System

Abstract

An object of a system according to an embodiment is to improve the efficiency of maintenance of a home or a small-scale solar power generation facility.SOLUTION: A system according to an embodiment includes an image analysis unit, an abnormality detection unit, a report generation unit, and a maintenance suggestion unit. The image analysis unit analyzes image data of a photovoltaic power generation facility. The abnormality detection unit detects an abnormal part from the image data of the photovoltaic power generation facility analyzed by the image analysis unit. The report generation unit generates an inspection result based on the abnormal part detected by the abnormality detection unit. The maintenance proposal unit proposes an optimum maintenance schedule based on the inspection result generated by the report generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not allow for efficient maintenance of home and small-scale solar power generation facilities, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency of maintenance of household or small-scale solar power generation facilities. [Means for solving the problem]

[0006] The system according to the embodiment includes an image analysis unit, an anomaly detection unit, a report generation unit, and a maintenance proposal unit. The image analysis unit analyzes image data of the photovoltaic power generation facility. The anomaly detection unit detects abnormal locations from the image data of the photovoltaic power generation facility analyzed by the image analysis unit. The report generation unit generates inspection results based on the abnormal locations detected by the anomaly detection unit. The maintenance proposal unit proposes an optimal maintenance schedule based on the inspection results generated by the report generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of maintenance of household or small-scale solar power generation facilities. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A maintenance system according to an embodiment of the present invention is a system that simplifies the maintenance of home and small-scale solar power generation facilities by using generative AI. As a result, the maintenance system can efficiently and simply maintain home and small-scale solar power generation facilities.

[0029] A maintenance system according to an embodiment includes an image analysis unit, an anomaly detection unit, a report generation unit, and a maintenance proposal unit. The image analysis unit analyzes image data of a solar power generation facility. For example, the image analysis unit analyzes image data captured by a drone or a fixed camera to detect abnormalities. The image analysis unit can also optimize the resolution and shooting angle of the image data. For example, the image analysis unit captures high-resolution images to improve the accuracy of detecting abnormalities. The anomaly detection unit detects abnormalities from the image data of the solar power generation facility analyzed by the image analysis unit. For example, the anomaly detection unit detects abnormalities such as dirt, damage, or deterioration of solar panels. The anomaly detection unit can also detect abnormalities using an infrared camera or a thermal imaging camera. For example, the anomaly detection unit detects temperature abnormalities in solar panels and identifies heat-generating locations. The report generation unit generates inspection results based on the abnormalities detected by the anomaly detection unit. For example, the report generation unit creates a detailed report including images of the abnormalities and analysis results, and provides the report to a user. The report generation unit can also propose repair methods for the abnormalities and display repair procedures. For example, the report generation unit provides detailed instructions on how to repair cracks in solar panels. The maintenance suggestion unit proposes an optimal maintenance schedule based on the inspection results generated by the report generation unit. For example, the maintenance suggestion unit proposes cleaning and repair timing based on the degree of dirt and deterioration of the solar panels. The maintenance suggestion unit can also predict the repair cost and time for abnormalities and propose an optimal maintenance schedule. For example, the maintenance suggestion unit calculates the cost of repairing cracks in solar panels and proposes the timing of the next repair. This enables the maintenance system according to the embodiment to efficiently and simply maintain the maintenance of home and small-scale solar power generation facilities. For example, users can monitor the status of the facility in real time through a smartphone app or web app and immediately respond if an abnormality is detected. Furthermore, following the maintenance schedule proposed by the generation AI can extend the life of the facility and maintain efficient power generation.

[0030] The image analysis unit can analyze image data taken by a drone or fixed camera to detect abnormalities. The image analysis unit can analyze image data taken by a drone or fixed camera to detect abnormalities. For example, the image analysis unit can analyze high-resolution images taken by a drone to detect dirt or damage on solar panels. The image analysis unit can also analyze image data taken by a fixed camera to detect deterioration of solar panels. For example, the image analysis unit can detect cracks or poor connections on solar panels based on image data taken by a fixed camera. This enables efficient inspections using drones and fixed cameras.

[0031] The report generation unit can create a detailed report including images of abnormal areas and analysis results and provide it to the user. The report generation unit, for example, creates a detailed report including images of abnormal areas and analysis results and provides it to the user. For example, the report generation unit creates a report including images of dirt and damage on solar panels and provides it to the user. The report generation unit can also suggest repair methods for abnormal areas and display repair procedures. For example, the report generation unit can explain in detail the procedures for repairing cracks in solar panels. This makes it possible to provide the user with detailed inspection results.

[0032] The maintenance suggestion unit can suggest the timing of cleaning or repair depending on the degree of dirt or deterioration of the solar panel. The maintenance suggestion unit suggests the timing of cleaning or repair depending on, for example, the degree of dirt or deterioration of the solar panel. For example, the maintenance suggestion unit suggests the timing of cleaning when the solar panel is heavily dirty. The maintenance suggestion unit can also suggest the timing of repair when the solar panel is severely deteriorated. For example, the maintenance suggestion unit suggests the timing of repair when cracks in the solar panel are progressing. This makes it possible to suggest an optimal maintenance schedule for the solar panel.

[0033] The system can constantly monitor the status of the solar power generation equipment using sensors or cameras and immediately notify if an abnormality is detected. The system can constantly monitor the status of the solar power generation equipment using sensors or cameras and immediately notify if an abnormality is detected. For example, the system can constantly monitor the temperature of the solar panels using sensors and immediately notify if a temperature abnormality is detected. The system can also constantly monitor the status of the solar panels using cameras and immediately notify if damage is detected. For example, the system can use a camera to detect cracks in the solar panels and immediately notify. This makes it possible to detect abnormalities in the solar power generation equipment in real time and respond quickly.

[0034] The system may provide a user with an easy-to-operate interface, allowing the user to check inspection results and maintenance schedules through a smartphone app or web app. The system may, for example, provide a user with an easy-to-operate interface, allowing the user to check inspection results and maintenance schedules through a smartphone app or web app. For example, the system may display the inspection results of a solar panel through a smartphone app and suggest repair methods for any abnormalities. The system may also display the maintenance schedule through a web app, allowing the user to check the timing of the next inspection. For example, the system may display the cleaning schedule for the solar panel through a web app, allowing the user to check the timing of the next cleaning. This allows the user to easily check the inspection results and maintenance schedule.

[0035] Generative AI can also be applied to inspecting other household electrical equipment (e.g., air conditioners, refrigerators) based on inspection data. For example, generative AI can inspect air conditioners based on inspection data. For example, generative AI can detect dirt on the air conditioner filter or refrigerant leaks and suggest repair methods. Generative AI can also inspect refrigerators. For example, generative AI can detect abnormalities in the refrigerator's cooling performance or piping and suggest repair methods. This makes it applicable to inspecting household electrical equipment.

[0036] Based on the inspection data, the generative AI can make suggestions to optimize energy consumption and improve the energy efficiency of the entire household. For example, the generative AI can make suggestions to optimize energy consumption for the entire household based on the inspection data. For example, the generative AI can suggest a cleaning schedule to maximize the power generation efficiency of solar panels. The generative AI can also make suggestions to optimize the energy consumption of air conditioners and refrigerators. For example, the generative AI can suggest optimizing the operation schedule of an air conditioner and reduce energy consumption. This can improve the energy efficiency of the entire household.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The maintenance system can further include a voice recognition unit. The voice recognition unit analyzes the user's voice commands and simplifies system operation. For example, if the user issues a voice command such as "Display the inspection results," the voice recognition unit analyzes the command and displays the inspection results. The voice recognition unit can also provide a voice notification of the next maintenance schedule when the user asks, "When is the next maintenance?" Furthermore, the voice recognition unit can also provide a voice explanation of the repair procedure when the user instructs, "Tell me how to repair the abnormality." This allows the user to operate the system and obtain maintenance information without using their hands.

[0039] The maintenance system can further include a predictive analysis unit. The predictive analysis unit predicts future abnormalities based on past inspection data. For example, the predictive analysis unit analyzes past solar panel deterioration data and predicts the areas most likely to deteriorate next. The predictive analysis unit can also predict when the next repair will be necessary based on past repair history. Furthermore, the predictive analysis unit can take weather data into account and predict when the solar panels will need to be cleaned. This allows users to create maintenance plans in advance and prepare to deal with sudden abnormalities.

[0040] The maintenance system can further include an energy management unit. The energy management unit analyzes power generation data from the solar power generation equipment and proposes optimization of energy consumption. For example, the energy management unit proposes a schedule that concentrates energy consumption during times when power generation is high. The energy management unit can also make proposals to reduce energy consumption during times when power generation is low. Furthermore, the energy management unit can analyze energy consumption data from other electrical equipment in the home (e.g., air conditioners, refrigerators) and make proposals to improve overall energy efficiency. This allows users to reduce energy costs and alleviate environmental impact.

[0041] The maintenance system may further include a user education unit. The user education unit provides educational content related to the maintenance of solar power generation equipment. For example, the user education unit may provide video tutorials that explain how to clean solar panels and the signs of deterioration. The user education unit may also provide guidelines that explain the importance of regular maintenance. Furthermore, the user education unit may provide interactive simulations that explain how to respond immediately if an abnormality occurs. This allows users to acquire the skills to perform simple maintenance themselves and extend the life of the equipment.

[0042] The maintenance system can further include a community collaboration unit, which provides a platform for users to share information with each other. For example, the community collaboration unit may provide a forum where users can post their own maintenance experiences and tips. The community collaboration unit may also provide a Q&A section where users can post questions and receive answers from other users and experts. Furthermore, the community collaboration unit may plan events for users to collaborate on maintenance and recruit participants. This allows users to share information with other users and deepen their knowledge about maintenance.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The image analysis unit analyzes image data from the solar power generation facility. For example, it can analyze image data taken by a drone or fixed camera and optimize the image data resolution and shooting angle. High-resolution images are taken, improving the accuracy of detecting abnormalities. Step 2: The anomaly detection unit detects abnormalities from the image data of the solar power generation facility analyzed by the image analysis unit. For example, it detects abnormalities such as dirt, damage, or deterioration of the solar panels, and uses an infrared camera or thermal imaging camera to detect temperature abnormalities and identify heat-generating areas. Step 3: The report generation unit generates inspection results based on the abnormalities detected by the anomaly detection unit. For example, it creates a detailed report including images of the abnormalities and analysis results and provides it to the user. It can also suggest repair methods for the abnormalities and display the repair procedures. For example, it can explain in detail the procedures for repairing cracks in solar panels. Step 4: The maintenance proposal unit proposes an optimal maintenance schedule based on the inspection results generated by the report generation unit. For example, it proposes the timing of cleaning and repairs depending on the degree of dirt and deterioration of the solar panels. It can also predict the repair costs and time required for abnormalities and propose an optimal maintenance schedule. For example, it calculates the cost of repairing a crack in a solar panel and proposes the timing of the next repair.

[0045] (Example 2) A maintenance system according to an embodiment of the present invention is a system that simplifies the maintenance of home and small-scale solar power generation facilities by using generative AI. As a result, the maintenance system can efficiently and simply maintain home and small-scale solar power generation facilities.

[0046] A maintenance system according to an embodiment includes an image analysis unit, an anomaly detection unit, a report generation unit, and a maintenance proposal unit. The image analysis unit analyzes image data of a solar power generation facility. For example, the image analysis unit analyzes image data captured by a drone or a fixed camera to detect abnormalities. The image analysis unit can also optimize the resolution and shooting angle of the image data. For example, the image analysis unit captures high-resolution images to improve the accuracy of detecting abnormalities. The anomaly detection unit detects abnormalities from the image data of the solar power generation facility analyzed by the image analysis unit. For example, the anomaly detection unit detects abnormalities such as dirt, damage, or deterioration of solar panels. The anomaly detection unit can also detect abnormalities using an infrared camera or a thermal imaging camera. For example, the anomaly detection unit detects temperature abnormalities in solar panels and identifies heat-generating locations. The report generation unit generates inspection results based on the abnormalities detected by the anomaly detection unit. For example, the report generation unit creates a detailed report including images of the abnormalities and analysis results, and provides the report to a user. The report generation unit can also propose repair methods for the abnormalities and display repair procedures. For example, the report generation unit provides detailed instructions on how to repair cracks in solar panels. The maintenance suggestion unit proposes an optimal maintenance schedule based on the inspection results generated by the report generation unit. For example, the maintenance suggestion unit proposes cleaning and repair timing based on the degree of dirt and deterioration of the solar panels. The maintenance suggestion unit can also predict the repair cost and time for abnormalities and propose an optimal maintenance schedule. For example, the maintenance suggestion unit calculates the cost of repairing cracks in solar panels and proposes the timing of the next repair. This enables the maintenance system according to the embodiment to efficiently and simply maintain the maintenance of home and small-scale solar power generation facilities. For example, users can monitor the status of the facility in real time through a smartphone app or web app and immediately respond if an abnormality is detected. Furthermore, following the maintenance schedule proposed by the generation AI can extend the life of the facility and maintain efficient power generation.

[0047] The image analysis unit can analyze image data taken by a drone or fixed camera to detect abnormalities. The image analysis unit can analyze image data taken by a drone or fixed camera to detect abnormalities. For example, the image analysis unit can analyze high-resolution images taken by a drone to detect dirt or damage on solar panels. The image analysis unit can also analyze image data taken by a fixed camera to detect deterioration of solar panels. For example, the image analysis unit can detect cracks or poor connections on solar panels based on image data taken by a fixed camera. This enables efficient inspections using drones and fixed cameras.

[0048] The report generation unit can create a detailed report including images of abnormal areas and analysis results and provide it to the user. The report generation unit, for example, creates a detailed report including images of abnormal areas and analysis results and provides it to the user. For example, the report generation unit creates a report including images of dirt and damage on solar panels and provides it to the user. The report generation unit can also suggest repair methods for abnormal areas and display repair procedures. For example, the report generation unit can explain in detail the procedures for repairing cracks in solar panels. This makes it possible to provide the user with detailed inspection results.

[0049] The maintenance suggestion unit can suggest the timing of cleaning or repair depending on the degree of dirt or deterioration of the solar panel. The maintenance suggestion unit suggests the timing of cleaning or repair depending on, for example, the degree of dirt or deterioration of the solar panel. For example, the maintenance suggestion unit suggests the timing of cleaning when the solar panel is heavily dirty. The maintenance suggestion unit can also suggest the timing of repair when the solar panel is severely deteriorated. For example, the maintenance suggestion unit suggests the timing of repair when cracks in the solar panel are progressing. This makes it possible to suggest an optimal maintenance schedule for the solar panel.

[0050] The system can constantly monitor the status of the solar power generation equipment using sensors or cameras and immediately notify if an abnormality is detected. The system can constantly monitor the status of the solar power generation equipment using sensors or cameras and immediately notify if an abnormality is detected. For example, the system can constantly monitor the temperature of the solar panels using sensors and immediately notify if a temperature abnormality is detected. The system can also constantly monitor the status of the solar panels using cameras and immediately notify if damage is detected. For example, the system can use a camera to detect cracks in the solar panels and immediately notify. This makes it possible to detect abnormalities in the solar power generation equipment in real time and respond quickly.

[0051] The system may provide a user with an easy-to-operate interface, allowing the user to check inspection results and maintenance schedules through a smartphone app or web app. The system may, for example, provide a user with an easy-to-operate interface, allowing the user to check inspection results and maintenance schedules through a smartphone app or web app. For example, the system may display the inspection results of a solar panel through a smartphone app and suggest repair methods for any abnormalities. The system may also display the maintenance schedule through a web app, allowing the user to check the timing of the next inspection. For example, the system may display the cleaning schedule for the solar panel through a web app, allowing the user to check the timing of the next cleaning. This allows the user to easily check the inspection results and maintenance schedule.

[0052] The emotion estimation function can generate a customized inspection report to alleviate the user's anxiety and concerns. The emotion estimation function generates, for example, an inspection report to alleviate the user's anxiety and concerns. For example, if the user is feeling anxious, the emotion estimation function creates a report including a message that provides reassurance. The emotion estimation function can also suggest specific measures to alleviate the user's concerns. For example, if the user is feeling concerned, the emotion estimation function creates a report including specific repair procedures. In this way, a customized inspection report can be provided to alleviate the user's anxiety and concerns.

[0053] Generative AI can also be applied to inspecting other household electrical equipment (e.g., air conditioners, refrigerators) based on inspection data. For example, generative AI can inspect air conditioners based on inspection data. For example, generative AI can detect dirt on the air conditioner filter or refrigerant leaks and suggest repair methods. Generative AI can also inspect refrigerators. For example, generative AI can detect abnormalities in the refrigerator's cooling performance or piping and suggest repair methods. This makes it applicable to inspecting household electrical equipment.

[0054] Based on the inspection data, the generative AI can make suggestions to optimize energy consumption and improve the energy efficiency of the entire household. For example, the generative AI can make suggestions to optimize energy consumption for the entire household based on the inspection data. For example, the generative AI can suggest a cleaning schedule to maximize the power generation efficiency of solar panels. The generative AI can also make suggestions to optimize the energy consumption of air conditioners and refrigerators. For example, the generative AI can suggest optimizing the operation schedule of an air conditioner and reduce energy consumption. This can improve the energy efficiency of the entire household.

[0055] The emotion estimation function can suggest an inspection frequency and method according to the user's emotion, thereby improving user satisfaction. The emotion estimation function, for example, suggests an inspection frequency according to the user's emotion. For example, if the user is feeling anxious, the emotion estimation function increases the inspection frequency to provide a sense of security. The emotion estimation function can also suggest an inspection method according to the user's emotion. For example, if the user is feeling concerned, the emotion estimation function suggests a detailed inspection method. This can improve user satisfaction.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The maintenance system can further include a voice recognition unit. The voice recognition unit analyzes the user's voice commands and simplifies system operation. For example, if the user issues a voice command such as "Display the inspection results," the voice recognition unit analyzes the command and displays the inspection results. The voice recognition unit can also provide a voice notification of the next maintenance schedule when the user asks, "When is the next maintenance?" Furthermore, the voice recognition unit can also provide a voice explanation of the repair procedure when the user instructs, "Tell me how to repair the abnormality." This allows the user to operate the system and obtain maintenance information without using their hands.

[0058] The maintenance system can further include a predictive analysis unit. The predictive analysis unit predicts future abnormalities based on past inspection data. For example, the predictive analysis unit analyzes past solar panel deterioration data and predicts the areas most likely to deteriorate next. The predictive analysis unit can also predict when the next repair will be necessary based on past repair history. Furthermore, the predictive analysis unit can take weather data into account and predict when the solar panels will need to be cleaned. This allows users to create maintenance plans in advance and prepare to deal with sudden abnormalities.

[0059] The maintenance system can further include an energy management unit. The energy management unit analyzes power generation data from the solar power generation equipment and proposes optimization of energy consumption. For example, the energy management unit proposes a schedule that concentrates energy consumption during times when power generation is high. The energy management unit can also make proposals to reduce energy consumption during times when power generation is low. Furthermore, the energy management unit can analyze energy consumption data from other electrical equipment in the home (e.g., air conditioners, refrigerators) and make proposals to improve overall energy efficiency. This allows users to reduce energy costs and alleviate environmental impact.

[0060] The maintenance system may further include a user education unit. The user education unit provides educational content related to the maintenance of solar power generation equipment. For example, the user education unit may provide video tutorials that explain how to clean solar panels and the signs of deterioration. The user education unit may also provide guidelines that explain the importance of regular maintenance. Furthermore, the user education unit may provide interactive simulations that explain how to respond immediately if an abnormality occurs. This allows users to acquire the skills to perform simple maintenance themselves and extend the life of the equipment.

[0061] The maintenance system can further include a community collaboration unit, which provides a platform for users to share information with each other. For example, the community collaboration unit may provide a forum where users can post their own maintenance experiences and tips. The community collaboration unit may also provide a Q&A section where users can post questions and receive answers from other users and experts. Furthermore, the community collaboration unit may plan events for users to collaborate on maintenance and recruit participants. This allows users to share information with other users and deepen their knowledge about maintenance.

[0062] The emotion estimation function can estimate a user's stress level and provide appropriate maintenance advice. For example, if a user is feeling highly stressed, the emotion estimation function can suggest a simple and quick maintenance procedure. Alternatively, if a user is relaxed, the emotion estimation function can suggest a detailed maintenance procedure. Furthermore, the emotion estimation function can adjust the frequency of maintenance according to the user's stress level. This allows the user to perform appropriate maintenance without feeling stressed.

[0063] The emotion estimation function can send customized notifications based on the user's emotions. For example, if the user is feeling anxious, the emotion estimation function can send a notification containing a reassuring message. Alternatively, if the user is excited, the emotion estimation function can send a notification containing a message encouraging the user to stay calm. Furthermore, the emotion estimation function can adjust the frequency and content of notifications according to the user's emotions. This allows the user to receive appropriate information according to their emotions.

[0064] The emotion estimation function can adjust the priority of maintenance based on the user's emotions. For example, if the user is feeling anxious, the emotion estimation function can prioritize and suggest important maintenance items. Alternatively, if the user is feeling relaxed, the emotion estimation function can suggest following a regular maintenance schedule. Furthermore, the emotion estimation function can provide detailed explanations of maintenance according to the user's emotions. This allows the user to perform appropriate maintenance according to their own emotions.

[0065] The emotion estimation function can adjust the timing of maintenance based on the user's emotions. For example, the emotion estimation function can delay the timing of maintenance when the user is feeling stressed. The emotion estimation function can also advance the timing of maintenance when the user is relaxed. Furthermore, the emotion estimation function can adjust the frequency of maintenance according to the user's emotions. This allows the user to perform maintenance at an appropriate time according to their own emotions.

[0066] The emotion estimation function can customize the maintenance method based on the user's emotions. For example, if the user is feeling anxious, the emotion estimation function can suggest a simple and quick maintenance procedure. Alternatively, if the user is feeling relaxed, the emotion estimation function can suggest a detailed maintenance procedure. Furthermore, the emotion estimation function can adjust the frequency of maintenance according to the user's emotions. This allows the user to perform appropriate maintenance according to their own emotions.

[0067] The processing flow of the second embodiment will be briefly explained below.

[0068] Step 1: The image analysis unit analyzes image data from the solar power generation facility. For example, it can analyze image data taken by a drone or fixed camera and optimize the image data resolution and shooting angle. High-resolution images are taken, improving the accuracy of detecting abnormalities. Step 2: The anomaly detection unit detects abnormalities from the image data of the solar power generation facility analyzed by the image analysis unit. For example, it detects abnormalities such as dirt, damage, or deterioration of the solar panels, and uses an infrared camera or thermal imaging camera to detect temperature abnormalities and identify heat-generating areas. Step 3: The report generation unit generates inspection results based on the abnormalities detected by the anomaly detection unit. For example, it creates a detailed report including images of the abnormalities and analysis results and provides it to the user. It can also suggest repair methods for the abnormalities and display the repair procedures. For example, it can explain in detail the procedures for repairing cracks in solar panels. Step 4: The maintenance proposal unit proposes an optimal maintenance schedule based on the inspection results generated by the report generation unit. For example, it proposes the timing of cleaning and repairs depending on the degree of dirt and deterioration of the solar panels. It can also predict the repair costs and time required for abnormalities and propose an optimal maintenance schedule. For example, it calculates the cost of repairing a crack in a solar panel and proposes the timing of the next repair.

[0069] 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.

[0070] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0071] 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.

[0072] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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).

[0078] 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.

[0079] 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.

[0080] 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.

[0081] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0082] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0083] 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.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0088] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] 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.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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.

[0102] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] 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.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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."

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0135] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0136] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image analysis unit that analyzes image data of the solar power generation facility; an abnormality detection unit that detects an abnormality location from the image data of the photovoltaic power generation facility analyzed by the image analysis unit; a report generation unit that generates an inspection result based on the abnormality location detected by the abnormality detection unit; a maintenance proposal unit that proposes an optimal maintenance schedule based on the inspection results generated by the report generation unit. A system characterized by:

2. The image analysis unit The system according to claim 1, characterized in that the image data taken by a drone or a fixed camera is analyzed to detect the abnormal area.

3. The report generation unit The system according to claim 1, wherein a detailed report including images of the abnormal area and analysis results is created and provided to the user.

4. The system comprises: The status of the solar power generation equipment is constantly monitored using sensors or cameras, and if an abnormality is detected, a notification is sent immediately.

2. The system of claim 1.

5. The system comprises: Generate customized inspection reports to alleviate user anxieties or concerns 2. The system of claim 1.

Images