example, the setting unit adjusts the installation position of the solar panel in real time to set it to the optimal angle. The setting unit can also adjust the installation angle in real time to maximize sunlight. Furthermore, the setting unit can set the solar panel taking into account the installation environment. The maintenance unit performs maintenance of the solar panel set by the setting unit. The maintenance includes, for example, but is not limited to, cleaning, inspection, and repair of the solar panel. For example, the maintenance unit regularly cleans the solar panel.The maintenance unit can also inspect the solar panels and detect any abnormalities. Furthermore, the maintenance unit can repair the solar panels and maintain power generation efficiency. This allows the solar panel configuration system according to the embodiment to increase the amount of power generation. Some or all of the above-described processing in the maintenance unit may be performed using, for example, AI, or may be performed without using AI. For example, the maintenance unit can perform maintenance using an AI model that monitors the status of the solar panels and automatically performs necessary maintenance.

[0030] The collection unit can collect past weather data or sunshine data. The collection unit, for example, acquires past weather data from a weather database. Past weather data includes, for example, temperature, precipitation, wind speed, etc. The collection unit can also acquire sunshine data from a sunshine meter. Sunshine data includes, for example, solar radiation and sunshine hours. The collection unit can also acquire power consumption data from a power company database. Power consumption data includes, for example, power usage and peak consumption data. By collecting past weather data and sunshine data, the accuracy of predictions can be improved. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input weather data acquired from the weather database into the generation AI and have the generation AI perform preprocessing of the data.

[0031] The analysis unit can analyze the collected data in detail and predict the angle and direction. The analysis unit, for example, performs preprocessing of the collected data. Preprocessing includes, for example, missing value completion and data normalization. The analysis unit can also extract features of the data and input them into an analysis model. Features include, for example, temperature fluctuations and solar radiation patterns. The analysis unit can also analyze data patterns using machine learning algorithms. Machine learning algorithms include, for example, regression analysis, clustering, and deep learning. This improves the accuracy of predicting the optimal angle and direction through detailed analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI analyze the data.

[0032] The setting unit can set the solar panel based on the predicted angle and direction. The setting unit, for example, adjusts the installation position of the solar panel in real time to set the optimal angle. Adjusting the installation position includes, for example, selecting an installation location for the solar panel and adjusting the installation angle. The setting unit can also adjust the installation angle in real time to maximize sunlight. Adjusting the installation angle includes, for example, calculating the angle of the solar panel using an optimization algorithm. Furthermore, the setting unit can set the solar panel taking into account the installation environment. The installation environment includes, for example, the influence of surrounding buildings and trees. By setting the solar panel based on the predicted angle and direction, power generation efficiency is improved. Some or all of the above-described processing in the setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the setting unit can input the predicted angle and direction to a generation AI and have the generation AI configure the solar panel.

[0033] The maintenance unit can clean, inspect, and repair the solar panels. For example, the maintenance unit periodically cleans the solar panels. Cleaning includes, for example, cleaning the surface of the solar panels using specialized cleaning equipment. The maintenance unit can also inspect the solar panels to detect abnormalities. Inspection includes, for example, checking for damage or dirt on the surface of the solar panels and checking electrical connections. The maintenance unit can also repair the solar panels to maintain power generation efficiency. Repairs include, for example, replacing damaged solar panels and repairing electrical connections. In this way, power generation efficiency is maintained by cleaning, inspecting, and repairing the solar panels. Some or all of the above-described processing in the maintenance unit can be performed using, for example, AI, or without AI. For example, the maintenance unit can perform maintenance using an AI model that monitors the status of the solar panels and automatically performs necessary maintenance.

[0034] The maintenance unit can monitor the status of the solar panels and automatically perform necessary maintenance. For example, the maintenance unit monitors the status of the solar panels in real time and automatically performs maintenance if an abnormality is detected. Monitoring includes, for example, monitoring the status of the solar panels using sensors. Examples of sensors include temperature sensors, humidity sensors, and light sensors. The maintenance unit can also monitor the deterioration status of the solar panels in real time and automatically schedule necessary maintenance. Monitoring the deterioration status includes, for example, checking for damage or dirt on the surface of the solar panels and checking electrical connections. Furthermore, the maintenance unit can monitor the cleaning and inspection of the solar panels in real time and automatically perform necessary work. This improves maintenance efficiency by monitoring the status of the solar panels and automatically performing necessary maintenance. Some or all of the above-described processing in the maintenance unit may be performed using, for example, AI, or without AI. For example, the maintenance unit can input data monitoring the status of the solar panels into a generation AI and have the generation AI detect abnormalities and schedule maintenance.

[0035] The collection unit can collect regional power consumption data in addition to past weather data and sunshine data. For example, the collection unit acquires regional power consumption data via an API in addition to past weather data and sunshine data. The regional power consumption data includes, for example, power usage and peak consumption data. The collection unit can also collect regional power consumption data in real time and analyze it in combination with weather data and sunshine data. For example, the collection unit collects data taking into account consumption patterns during specific time periods or seasons. In this way, collecting regional power consumption data improves the accuracy of the analysis. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input regional power consumption data into a generation AI and have the generation AI perform preprocessing of the data.

[0036] The collection unit can detect abnormal weather and sudden weather changes in real time during data collection and reflect them in the data. For example, the collection unit can detect abnormal weather in real time and immediately reflect them in the collected data. Examples of abnormal weather include typhoons, heavy rain, and extreme heat. The collection unit can also detect sudden weather changes and adjust the frequency of data collection. Examples of sudden weather changes include sudden thunderstorms and strong winds. Furthermore, the collection unit can prioritize collection of data from specific sensors when abnormal weather occurs. This improves data accuracy by detecting abnormal weather and sudden weather changes in real time. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input abnormal weather data into a generation AI and have the generation AI analyze the data and adjust the collection frequency.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, collects the most relevant weather data based on the user's current location. Geographical location information includes, for example, GPS data, location information services, etc. The collection unit can also prioritize collecting highly relevant data by taking into account the user's movement patterns. For example, the collection unit prioritizes collecting data from a specific area based on the user's geographical location information. This allows highly relevant data to be collected preferentially by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0038] The collection unit can collect weather information from social media and news sites during data collection and reflect it in the data. The collection unit, for example, collects weather information from social media and reflects it in the data. Social media includes, for example, X (formerly Twitter (registered trademark)) and Facebook (registered trademark). The collection unit can also collect weather information from news sites and reflect it in the data. News sites include, for example, RSS feeds and APIs. Furthermore, the collection unit can collect information from social media and news sites in real time and reflect it in the data. In this way, collecting information from social media and news sites improves the accuracy of the data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input weather information from social media and news sites into the generation AI and have the generation AI perform data preprocessing and analysis.

[0039] During analysis, the analysis unit can compare past data with current data to analyze long-term weather patterns. For example, the analysis unit compares past weather data with current data to analyze long-term weather patterns. Past weather data includes, for example, temperature, precipitation, wind speed, etc. Current weather data includes, for example, temperature, precipitation, wind speed, etc. The analysis unit can analyze these data using time series analysis or trend analysis. For example, the analysis unit analyzes changes in current weather patterns based on past data. Furthermore, the analysis unit can analyze long-term weather patterns and use the results to predict future weather. In this way, long-term weather patterns can be analyzed by comparing past data with current data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input past data and current data into a generation AI and cause the generation AI to analyze long-term weather patterns.

[0040] The analysis unit can detect outliers in the data during analysis and reflect them in the analysis results. For example, the analysis unit can detect outliers in the data during analysis and reflect them in the analysis results. Examples of outlier detection include statistical anomaly detection and machine learning-based anomaly detection. Statistical anomaly detection includes, for example, methods using deviation from the mean or standard deviation. Machine learning-based anomaly detection includes, for example, anomaly detection algorithms and deep learning models. The analysis unit can also detect outliers and exclude them from the analysis results. Furthermore, the analysis unit can detect outliers and adjust them so that they do not affect the analysis results. This improves the accuracy of the analysis results by detecting outliers in the data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input outliers in the data to a generation AI and have the generation AI detect outliers and adjust the analysis results.

[0041] During analysis, the analysis unit can adjust the analysis results by taking into account regional power consumption data. The analysis unit, for example, adjusts the analysis results by taking into account regional power consumption data. Regional power consumption data includes, for example, power usage and peak consumption data. The analysis unit can also optimize the analysis results based on the power consumption data. For example, the analysis unit reflects regional power consumption data in the analysis to improve accuracy. As a result, the accuracy of the analysis results is improved by taking into account regional power consumption data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input regional power consumption data into the generation AI and have the generation AI adjust the analysis results.

[0042] The analysis unit can refer to weather data from other regions during analysis to improve the accuracy of the analysis results. The analysis unit, for example, refers to weather data from other regions to improve the accuracy of the analysis results. Weather data from other regions includes, for example, weather data from neighboring regions and international weather data. The analysis unit can also complement the analysis results based on data from other regions. For example, the analysis unit reflects weather patterns from other regions in the analysis to improve accuracy. In this way, the accuracy of the analysis results is improved by referring to weather data from other regions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input weather data from other regions into the generation AI and cause the generation AI to perform an analysis to improve the accuracy of the analysis results.

[0043] The prediction unit can improve prediction accuracy by combining past weather data and current weather data when making predictions. The prediction unit, for example, combines past weather data and current weather data to improve prediction accuracy. Past weather data includes, for example, temperature, precipitation, wind speed, etc. Current weather data includes, for example, temperature, precipitation, wind speed, etc. The prediction unit can integrate these data and input them into a prediction model. For example, the prediction unit makes predictions based on past data and taking current weather patterns into account. Furthermore, the prediction unit can integrate past data and current data to improve prediction accuracy. In this way, combining past weather data and current weather data improves prediction accuracy. Some or all of the above-described processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input past weather data and current weather data into the generation AI and cause the generation AI to perform predictions to improve prediction accuracy.

[0044] The prediction unit can adjust the prediction results by taking into account the probability of occurrence of abnormal weather during prediction. The prediction unit adjusts the prediction results, for example, by taking into account the probability of occurrence of abnormal weather. The probability of occurrence of abnormal weather includes, for example, a statistical model and a machine learning model. The statistical model includes, for example, a probability calculation based on past abnormal weather data. Furthermore, the machine learning model includes, for example, a model that has learned the occurrence pattern of abnormal weather. The prediction unit can also optimize the prediction results based on the abnormal weather data. For example, the prediction unit reflects the probability of occurrence of abnormal weather in the analysis to improve the prediction accuracy. In this way, the accuracy of the prediction results is improved by taking into account the probability of occurrence of abnormal weather. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the probability of occurrence of abnormal weather into the generation AI and have the generation AI adjust the prediction results.

[0045] The prediction unit can adjust the prediction result by taking into account regional power consumption data when making a prediction. The prediction unit, for example, adjusts the prediction result by taking into account regional power consumption data. Regional power consumption data includes, for example, power usage and peak consumption data. The prediction unit can also optimize the prediction result based on the power consumption data. For example, the prediction unit reflects regional power consumption data in the prediction to improve accuracy. As a result, the accuracy of the prediction result is improved by taking into account regional power consumption data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input regional power consumption data into the generation AI and have the generation AI adjust the prediction result.

[0046] The prediction unit can refer to weather data from other regions when making predictions to improve the accuracy of the prediction results. The prediction unit, for example, refers to weather data from other regions to improve the accuracy of the prediction results. Weather data from other regions includes, for example, weather data from neighboring regions, international weather data, etc. The prediction unit can also complement the prediction results based on data from other regions. For example, the prediction unit reflects weather patterns from other regions in the prediction to improve accuracy. In this way, the accuracy of the prediction results is improved by referring to weather data from other regions. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input weather data from other regions into the generation AI and cause the generation AI to perform predictions to improve the accuracy of the prediction results.

[0047] The setting unit can adjust the installation position and angle of the solar panel in real time during setup. The setting unit, for example, adjusts the installation position of the solar panel in real time to set it to an optimal angle. Adjusting the installation position includes, for example, selecting an installation location for the solar panel and adjusting the installation angle. The setting unit can also adjust the installation angle in real time to maximize sunlight. Adjusting the installation angle includes, for example, calculating the angle of the solar panel using an optimization algorithm. Furthermore, the setting unit can set the solar panel taking into account the installation environment. The installation environment includes, for example, the influence of surrounding buildings and trees. This improves power generation efficiency by adjusting the installation position and angle of the solar panel in real time. Some or all of the above-described processing in the setting unit may be performed using, for example, AI, or may be performed without AI. For example, the setting unit can input the installation position and angle of the solar panel to the generation AI and cause the generation AI to perform real-time adjustments.

[0048] The setting unit can perform the setting while taking into account the installation environment of the solar panel. For example, the setting unit adjusts the installation angle of the solar panel while taking into account the influence of surrounding buildings. The installation environment includes, for example, the influence of surrounding buildings and trees. The setting unit can also optimize the installation position of the solar panel while taking into account the influence of trees. For example, the setting unit sets the solar panel while taking into account the entire installation environment to maximize power generation efficiency. As a result, power generation efficiency is improved by taking into account the installation environment of the solar panel. Some or all of the above-mentioned processing in the setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the setting unit can input installation environment data of the solar panel to the generation AI and cause the generation AI to perform settings that take into account the installation environment.

[0049] During setup, the setting unit can adjust the solar panel settings taking into account local power consumption data. The setting unit, for example, adjusts the solar panel settings taking into account local power consumption data. The local power consumption data includes, for example, power usage and peak consumption data. The setting unit can also optimize the solar panel settings based on the power consumption data. For example, the setting unit reflects the local power consumption data in the settings to improve power generation efficiency. As a result, the accuracy of the solar panel settings is improved by taking the local power consumption data into account. Some or all of the above-described processing in the setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the setting unit can input the local power consumption data into a generation AI and cause the generation AI to configure the solar panel.

[0050] During configuration, the setting unit can refer to solar panel configuration data for other regions to improve the accuracy of the configuration. The setting unit, for example, refers to solar panel configuration data for other regions to improve the accuracy of the configuration. Solar panel configuration data for other regions includes, for example, configuration data for neighboring regions, international configuration data, etc. The setting unit can also complement the solar panel configuration based on data for other regions. For example, the setting unit reflects the configuration patterns of other regions in the configuration to improve accuracy. As a result, the accuracy of the configuration is improved by referring to the solar panel configuration data for other regions. Some or all of the above-described processing in the setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the setting unit can input solar panel configuration data for other regions into the generation AI and cause the generation AI to perform adjustments to improve the accuracy of the configuration.

[0051] The maintenance unit can monitor the status of the solar panels in real time during maintenance and automatically perform necessary maintenance. For example, the maintenance unit can monitor the status of the solar panels in real time and automatically perform maintenance if an abnormality is detected. Monitoring can include, for example, using sensors to monitor the status of the solar panels. Examples of sensors include temperature sensors, humidity sensors, and light sensors. The maintenance unit can also monitor the deterioration status of the solar panels in real time and automatically schedule necessary maintenance. Monitoring the deterioration status can include, for example, checking for damage or dirt on the solar panel surface and checking electrical connections. Furthermore, the maintenance unit can monitor the cleaning and inspection of the solar panels in real time and automatically perform necessary work. This improves maintenance efficiency by monitoring the status of the solar panels and automatically performing necessary maintenance. Some or all of the above-described processing in the maintenance unit can be performed using, for example, AI, or without AI. For example, the maintenance unit can input data monitoring the status of the solar panels into a generation AI and have the generation AI detect abnormalities and schedule maintenance.

[0052] The maintenance unit can analyze the deterioration status of the solar panel during maintenance and select the optimal maintenance method. The maintenance unit, for example, analyzes the deterioration status of the solar panel and selects the optimal cleaning method. The analysis of the deterioration status includes, for example, a deterioration index and an analysis algorithm. The maintenance unit can also analyze the deterioration status and select a necessary repair method. For example, the maintenance unit analyzes the deterioration status and selects an optimal inspection schedule. In this way, the optimal maintenance method can be selected by analyzing the deterioration status of the solar panel. Some or all of the above-mentioned processing in the maintenance unit may be performed using, for example, AI, or may be performed without using AI. For example, the maintenance unit can input deterioration status data of the solar panel to a generation AI and have the generation AI select the optimal maintenance method.

[0053] During maintenance, the maintenance unit can adjust the timing of maintenance taking into account local weather data. The maintenance unit adjusts the timing of maintenance, for example, taking into account local weather data. Local weather data includes, for example, temperature, precipitation, wind speed, etc. The maintenance unit can also optimize the maintenance schedule based on the weather data. For example, the maintenance unit reflects local weather data in maintenance to perform efficient work. In this way, the timing of maintenance can be optimized by taking into account local weather data. Some or all of the above-mentioned processing in the maintenance unit may be performed using, for example, AI, or may be performed without using AI. For example, the maintenance unit can input local weather data into a generation AI and have the generation AI execute the timing of maintenance.

[0054] The maintenance unit can improve the accuracy of maintenance by referring to maintenance data from other regions during maintenance. The maintenance unit, for example, refers to maintenance data from other regions to improve the accuracy of maintenance. Maintenance data from other regions includes, for example, maintenance data from neighboring regions and international maintenance data. The maintenance unit can also complement the maintenance method based on data from other regions. For example, the maintenance unit reflects maintenance patterns from other regions in the maintenance to improve accuracy. In this way, the accuracy of maintenance is improved by referring to the maintenance data from other regions. Some or all of the above-described processing in the maintenance unit may be performed using, for example, AI, or may be performed without using AI. For example, the maintenance unit can input maintenance data from other regions into the generation AI and have the generation AI perform analysis to improve the accuracy of maintenance.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] During analysis, the analysis unit can compare past data with current data to analyze long-term weather patterns. For example, past weather data can be compared with current data to analyze long-term weather patterns. Past weather data includes temperature, precipitation, wind speed, etc. Current weather data includes temperature, precipitation, wind speed, etc. The analysis unit can analyze this data using time series analysis and trend analysis. For example, changes in current weather patterns can be analyzed based on past data. Furthermore, analyzing long-term weather patterns can also be used to predict future weather. In this way, long-term weather patterns can be analyzed by comparing past data with current data.

[0057] During maintenance, the maintenance unit can analyze the deterioration status of the solar panels and select the optimal maintenance method. For example, it analyzes the deterioration status of the solar panels and selects the optimal cleaning method. The analysis of the deterioration status includes deterioration indicators and analysis algorithms. It can also analyze the deterioration status and select the necessary repair method. Furthermore, it analyzes the deterioration status and selects the optimal inspection schedule. In this way, it is possible to select the optimal maintenance method by analyzing the deterioration status of the solar panels.

[0058] The collection unit can collect weather information from social media and news sites during data collection and reflect it in the data. For example, weather information from social media is collected and reflected in the data. Social media includes X (formerly Twitter) and Facebook. Weather information from news sites can also be collected and reflected in the data. Furthermore, information from social media and news sites can be collected in real time and reflected in the data. In this way, collecting information from social media and news sites improves the accuracy of the data.

[0059] The prediction unit can adjust the prediction results by taking into account the probability of occurrence of abnormal weather when making predictions. For example, the prediction results are adjusted by taking into account the probability of occurrence of abnormal weather. The probability of occurrence of abnormal weather includes statistical models and machine learning models. Statistical models include probability calculations based on past abnormal weather data. Machine learning models include models that have learned patterns of occurrence of abnormal weather. The prediction unit can also optimize the prediction results based on abnormal weather data. In this way, the accuracy of the prediction results is improved by taking into account the probability of occurrence of abnormal weather.

[0060] During configuration, the configuration unit can improve the accuracy of the configuration by referring to solar panel configuration data for other regions. For example, the configuration accuracy can be improved by referring to solar panel configuration data for other regions. Solar panel configuration data for other regions includes configuration data for neighboring regions, international configuration data, etc. The solar panel configuration can also be supplemented based on data for other regions. In this way, the accuracy of the configuration can be improved by referring to solar panel configuration data for other regions.

[0061] The collection unit can detect abnormal weather and sudden weather changes in real time during data collection and reflect them in the data. For example, it can detect abnormal weather in real time and immediately reflect them in the collected data. Abnormal weather includes typhoons, heavy rain, and extreme heat. It can also detect sudden weather changes and adjust the frequency of data collection. Furthermore, when abnormal weather occurs, it can prioritize the collection of data from specific sensors. This improves the accuracy of data by detecting abnormal weather and sudden weather changes in real time.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The collection unit collects data. The data includes past weather data, sunshine data, and power consumption data. The collection unit obtains past weather data from a weather database, sunshine data from a sunshine meter, and power consumption data from a power company database. These data are collected automatically via API. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. The analysis unit preprocesses the data, complements missing values, extracts data features, and inputs them into an analysis model. Furthermore, the analysis unit uses machine learning algorithms to analyze data patterns. Step 3: The prediction unit predicts the angle and direction based on the data analyzed by the analysis unit. The prediction is performed using a prediction model or algorithm. The prediction unit uses an optimization algorithm to calculate the optimal angle and direction of the solar panel, and determines the optimal angle and direction based on the simulation results. It is also possible to improve the accuracy of the prediction by combining past data with current data. Step 4: The setting unit sets the solar panel based on the angle and direction predicted by the prediction unit. The setting includes adjusting the installation position and angle of the solar panel. The setting unit adjusts the installation position of the solar panel in real time to set it at the optimal angle. The installation angle is adjusted in real time to maximize sunlight. The solar panel is set up taking into account the installation environment. Step 5: The maintenance unit performs maintenance on the solar panels configured by the configuration unit. Maintenance includes cleaning, inspection, and repair of the solar panels. The maintenance unit periodically cleans the solar panels, inspects them to detect abnormalities, and repairs them to maintain power generation efficiency. The maintenance unit's processing can also be performed automatically using AI.

[0064] (Example 2) A solar panel configuration system according to an embodiment of the present invention uses AI to predict the optimal angle and direction based on accumulated data and configures solar panels based on that data. This solar panel configuration system can increase power generation by approximately 1.5 to 2 times. Specifically, AI first analyzes accumulated data, such as past weather and sunshine data, to predict the optimal angle and direction for the solar panels. Next, the solar panels are configured based on the predicted angle and direction. This increases power generation by approximately 1.5 to 2 times. Furthermore, periodic maintenance of the solar panels is performed by a dedicated maintenance system. This includes, for example, cleaning, inspection, and repair of the solar panels. This maintenance system monitors the status of the solar panels and automatically performs necessary maintenance. This system improves the power generation efficiency of the solar panels and realizes a sustainable energy supply. For example, applying this system to a residential solar panel system can significantly reduce household power consumption. Furthermore, applying it to a large-scale solar farm can improve the efficiency of energy supply throughout the entire region. As a result, the solar panel configuration system can increase power generation by approximately 1.5 to 2 times. In addition, by automatically performing maintenance on solar panels, power generation efficiency can be maintained and a sustainable energy supply can be achieved.

[0065] A solar panel configuration system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, a configuration unit, and a maintenance unit. The collection unit collects data. The data includes, for example, past weather data, sunshine data, and power consumption data, but is not limited to these examples. The collection unit acquires, for example, past weather data from a weather database. The collection unit can also acquire sunshine data from a sunshine meter. The collection unit can also acquire power consumption data from a power company database. For example, the collection unit can automatically collect these data via an API. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit preprocesses the data and fills in missing values. The analysis unit can also extract features of the data and input them into an analysis model. The analysis unit can also analyze patterns in the data using a machine learning algorithm. The prediction unit predicts an angle and direction based on the data analyzed by the analysis unit. The prediction is performed using, for example, a prediction model or algorithm, but is not limited to these examples. For example, the prediction unit calculates the optimal angle and direction of the solar panel using an optimization algorithm. The prediction unit can also determine the optimal angle and direction based on simulation results. Furthermore, the prediction unit can improve prediction accuracy by combining past data and current data. The setting unit sets the solar panel based on the angle and direction predicted by the prediction unit. The setting includes, for example, but is not limited to, adjusting the installation position and angle of the solar panel. For example, the setting unit adjusts the installation position of the solar panel in real time to set it to the optimal angle. The setting unit can also adjust the installation angle in real time to maximize sunlight. Furthermore, the setting unit can set the solar panel taking into account the installation environment. The maintenance unit performs maintenance of the solar panel set by the setting unit. The maintenance includes, for example, but is not limited to, cleaning, inspection, and repair of the solar panel. For example, the maintenance unit regularly cleans the solar panel.The maintenance unit can also inspect the solar panels and detect any abnormalities. Furthermore, the maintenance unit can repair the solar panels and maintain power generation efficiency. This allows the solar panel configuration system according to the embodiment to increase the amount of power generation. Some or all of the above-described processing in the maintenance unit may be performed using, for example, AI, or may be performed without using AI. For example, the maintenance unit can perform maintenance using an AI model that monitors the status of the solar panels and automatically performs necessary maintenance.

[0066] The collection unit can collect past weather data or sunshine data. The collection unit, for example, acquires past weather data from a weather database. Past weather data includes, for example, temperature, precipitation, wind speed, etc. The collection unit can also acquire sunshine data from a sunshine meter. Sunshine data includes, for example, solar radiation and sunshine hours. The collection unit can also acquire power consumption data from a power company database. Power consumption data includes, for example, power usage and peak consumption data. By collecting past weather data and sunshine data, the accuracy of predictions can be improved. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input weather data acquired from the weather database into the generation AI and have the generation AI perform preprocessing of the data.

[0067] The analysis unit can analyze the collected data in detail and predict the angle and direction. The analysis unit, for example, performs preprocessing of the collected data. Preprocessing includes, for example, missing value completion and data normalization. The analysis unit can also extract features of the data and input them into an analysis model. Features include, for example, temperature fluctuations and solar radiation patterns. The analysis unit can also analyze data patterns using machine learning algorithms. Machine learning algorithms include, for example, regression analysis, clustering, and deep learning. This improves the accuracy of predicting the optimal angle and direction through detailed analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI analyze the data.

[0068] The setting unit can set the solar panel based on the predicted angle and direction. The setting unit, for example, adjusts the installation position of the solar panel in real time to set the optimal angle. Adjusting the installation position includes, for example, selecting an installation location for the solar panel and adjusting the installation angle. The setting unit can also adjust the installation angle in real time to maximize sunlight. Adjusting the installation angle includes, for example, calculating the angle of the solar panel using an optimization algorithm. Furthermore, the setting unit can set the solar panel taking into account the installation environment. The installation environment includes, for example, the influence of surrounding buildings and trees. By setting the solar panel based on the predicted angle and direction, power generation efficiency is improved. Some or all of the above-described processing in the setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the setting unit can input the predicted angle and direction to a generation AI and have the generation AI configure the solar panel.

[0069] The maintenance unit can clean, inspect, and repair the solar panels. For example, the maintenance unit periodically cleans the solar panels. Cleaning includes, for example, cleaning the surface of the solar panels using specialized cleaning equipment. The maintenance unit can also inspect the solar panels to detect abnormalities. Inspection includes, for example, checking for damage or dirt on the surface of the solar panels and checking electrical connections. The maintenance unit can also repair the solar panels to maintain power generation efficiency. Repairs include, for example, replacing damaged solar panels and repairing electrical connections. In this way, power generation efficiency is maintained by cleaning, inspecting, and repairing the solar panels. Some or all of the above-described processing in the maintenance unit can be performed using, for example, AI, or without AI. For example, the maintenance unit can perform maintenance using an AI model that monitors the status of the solar panels and automatically performs necessary maintenance.

[0070] The maintenance unit can monitor the status of the solar panels and automatically perform necessary maintenance. For example, the maintenance unit monitors the status of the solar panels in real time and automatically performs maintenance if an abnormality is detected. Monitoring includes, for example, monitoring the status of the solar panels using sensors. Examples of sensors include temperature sensors, humidity sensors, and light sensors. The maintenance unit can also monitor the deterioration status of the solar panels in real time and automatically schedule necessary maintenance. Monitoring the deterioration status includes, for example, checking for damage or dirt on the surface of the solar panels and checking electrical connections. Furthermore, the maintenance unit can monitor the cleaning and inspection of the solar panels in real time and automatically perform necessary work. This improves maintenance efficiency by monitoring the status of the solar panels and automatically performing necessary maintenance. Some or all of the above-described processing in the maintenance unit may be performed using, for example, AI, or without AI. For example, the maintenance unit can input data monitoring the status of the solar panels into a generation AI and have the generation AI detect abnormalities and schedule maintenance.

[0071] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of data collection, thereby reducing the burden on the user. For example, the collection unit uses an emotion recognition algorithm to estimate the user's emotions. Emotion recognition algorithms include, for example, facial expression recognition, voice analysis, and text analysis. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection and collect more detailed data. For example, the collection unit analyzes the user's emotion data in real time and adjusts the timing of data collection. Furthermore, if the user is in a hurry, the collection unit can quickly collect data and provide necessary information immediately. This reduces the burden on the user by adjusting the timing of data collection based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input user emotion data into the generation AI and have the generation AI execute data collection timing.

[0072] The collection unit can collect regional power consumption data in addition to past weather data and sunshine data. For example, the collection unit acquires regional power consumption data via an API in addition to past weather data and sunshine data. The regional power consumption data includes, for example, power usage and peak consumption data. The collection unit can also collect regional power consumption data in real time and analyze it in combination with weather data and sunshine data. For example, the collection unit collects data taking into account consumption patterns during specific time periods or seasons. In this way, collecting regional power consumption data improves the accuracy of the analysis. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input regional power consumption data into a generation AI and have the generation AI perform preprocessing of the data.

[0073] The collection unit can detect abnormal weather and sudden weather changes in real time during data collection and reflect them in the data. For example, the collection unit can detect abnormal weather in real time and immediately reflect them in the collected data. Examples of abnormal weather include typhoons, heavy rain, and extreme heat. The collection unit can also detect sudden weather changes and adjust the frequency of data collection. Examples of sudden weather changes include sudden thunderstorms and strong winds. Furthermore, the collection unit can prioritize collection of data from specific sensors when abnormal weather occurs. This improves data accuracy by detecting abnormal weather and sudden weather changes in real time. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input abnormal weather data into a generation AI and have the generation AI analyze the data and adjust the collection frequency.

[0074] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting data of high importance. For example, the collection unit uses an emotion recognition algorithm to estimate the user's emotions. Emotion recognition algorithms include, for example, facial expression recognition, voice analysis, and text analysis. The collection unit can also prioritize collecting detailed data when the user is relaxed. For example, the collection unit analyzes the user's emotion data in real time and prioritizes the data to be collected. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly. This allows important data to be collected preferentially by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input user emotion data into the generation AI and have the generation AI prioritize the data.

[0075] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, collects the most relevant weather data based on the user's current location. Geographical location information includes, for example, GPS data, location information services, etc. The collection unit can also prioritize collecting highly relevant data by taking into account the user's movement patterns. For example, the collection unit prioritizes collecting data from a specific area based on the user's geographical location information. This allows highly relevant data to be collected preferentially by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0076] The collection unit can collect weather information from social media and news sites during data collection and reflect it in the data. The collection unit, for example, collects weather information from social media and reflects it in the data. Social media includes, for example, X (formerly Twitter) and Facebook. The collection unit can also collect weather information from news sites and reflect it in the data. News sites include, for example, RSS feeds and APIs. Furthermore, the collection unit can collect information from social media and news sites in real time and reflect it in the data. In this way, collecting information from social media and news sites improves the accuracy of the data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input weather information from social media and news sites into the generation AI and have the generation AI perform data preprocessing and analysis.

[0077] 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, highly visible display method. For example, the analysis unit uses an emotion recognition algorithm to estimate the user's emotions. Emotion recognition algorithms include, for example, facial expression recognition, voice analysis, and text analysis. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, the analysis unit analyzes the user's emotion data in real time and adjusts the display method of the analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for an easy-to-read display by adjusting the display method of the analysis results based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI execute a method for displaying the analysis results.

[0078] During analysis, the analysis unit can compare past data with current data to analyze long-term weather patterns. For example, the analysis unit compares past weather data with current data to analyze long-term weather patterns. Past weather data includes, for example, temperature, precipitation, wind speed, etc. Current weather data includes, for example, temperature, precipitation, wind speed, etc. The analysis unit can analyze these data using time series analysis or trend analysis. For example, the analysis unit analyzes changes in current weather patterns based on past data. Furthermore, the analysis unit can analyze long-term weather patterns and use the results to predict future weather. In this way, long-term weather patterns can be analyzed by comparing past data with current data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input past data and current data into a generation AI and cause the generation AI to analyze long-term weather patterns.

[0079] The analysis unit can detect outliers in the data during analysis and reflect them in the analysis results. For example, the analysis unit can detect outliers in the data during analysis and reflect them in the analysis results. Examples of outlier detection include statistical anomaly detection and machine learning-based anomaly detection. Statistical anomaly detection includes, for example, methods using deviation from the mean or standard deviation. Machine learning-based anomaly detection includes, for example, anomaly detection algorithms and deep learning models. The analysis unit can also detect outliers and exclude them from the analysis results. Furthermore, the analysis unit can detect outliers and adjust them so that they do not affect the analysis results. This improves the accuracy of the analysis results by detecting outliers in the data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input outliers in the data to a generation AI and have the generation AI detect outliers and adjust the analysis results.

[0080] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit prioritizes analysis of higher importance. For example, the analysis unit uses an emotion recognition algorithm to estimate the user's emotions. Emotion recognition algorithms include, for example, facial expression recognition, voice analysis, and text analysis. Furthermore, if the user is relaxed, the analysis unit can prioritize detailed analysis. For example, the analysis unit analyzes the user's emotion data in real time and determines the priority of analysis. Furthermore, if the user is in a hurry, the analysis unit can quickly perform analysis and provide necessary information. Thus, by determining the priority of analysis based on the user's emotions, important analysis can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI perform the analysis priorities.

[0081] During analysis, the analysis unit can adjust the analysis results by taking into account regional power consumption data. The analysis unit, for example, adjusts the analysis results by taking into account regional power consumption data. Regional power consumption data includes, for example, power usage and peak consumption data. The analysis unit can also optimize the analysis results based on the power consumption data. For example, the analysis unit reflects regional power consumption data in the analysis to improve accuracy. As a result, the accuracy of the analysis results is improved by taking into account regional power consumption data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input regional power consumption data into the generation AI and have the generation AI adjust the analysis results.

[0082] The analysis unit can refer to weather data from other regions during analysis to improve the accuracy of the analysis results. The analysis unit, for example, refers to weather data from other regions to improve the accuracy of the analysis results. Weather data from other regions includes, for example, weather data from neighboring regions and international weather data. The analysis unit can also complement the analysis results based on data from other regions. For example, the analysis unit reflects weather patterns from other regions in the analysis to improve accuracy. In this way, the accuracy of the analysis results is improved by referring to weather data from other regions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input weather data from other regions into the generation AI and cause the generation AI to perform an analysis to improve the accuracy of the analysis results.

[0083] The prediction unit can estimate the user's emotion and adjust the display method of the prediction result based on the estimated user emotion. For example, if the user is nervous, the prediction unit provides a simple, highly visible display method. For example, the prediction unit uses an emotion recognition algorithm to estimate the user's emotion. Emotion recognition algorithms include, for example, facial expression recognition, voice analysis, and text analysis. Furthermore, if the user is relaxed, the prediction unit can provide a display method that includes detailed information. For example, the prediction unit analyzes the user's emotion data in real time and adjusts the display method of the prediction result. Furthermore, if the user is in a hurry, the prediction unit can provide a display method that focuses on the main points. This allows for an easy-to-read display by adjusting the display method of the prediction result based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prediction unit can be performed using, for example, an AI, or without an AI. For example, the prediction unit can input user emotion data into the generation AI and have the generation AI execute a method for displaying the prediction results.

[0084] The prediction unit can improve prediction accuracy by combining past weather data and current weather data when making predictions. The prediction unit, for example, combines past weather data and current weather data to improve prediction accuracy. Past weather data includes, for example, temperature, precipitation, wind speed, etc. Current weather data includes, for example, temperature, precipitation, wind speed, etc. The prediction unit can integrate these data and input them into a prediction model. For example, the prediction unit makes predictions based on past data and taking current weather patterns into account. Furthermore, the prediction unit can integrate past data and current data to improve prediction accuracy. In this way, combining past weather data and current weather data improves prediction accuracy. Some or all of the above-described processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input past weather data and current weather data into the generation AI and cause the generation AI to perform predictions to improve prediction accuracy.

[0085] The prediction unit can adjust the prediction results by taking into account the probability of occurrence of abnormal weather during prediction. The prediction unit adjusts the prediction results, for example, by taking into account the probability of occurrence of abnormal weather. The probability of occurrence of abnormal weather includes, for example, a statistical model and a machine learning model. The statistical model includes, for example, a probability calculation based on past abnormal weather data. Furthermore, the machine learning model includes, for example, a model that has learned the occurrence pattern of abnormal weather. The prediction unit can also optimize the prediction results based on the abnormal weather data. For example, the prediction unit reflects the probability of occurrence of abnormal weather in the analysis to improve the prediction accuracy. In this way, the accuracy of the prediction results is improved by taking into account the probability of occurrence of abnormal weather. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the probability of occurrence of abnormal weather into the generation AI and have the generation AI adjust the prediction results.

[0086] The prediction unit can estimate the user's emotions and determine the priority of predictions based on the estimated user emotions. For example, when the user is feeling stressed, the prediction unit prioritizes predictions of high importance. For example, the prediction unit uses an emotion recognition algorithm to estimate the user's emotions. The emotion recognition algorithm includes, for example, facial expression recognition, voice analysis, and text analysis. Furthermore, when the user is relaxed, the prediction unit can prioritize detailed predictions. For example, the prediction unit analyzes the user's emotion data in real time and determines the priority of predictions. Furthermore, when the user is in a hurry, the prediction unit can quickly make predictions and provide necessary information. Thus, by prioritizing predictions based on the user's emotions, important predictions can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input user emotion data into the generation AI and have the generation AI execute prediction priorities.

[0087] The prediction unit can adjust the prediction result by taking into account regional power consumption data when making a prediction. The prediction unit, for example, adjusts the prediction result by taking into account regional power consumption data. Regional power consumption data includes, for example, power usage and peak consumption data. The prediction unit can also optimize the prediction result based on the power consumption data. For example, the prediction unit reflects regional power consumption data in the prediction to improve accuracy. As a result, the accuracy of the prediction result is improved by taking into account regional power consumption data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input regional power consumption data into the generation AI and have the generation AI adjust the prediction result.

[0088] The prediction unit can refer to weather data from other regions when making predictions to improve the accuracy of the prediction results. The prediction unit, for example, refers to weather data from other regions to improve the accuracy of the prediction results. Weather data from other regions includes, for example, weather data from neighboring regions, international weather data, etc. The prediction unit can also complement the prediction results based on data from other regions. For example, the prediction unit reflects weather patterns from other regions in the prediction to improve accuracy. In this way, the accuracy of the prediction results is improved by referring to weather data from other regions. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input weather data from other regions into the generation AI and cause the generation AI to perform predictions to improve the accuracy of the prediction results.

[0089] The setting unit can estimate the user's emotions and adjust the solar panel settings based on the estimated user emotions. For example, if the user is nervous, the setting unit provides a simple and highly visible setting method. For example, the setting unit uses an emotion recognition algorithm to estimate the user's emotions. Emotion recognition algorithms include, for example, facial expression recognition, voice analysis, and text analysis. The setting unit can also provide detailed setting options if the user is relaxed. For example, the setting unit analyzes the user's emotion data in real time and adjusts the solar panel settings. Furthermore, if the user is in a hurry, the setting unit can provide a method for quick setting. This allows for user-friendly settings by adjusting the solar panel settings based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the setting unit may be performed using AI, for example, or without AI. For example, the setting unit can input the user's emotional data into the generation AI and cause the generation AI to execute a method for setting up a solar panel.

[0090] The setting unit can adjust the installation position and angle of the solar panel in real time during setup. The setting unit, for example, adjusts the installation position of the solar panel in real time to set it to an optimal angle. Adjusting the installation position includes, for example, selecting an installation location for the solar panel and adjusting the installation angle. The setting unit can also adjust the installation angle in real time to maximize sunlight. Adjusting the installation angle includes, for example, calculating the angle of the solar panel using an optimization algorithm. Furthermore, the setting unit can set the solar panel taking into account the installation environment. The installation environment includes, for example, the influence of surrounding buildings and trees. This improves power generation efficiency by adjusting the installation position and angle of the solar panel in real time. Some or all of the above-described processing in the setting unit may be performed using, for example, AI, or may be performed without AI. For example, the setting unit can input the installation position and angle of the solar panel to the generation AI and cause the generation AI to perform real-time adjustments.

[0091] The setting unit can perform the setting while taking into account the installation environment of the solar panel. For example, the setting unit adjusts the installation angle of the solar panel while taking into account the influence of surrounding buildings. The installation environment includes, for example, the influence of surrounding buildings and trees. The setting unit can also optimize the installation position of the solar panel while taking into account the influence of trees. For example, the setting unit sets the solar panel while taking into account the entire installation environment to maximize power generation efficiency. As a result, power generation efficiency is improved by taking into account the installation environment of the solar panel. Some or all of the above-mentioned processing in the setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the setting unit can input installation environment data of the solar panel to the generation AI and cause the generation AI to perform settings that take into account the installation environment.

[0092] The setting unit can estimate the user's emotions and determine the priority of solar panel settings based on the estimated user emotions. For example, if the user is feeling stressed, the setting unit prioritizes settings with high importance. For example, the setting unit uses an emotion recognition algorithm to estimate the user's emotions. The emotion recognition algorithm includes, for example, facial expression recognition, voice analysis, and text analysis. Furthermore, if the user is relaxed, the setting unit can prioritize detailed settings. For example, the setting unit analyzes the user's emotion data in real time and determines the priority of settings. Furthermore, if the user is in a hurry, the setting unit can prioritize settings that can be made quickly. Thus, by determining the priority of settings based on the user's emotions, important settings can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the setting unit can input the user's emotional data into the generation AI and have the generation AI execute the setting priorities.

[0093] During setup, the setting unit can adjust the solar panel settings taking into account local power consumption data. The setting unit, for example, adjusts the solar panel settings taking into account local power consumption data. The local power consumption data includes, for example, power usage and peak consumption data. The setting unit can also optimize the solar panel settings based on the power consumption data. For example, the setting unit reflects the local power consumption data in the settings to improve power generation efficiency. As a result, the accuracy of the solar panel settings is improved by taking the local power consumption data into account. Some or all of the above-described processing in the setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the setting unit can input the local power consumption data into a generation AI and cause the generation AI to configure the solar panel.

[0094] During configuration, the setting unit can refer to solar panel configuration data for other regions to improve the accuracy of the configuration. The setting unit, for example, refers to solar panel configuration data for other regions to improve the accuracy of the configuration. Solar panel configuration data for other regions includes, for example, configuration data for neighboring regions, international configuration data, etc. The setting unit can also complement the solar panel configuration based on data for other regions. For example, the setting unit reflects the configuration patterns of other regions in the configuration to improve accuracy. As a result, the accuracy of the configuration is improved by referring to the solar panel configuration data for other regions. Some or all of the above-described processing in the setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the setting unit can input solar panel configuration data for other regions into the generation AI and cause the generation AI to perform adjustments to improve the accuracy of the configuration.

[0095] The maintenance unit can estimate the user's emotions and adjust the timing of maintenance based on the estimated user emotions. For example, if the user is feeling stressed, the maintenance unit reduces the frequency of maintenance and reduces the burden on the user. For example, the maintenance unit uses an emotion recognition algorithm to estimate the user's emotions. The emotion recognition algorithm includes, for example, facial expression recognition, voice analysis, and text analysis. Furthermore, if the user is relaxed, the maintenance unit can increase the frequency of maintenance and perform detailed inspections. For example, the maintenance unit analyzes the user's emotion data in real time and adjusts the timing of maintenance. Furthermore, if the user is in a hurry, the maintenance unit can quickly perform maintenance and immediately perform necessary work. This reduces the burden on the user by adjusting the timing of maintenance based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the maintenance unit may be performed using, for example, an AI or without an AI. For example, the maintenance unit can input the user's emotional data into the generation AI and have the generation AI perform maintenance at the appropriate time.

[0096] The maintenance unit can monitor the status of the solar panels in real time during maintenance and automatically perform necessary maintenance. For example, the maintenance unit can monitor the status of the solar panels in real time and automatically perform maintenance if an abnormality is detected. Monitoring can include, for example, using sensors to monitor the status of the solar panels. Examples of sensors include temperature sensors, humidity sensors, and light sensors. The maintenance unit can also monitor the deterioration status of the solar panels in real time and automatically schedule necessary maintenance. Monitoring the deterioration status can include, for example, checking for damage or dirt on the solar panel surface and checking electrical connections. Furthermore, the maintenance unit can monitor the cleaning and inspection of the solar panels in real time and automatically perform necessary work. This improves maintenance efficiency by monitoring the status of the solar panels and automatically performing necessary maintenance. Some or all of the above-described processing in the maintenance unit can be performed using, for example, AI, or without AI. For example, the maintenance unit can input data monitoring the status of the solar panels into a generation AI and have the generation AI detect abnormalities and schedule maintenance.

[0097] The maintenance unit can analyze the deterioration status of the solar panel during maintenance and select the optimal maintenance method. The maintenance unit, for example, analyzes the deterioration status of the solar panel and selects the optimal cleaning method. The analysis of the deterioration status includes, for example, a deterioration index and an analysis algorithm. The maintenance unit can also analyze the deterioration status and select a necessary repair method. For example, the maintenance unit analyzes the deterioration status and selects an optimal inspection schedule. In this way, the optimal maintenance method can be selected by analyzing the deterioration status of the solar panel. Some or all of the above-mentioned processing in the maintenance unit may be performed using, for example, AI, or may be performed without using AI. For example, the maintenance unit can input deterioration status data of the solar panel to a generation AI and have the generation AI select the optimal maintenance method.

[0098] The maintenance unit can estimate the user's emotions and determine the priority of maintenance based on the estimated user emotions. For example, if the user is feeling stressed, the maintenance unit prioritizes maintenance that is of high importance. For example, the maintenance unit uses an emotion recognition algorithm to estimate the user's emotions. The emotion recognition algorithm includes, for example, facial expression recognition, voice analysis, and text analysis. Furthermore, if the user is relaxed, the maintenance unit can prioritize detailed maintenance. For example, the maintenance unit analyzes the user's emotion data in real time to determine the priority of maintenance. Furthermore, if the user is in a hurry, the maintenance unit can prioritize maintenance items that can be performed quickly. Thus, by determining the priority of maintenance based on the user's emotions, important maintenance can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the maintenance unit may be performed using, for example, an AI, or without an AI. For example, the maintenance unit can input the user's emotional data into the generation AI and have the generation AI execute maintenance priorities.

[0099] During maintenance, the maintenance unit can adjust the timing of maintenance taking into account local weather data. The maintenance unit adjusts the timing of maintenance, for example, taking into account local weather data. Local weather data includes, for example, temperature, precipitation, wind speed, etc. The maintenance unit can also optimize the maintenance schedule based on the weather data. For example, the maintenance unit reflects local weather data in maintenance to perform efficient work. In this way, the timing of maintenance can be optimized by taking into account local weather data. Some or all of the above-mentioned processing in the maintenance unit may be performed using, for example, AI, or may be performed without using AI. For example, the maintenance unit can input local weather data into a generation AI and have the generation AI execute the timing of maintenance.

[0100] The maintenance unit can improve the accuracy of maintenance by referring to maintenance data from other regions during maintenance. The maintenance unit, for example, refers to maintenance data from other regions to improve the accuracy of maintenance. Maintenance data from other regions includes, for example, maintenance data from neighboring regions and international maintenance data. The maintenance unit can also complement the maintenance method based on data from other regions. For example, the maintenance unit reflects maintenance patterns from other regions in the maintenance to improve accuracy. In this way, the accuracy of maintenance is improved by referring to the maintenance data from other regions. Some or all of the above-described processing in the maintenance unit may be performed using, for example, AI, or may be performed without using AI. For example, the maintenance unit can input maintenance data from other regions into the generation AI and have the generation AI perform analysis to improve the accuracy of maintenance. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, setting unit, and maintenance unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data using the camera 42 or a sensor of the smart device 14, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the angle and direction of the solar panel based on the analyzed data. The setting unit is realized by the control unit 46A of the smart device 14 and sets the solar panel based on the predicted angle and direction. The maintenance unit is realized by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12 and monitors the status of the solar panel and automatically performs necessary maintenance. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, setting unit, and maintenance unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data using the camera 42 or a sensor of the smart glasses 214, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the angle and direction of the solar panel based on the analyzed data. The setting unit is realized by the control unit 46A of the smart glasses 214 and sets the solar panel based on the predicted angle and direction. The maintenance unit is realized by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12 and monitors the status of the solar panel and automatically performs necessary maintenance. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, prediction unit, setting unit, and maintenance unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects data using the camera 42 or a sensor of the headset type terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the angle and direction of the solar panel based on the analyzed data. The setting unit is realized by the control unit 46A of the headset type terminal 314 and sets the solar panel based on the predicted angle and direction. The maintenance unit is realized by the control unit 46A of the headset type terminal 314 and the specific processing unit 290 of the data processing device 12 and monitors the status of the solar panel and automatically performs necessary maintenance. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, setting unit, and maintenance unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data using the camera 42 or sensors of the robot 414, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the angle and direction of the solar panel based on the analyzed data. The setting unit is realized by the control unit 46A of the robot 414 and sets the solar panel based on the predicted angle and direction. The maintenance unit is realized by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12 and monitors the status of the solar panel and automatically performs necessary maintenance.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the burden on the user. The collection unit uses an emotion recognition algorithm to estimate the user's emotions. The emotion recognition algorithm includes facial expression recognition, voice analysis, text analysis, etc. Also, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is in a hurry, data collection can be performed quickly to provide the necessary information immediately. In this way, the burden on the user can be reduced by adjusting the timing of data collection based on the user's emotions.

[0103] During analysis, the analysis unit can compare past data with current data to analyze long-term weather patterns. For example, past weather data can be compared with current data to analyze long-term weather patterns. Past weather data includes temperature, precipitation, wind speed, etc. Current weather data includes temperature, precipitation, wind speed, etc. The analysis unit can analyze this data using time series analysis and trend analysis. For example, changes in current weather patterns can be analyzed based on past data. Furthermore, analyzing long-term weather patterns can also be used to predict future weather. In this way, long-term weather patterns can be analyzed by comparing past data with current data.

[0104] The setting unit can estimate the user's emotions and adjust the solar panel setting method based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible setting method is provided. The setting unit uses an emotion recognition algorithm to estimate the user's emotions. The emotion recognition algorithm includes facial expression recognition, voice analysis, text analysis, etc. In addition, if the user is relaxed, detailed setting options can be provided. Furthermore, if the user is in a hurry, a method for quick setting can be provided. In this way, adjusting the solar panel setting method based on the user's emotions enables user-friendly settings.

[0105] During maintenance, the maintenance unit can analyze the deterioration status of the solar panels and select the optimal maintenance method. For example, it analyzes the deterioration status of the solar panels and selects the optimal cleaning method. The analysis of the deterioration status includes deterioration indicators and analysis algorithms. It can also analyze the deterioration status and select the necessary repair method. Furthermore, it analyzes the deterioration status and selects the optimal inspection schedule. In this way, it is possible to select the optimal maintenance method by analyzing the deterioration status of the solar panels.

[0106] The collection unit can collect weather information from social media and news sites during data collection and reflect it in the data. For example, weather information from social media is collected and reflected in the data. Social media includes X (formerly Twitter) and Facebook. Weather information from news sites can also be collected and reflected in the data. Furthermore, information from social media and news sites can be collected in real time and reflected in the data. In this way, collecting information from social media and news sites improves the accuracy of the data.

[0107] 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, a simple, highly visible display method is provided. The analysis unit uses an emotion recognition algorithm to estimate the user's emotions. Emotion recognition algorithms include facial expression recognition, voice analysis, text analysis, etc. Furthermore, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the analysis results based on the user's emotions, a display that is easy for the user to view can be achieved.

[0108] The prediction unit can adjust the prediction results by taking into account the probability of occurrence of abnormal weather when making predictions. For example, the prediction results are adjusted by taking into account the probability of occurrence of abnormal weather. The probability of occurrence of abnormal weather includes statistical models and machine learning models. Statistical models include probability calculations based on past abnormal weather data. Machine learning models include models that have learned patterns of occurrence of abnormal weather. The prediction unit can also optimize the prediction results based on abnormal weather data. In this way, the accuracy of the prediction results is improved by taking into account the probability of occurrence of abnormal weather.

[0109] During configuration, the configuration unit can improve the accuracy of the configuration by referring to solar panel configuration data for other regions. For example, the configuration accuracy can be improved by referring to solar panel configuration data for other regions. Solar panel configuration data for other regions includes configuration data for neighboring regions, international configuration data, etc. The solar panel configuration can also be supplemented based on data for other regions. In this way, the accuracy of the configuration can be improved by referring to solar panel configuration data for other regions.

[0110] The maintenance unit can estimate the user's emotions and determine the priority of maintenance based on the estimated user's emotions. For example, if the user is feeling stressed, maintenance with high importance is given priority. The maintenance unit uses an emotion recognition algorithm to estimate the user's emotions. The emotion recognition algorithm includes facial expression recognition, voice analysis, text analysis, etc. Also, if the user is relaxed, detailed maintenance can be given priority. Furthermore, if the user is in a hurry, maintenance items that can be performed quickly can be given priority. In this way, by determining the priority of maintenance based on the user's emotions, important maintenance can be given priority.

[0111] The collection unit can detect abnormal weather and sudden weather changes in real time during data collection and reflect them in the data. For example, it can detect abnormal weather in real time and immediately reflect them in the collected data. Abnormal weather includes typhoons, heavy rain, and extreme heat. It can also detect sudden weather changes and adjust the frequency of data collection. Furthermore, when abnormal weather occurs, it can prioritize the collection of data from specific sensors. This improves the accuracy of data by detecting abnormal weather and sudden weather changes in real time.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The collection unit collects data. The data includes past weather data, sunshine data, and power consumption data. The collection unit obtains past weather data from a weather database, sunshine data from a sunshine meter, and power consumption data from a power company database. These data are collected automatically via API. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. The analysis unit preprocesses the data, complements missing values, extracts data features, and inputs them into an analysis model. Furthermore, the analysis unit uses machine learning algorithms to analyze data patterns. Step 3: The prediction unit predicts the angle and direction based on the data analyzed by the analysis unit. The prediction is performed using a prediction model or algorithm. The prediction unit uses an optimization algorithm to calculate the optimal angle and direction of the solar panel, and determines the optimal angle and direction based on the simulation results. It is also possible to improve the accuracy of the prediction by combining past data with current data. Step 4: The setting unit sets the solar panel based on the angle and direction predicted by the prediction unit. The setting includes adjusting the installation position and angle of the solar panel. The setting unit adjusts the installation position of the solar panel in real time to set it at the optimal angle. The installation angle is adjusted in real time to maximize sunlight. The solar panel is set up taking into account the installation environment. Step 5: The maintenance unit performs maintenance on the solar panels configured by the configuration unit. Maintenance includes cleaning, inspection, and repair of the solar panels. The maintenance unit periodically cleans the solar panels, inspects them to detect abnormalities, and repairs them to maintain power generation efficiency. The maintenance unit's processing can also be performed automatically using AI.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0157] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0158] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0170] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0171] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0175] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0176] 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.

[0177] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0178] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0179] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0180] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0183] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a prediction unit that predicts an angle and a direction based on the data analyzed by the analysis unit; a setting unit that sets the solar panel based on the angle and direction predicted by the prediction unit; a maintenance unit that performs maintenance on the solar panel set by the setting unit; Equipped with A system characterized by:

2. The collecting unit Collect historical weather or sunshine data 2. The system of claim 1.

3. The analysis unit Analyze the collected data in detail to predict the angle and direction 2. The system of claim 1.

4. The setting unit Set solar panels based on predicted angle and direction 2. The system of claim 1.

5. The maintenance unit Cleaning, inspecting, and repairing solar panels 2. The system of claim 1.

6. The maintenance unit Monitor the status of solar panels and automatically perform necessary maintenance 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Collecting local electricity consumption data in addition to historical weather and sunshine data 2. The system of claim 1.

Images