System

Abstract

An object of a system according to an embodiment is to efficiently perform water quality management and safety assurance.SOLUTION: A system includes a water quality monitoring part, an adjustment part, a drowning detection part, a warning part, and a filtering optimization part. The water quality monitoring unit monitors a water quality parameter. The adjustment unit ensures an appropriate water quality maintenance state based on the water quality parameter monitored by the water quality monitoring unit. The drowning detection unit utilizes an underwater camera and a sensor device to detect a situation where a person is likely to drown. The warning unit issues a warning for accident prevention on the basis of the drowning prone situation detected by the drowning detection unit. The filtering optimizer optimizes the water filtering and disinfection process.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to simultaneously manage water quality and ensure safety, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently manage water quality and ensure safety. [Means for solving the problem]

[0006] The system according to the embodiment includes a water quality monitoring unit, an adjustment unit, a drowning detection unit, a warning unit, and a filtering optimization unit. The water quality monitoring unit monitors water quality parameters. The adjustment unit ensures proper water quality maintenance based on the water quality parameters monitored by the water quality monitoring unit. The drowning detection unit detects drowning situations using an underwater camera and a sensor device. The warning unit issues a warning to prevent accidents based on the drowning situations detected by the drowning detection unit. The filtering optimization unit optimizes the water filtering and disinfection process. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage water quality and ensure safety. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The Aqua Guardian system according to an embodiment of the present invention utilizes AI to manage water quality and ensure safety in aquariums and swimming pools. The system monitors water quality parameters to ensure proper water quality. It also uses underwater cameras and sensor devices to detect drowning situations and issue warnings to prevent accidents. Furthermore, it optimizes water filtering and disinfection processes to maintain the cleanliness and safety of water bodies. This allows the Aqua Guardian system to efficiently and effectively manage water quality and ensure safety in aquariums and swimming pools.

[0029] The Aqua Guardian system according to the embodiment includes a water quality monitoring unit, an adjustment unit, a drowning detection unit, a warning unit, and a filtering optimization unit. The water quality monitoring unit monitors water quality parameters. For example, the water quality monitoring unit constantly monitors parameters such as pH, chlorine concentration, temperature, and oxygen concentration. The water quality monitoring unit can also issue a warning if these parameters fall outside of an appropriate range. The adjustment unit ensures that appropriate water quality is maintained based on the water quality parameters monitored by the water quality monitoring unit. For example, the adjustment unit automatically adjusts the pH value if it falls outside of an appropriate range. The drowning detection unit detects drowning-prone situations using an underwater camera and a sensor device. For example, the drowning detection unit analyzes a person's movements and posture and issues a warning if it detects abnormal movements. The warning unit issues a warning to prevent accidents based on the drowning-prone situations detected by the drowning detection unit. For example, the warning unit can issue an audio alarm or a visual warning. The filtering optimization unit optimizes the water filtering and disinfection process. For example, the filtering optimization unit analyzes water quality data and indicates the optimal timing for filtering and disinfection, allowing the Aqua no Moribito system to efficiently manage water quality and ensure safety.

[0030] The water quality monitoring unit learns fluctuation patterns of water quality parameters and builds predictive models to detect abnormalities in advance. For example, AI analyzes past water quality data to learn fluctuation patterns. For example, it models fluctuations in pH and chlorine concentration depending on the season and time of day, and predicts abnormalities before they occur. The water quality monitoring unit also builds predictive models to detect abnormalities in advance. For example, AI builds predictive models using regression analysis and machine learning models to detect abnormalities. This allows water quality abnormalities to be detected in advance and responded to quickly.

[0031] The water quality monitoring unit analyzes microbial activity in real time and can prevent abnormal microbial growth. For example, AI can analyze microbial activity in water in real time and detect abnormal growth patterns. For example, it can monitor the rapid growth of specific bacteria or algae. The water quality monitoring unit also uses an online monitoring system for real-time analysis. For example, it can analyze microbial activity using real-time data processing technology. This can prevent abnormal microbial growth and maintain water quality.

[0032] The drowning detection unit uses voice recognition technology to detect cries for help and can immediately issue a warning. For example, the drowning detection unit uses AI voice recognition technology to build a system that detects cries for help in real time. For example, it detects keywords such as "help" and "drowning." The drowning detection unit also uses deep learning-based voice recognition technology. For example, it uses keyword spotting technology to detect cries for help. This allows it to quickly detect cries for help and immediately issue a warning.

[0033] The drowning detection unit analyzes video data from underwater cameras and learns specific movement patterns, enabling it to accurately detect situations where drowning is likely to occur. For example, the drowning detection unit builds a system in which AI analyzes video data from underwater cameras and learns specific movement patterns. For example, it detects abnormal movements that occur before drowning. The drowning detection unit also analyzes the frame rate and resolution of the video data. For example, the AI ​​uses high-resolution video data to learn movement patterns. This enables it to accurately detect situations where drowning is likely to occur and respond quickly.

[0034] The filtering optimization unit can analyze water quality data and automatically adjust the filtering and disinfection processes. For example, the filtering optimization unit uses AI to analyze water quality data and develop algorithms that automatically adjust the filtering and disinfection processes. For example, it adjusts the filtering intensity according to the pH value and chlorine concentration. The filtering optimization unit also uses a feedback control system. For example, AI analyzes water quality data in real time and adjusts the filtering and disinfection processes. This allows the filtering and disinfection processes to be automatically adjusted and water quality to be optimized.

[0035] The filtering optimization unit optimizes the filtering and disinfection processes in real time based on water quality data, reducing energy consumption. For example, AI can be used to build a system that optimizes the filtering and disinfection processes in real time based on water quality data, reducing energy consumption. For example, filtering can be performed only when necessary. The filtering optimization unit also uses energy-efficient equipment, for example by optimizing operating hours. This makes it possible to optimize the filtering and disinfection processes while reducing energy consumption.

[0036] The filtering optimization unit utilizes nanotechnology to efficiently remove microscopic pollutants. The filtering optimization unit, for example, uses nanotechnology to build a system that makes the filtering and disinfection processes more efficient. For example, a nanofilter is used to remove microscopic pollutants. The filtering optimization unit also uses nanoparticles. For example, nanoparticles are used to remove viruses and microscopic plastics. This makes it possible to efficiently remove microscopic pollutants by utilizing nanotechnology.

[0037] The filtering optimization unit can centrally manage the filtering and disinfection processes using a cloud-based management system and enable remote operation. The filtering optimization unit, for example, builds a system that centrally manages the filtering and disinfection processes using a cloud-based management system and enables remote operation. For example, the operation is performed using a smartphone app. The filtering optimization unit also uses cloud services such as AWS and Azure. For example, remote operation is performed using a remote desktop or mobile app. This allows the filtering and disinfection processes to be centrally managed using a cloud-based management system and enabled remote operation.

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

[0039] The Aqua Guardian system can also be equipped with a health monitoring unit that monitors the health of aquatic life. The health monitoring unit analyzes changes in the behavioral patterns and appearance of aquatic life to detect abnormalities. For example, it monitors changes in the swimming style and appetite of fish to detect early signs of illness. The health monitoring unit can also use AI to compare data with past data and detect abnormalities. This allows the health of aquatic life to be maintained and appropriate measures to be taken.

[0040] The Aqua no Moribito system can also be equipped with a behavior analysis unit that analyzes visitor behavior. The behavior analysis unit analyzes visitor movement and length of stay to predict congestion levels. For example, if people are congregating in a particular area, it will guide them to other areas. The behavior analysis unit can also analyze visitor interests and concerns, which can be used to improve the exhibit content. This can improve the visitor experience.

[0041] The Aqua Guardian System can also be equipped with an acoustic monitoring unit that monitors the underwater acoustic environment. The acoustic monitoring unit analyzes underwater acoustic data and detects abnormal sounds. For example, it can detect the sound of a malfunctioning machine or the abnormal sounds of living creatures. The acoustic monitoring unit can also predict changes in the underwater environment based on the acoustic data. This makes it possible to maintain the underwater acoustic environment and detect abnormalities early.

[0042] The Aqua Guardian system can also be equipped with a plant monitoring unit that monitors the growth of underwater plants. The plant monitoring unit analyzes the growth status of underwater plants and maintains an appropriate environment. For example, it monitors light intensity and nutrient balance to maintain the health of plants. The plant monitoring unit can also use AI to compare data with past data and detect abnormalities in growth. This allows the health of underwater plants to be maintained and appropriate measures to be taken.

[0043] The Aqua Guardian System can also be equipped with a garbage collection unit that automatically collects garbage in the water. The garbage collection unit detects garbage in the water and collects it automatically. For example, it can use sensors to identify the location of the garbage and collect it with a robotic arm. The garbage collection unit can also use AI to identify the type of garbage and dispose of it appropriately. This allows for efficient collection of garbage in the water and maintains a clean environment.

[0044] The Aqua Guardian System can also be equipped with a temperature control unit to maintain a uniform temperature in the water. The temperature control unit monitors the temperature in the water in real time and makes adjustments to keep it uniform. For example, it uses a heater or cooler to adjust the temperature. The temperature control unit can also use AI to compare it with past data and maintain the optimal temperature. This helps maintain a uniform temperature in the water and maintain the health of the living organisms.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The water quality monitor monitors water quality parameters. For example, the water quality monitor constantly monitors parameters such as pH, chlorine concentration, temperature, and oxygen concentration. The water quality monitor can also issue an alert if these parameters fall outside of the appropriate range. Step 2: The adjusting unit ensures proper water quality maintenance based on the water quality parameters monitored by the water quality monitoring unit. For example, the adjusting unit automatically adjusts the pH value if it is outside the proper range. Step 3: The drowning detection unit uses underwater cameras and sensor devices to detect situations that could lead to drowning. For example, the drowning detection unit analyzes a person's movements and posture, and issues a warning if it detects abnormal movements. Step 4: The warning unit issues a warning for accident prevention based on the drowning-prone situation detected by the drowning detection unit. For example, the warning unit can issue an audio alarm or a visual warning. Step 5: The filtering optimization unit optimizes the water filtering and disinfection process. For example, the filtering optimization unit analyzes water quality data and indicates the optimal timing for filtering and disinfection.

[0047] (Example 2) The Aqua Guardian system according to an embodiment of the present invention utilizes AI to manage water quality and ensure safety in aquariums and swimming pools. The system monitors water quality parameters to ensure proper water quality. It also uses underwater cameras and sensor devices to detect drowning situations and issue warnings to prevent accidents. Furthermore, it optimizes water filtering and disinfection processes to maintain the cleanliness and safety of water bodies. This allows the Aqua Guardian system to efficiently and effectively manage water quality and ensure safety in aquariums and swimming pools.

[0048] The Aqua Guardian system according to the embodiment includes a water quality monitoring unit, an adjustment unit, a drowning detection unit, a warning unit, and a filtering optimization unit. The water quality monitoring unit monitors water quality parameters. For example, the water quality monitoring unit constantly monitors parameters such as pH, chlorine concentration, temperature, and oxygen concentration. The water quality monitoring unit can also issue a warning if these parameters fall outside of an appropriate range. The adjustment unit ensures that appropriate water quality is maintained based on the water quality parameters monitored by the water quality monitoring unit. For example, the adjustment unit automatically adjusts the pH value if it falls outside of an appropriate range. The drowning detection unit detects drowning-prone situations using an underwater camera and a sensor device. For example, the drowning detection unit analyzes a person's movements and posture and issues a warning if it detects abnormal movements. The warning unit issues a warning to prevent accidents based on the drowning-prone situations detected by the drowning detection unit. For example, the warning unit can issue an audio alarm or a visual warning. The filtering optimization unit optimizes the water filtering and disinfection process. For example, the filtering optimization unit analyzes water quality data and indicates the optimal timing for filtering and disinfection, allowing the Aqua no Moribito system to efficiently manage water quality and ensure safety.

[0049] The water quality monitoring unit learns fluctuation patterns of water quality parameters and builds predictive models to detect abnormalities in advance. For example, AI analyzes past water quality data to learn fluctuation patterns. For example, it models fluctuations in pH and chlorine concentration depending on the season and time of day, and predicts abnormalities before they occur. The water quality monitoring unit also builds predictive models to detect abnormalities in advance. For example, AI builds predictive models using regression analysis and machine learning models to detect abnormalities. This allows water quality abnormalities to be detected in advance and responded to quickly.

[0050] The water quality monitoring unit analyzes microbial activity in real time and can prevent abnormal microbial growth. For example, AI can analyze microbial activity in water in real time and detect abnormal growth patterns. For example, it can monitor the rapid growth of specific bacteria or algae. The water quality monitoring unit also uses an online monitoring system for real-time analysis. For example, it can analyze microbial activity using real-time data processing technology. This can prevent abnormal microbial growth and maintain water quality.

[0051] The drowning detection unit uses voice recognition technology to detect cries for help and can immediately issue a warning. For example, the drowning detection unit uses AI voice recognition technology to build a system that detects cries for help in real time. For example, it detects keywords such as "help" and "drowning." The drowning detection unit also uses deep learning-based voice recognition technology. For example, it uses keyword spotting technology to detect cries for help. This allows it to quickly detect cries for help and immediately issue a warning.

[0052] The drowning detection unit analyzes video data from underwater cameras and learns specific movement patterns, enabling it to accurately detect situations where drowning is likely to occur. For example, the drowning detection unit builds a system in which AI analyzes video data from underwater cameras and learns specific movement patterns. For example, it detects abnormal movements that occur before drowning. The drowning detection unit also analyzes the frame rate and resolution of the video data. For example, the AI ​​uses high-resolution video data to learn movement patterns. This enables it to accurately detect situations where drowning is likely to occur and respond quickly.

[0053] The filtering optimization unit can analyze water quality data and automatically adjust the filtering and disinfection processes. For example, the filtering optimization unit uses AI to analyze water quality data and develop algorithms that automatically adjust the filtering and disinfection processes. For example, it adjusts the filtering intensity according to the pH value and chlorine concentration. The filtering optimization unit also uses a feedback control system. For example, AI analyzes water quality data in real time and adjusts the filtering and disinfection processes. This allows the filtering and disinfection processes to be automatically adjusted and water quality to be optimized.

[0054] The filtering optimization unit optimizes the filtering and disinfection processes in real time based on water quality data, reducing energy consumption. For example, AI can be used to build a system that optimizes the filtering and disinfection processes in real time based on water quality data, reducing energy consumption. For example, filtering can be performed only when necessary. The filtering optimization unit also uses energy-efficient equipment, for example by optimizing operating hours. This makes it possible to optimize the filtering and disinfection processes while reducing energy consumption.

[0055] The filtering optimization unit can use the emotion estimation function to adjust the filtering and disinfection processes based on visitor emotion data, thereby improving visitor satisfaction. The filtering optimization unit, for example, uses the emotion estimation function to build a system that adjusts the filtering and disinfection processes based on visitor emotion data. For example, if visitor satisfaction is high, filtering is strengthened. The filtering optimization unit also uses facial expression recognition and voice analysis technology. For example, it analyzes visitor emotion data and voice data to obtain emotion data. This allows the filtering and disinfection processes to be adjusted to improve visitor satisfaction.

[0056] The filtering optimization unit utilizes nanotechnology to efficiently remove microscopic pollutants. The filtering optimization unit, for example, uses nanotechnology to build a system that makes the filtering and disinfection processes more efficient. For example, a nanofilter is used to remove microscopic pollutants. The filtering optimization unit also uses nanoparticles. For example, nanoparticles are used to remove viruses and microscopic plastics. This makes it possible to efficiently remove microscopic pollutants by utilizing nanotechnology.

[0057] The filtering optimization unit can centrally manage the filtering and disinfection processes using a cloud-based management system and enable remote operation. The filtering optimization unit, for example, builds a system that centrally manages the filtering and disinfection processes using a cloud-based management system and enables remote operation. For example, the operation is performed using a smartphone app. The filtering optimization unit also uses cloud services such as AWS and Azure. For example, remote operation is performed using a remote desktop or mobile app. This allows the filtering and disinfection processes to be centrally managed using a cloud-based management system and enabled remote operation.

[0058] The filtering optimization unit uses the emotion estimation function to optimize the filtering and disinfection processes based on visitor emotion data, thereby enhancing visitor security. The filtering optimization unit, for example, uses the emotion estimation function to build a system that optimizes the filtering and disinfection processes based on visitor emotion data. For example, it adjusts water quality to enhance visitor security. The filtering optimization unit also uses facial expression recognition and voice analysis technology. For example, it analyzes visitor emotion data and voice data to obtain emotion data. This allows the filtering and disinfection processes to be optimized to enhance visitor security.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The Aqua Guardian system can also be equipped with a health monitoring unit that monitors the health of aquatic life. The health monitoring unit analyzes changes in the behavioral patterns and appearance of aquatic life to detect abnormalities. For example, it monitors changes in the swimming style and appetite of fish to detect early signs of illness. The health monitoring unit can also use AI to compare data with past data and detect abnormalities. This allows the health of aquatic life to be maintained and appropriate measures to be taken.

[0061] The Aqua no Moribito system can also be equipped with a behavior analysis unit that analyzes visitor behavior. The behavior analysis unit analyzes visitor movement and length of stay to predict congestion levels. For example, if people are congregating in a particular area, it will guide them to other areas. The behavior analysis unit can also analyze visitor interests and concerns, which can be used to improve the exhibit content. This can improve the visitor experience.

[0062] The Aqua Guardian System can also be equipped with an acoustic monitoring unit that monitors the underwater acoustic environment. The acoustic monitoring unit analyzes underwater acoustic data and detects abnormal sounds. For example, it can detect the sound of a malfunctioning machine or the abnormal sounds of living creatures. The acoustic monitoring unit can also predict changes in the underwater environment based on the acoustic data. This makes it possible to maintain the underwater acoustic environment and detect abnormalities early.

[0063] The Aqua Guardian System can also be equipped with an exhibit adjustment unit that estimates the emotions of visitors and dynamically changes the exhibit content. The exhibit adjustment unit changes the exhibit content in real time based on the visitor's emotional data. For example, if a visitor is excited, a more stimulating exhibit will be provided. The exhibit adjustment unit also uses facial expression recognition and voice analysis technology. For example, it analyzes the visitor's facial expression data and voice data to obtain emotional data. This makes it possible to provide exhibit content that matches the visitor's emotions and improve satisfaction.

[0064] The Aqua Guardian System can also be equipped with an environmental adjustment unit that estimates the visitor's emotions and adjusts the lighting and music in the facility. The environmental adjustment unit adjusts the lighting and music in real time based on the visitor's emotional data. For example, if a visitor is relaxing, soft lighting and calm music are provided. The environmental adjustment unit also uses facial expression recognition and voice analysis technology. For example, it analyzes the visitor's facial expression data and voice data to obtain emotional data. This makes it possible to provide an environment that suits the visitor's emotions and provide a comfortable experience.

[0065] The Aqua Guardian System can also be equipped with a guide adjustment unit that estimates the visitor's emotions and dynamically changes the content of the guided tour. The guide adjustment unit changes the content of the guided tour in real time based on the visitor's emotional data. For example, it may provide detailed explanations about exhibits that interest the visitor. The guide adjustment unit also uses facial expression recognition and voice analysis technology. For example, it may analyze the visitor's facial expression data and voice data to obtain emotional data. This makes it possible to provide a guided tour that is tailored to the visitor's emotions, improving satisfaction.

[0066] The Aqua Guardian system can also be equipped with a menu adjustment unit that estimates the emotions of visitors and dynamically changes the menu in the dining area. The menu adjustment unit changes the menu in the dining area in real time based on the visitor's emotional data. For example, if a visitor is relaxed, it may offer light meals or desserts. The menu adjustment unit also uses facial recognition and voice analysis technology. For example, it analyzes the visitor's facial expression data and voice data to obtain emotional data. This makes it possible to provide a menu that matches the visitor's emotions and increase satisfaction.

[0067] The Aqua Guardian system can also be equipped with a plant monitoring unit that monitors the growth of underwater plants. The plant monitoring unit analyzes the growth status of underwater plants and maintains an appropriate environment. For example, it monitors light intensity and nutrient balance to maintain the health of plants. The plant monitoring unit can also use AI to compare data with past data and detect abnormalities in growth. This allows the health of underwater plants to be maintained and appropriate measures to be taken.

[0068] The Aqua Guardian System can also be equipped with a garbage collection unit that automatically collects garbage in the water. The garbage collection unit detects garbage in the water and collects it automatically. For example, it can use sensors to identify the location of the garbage and collect it with a robotic arm. The garbage collection unit can also use AI to identify the type of garbage and dispose of it appropriately. This allows for efficient collection of garbage in the water and maintains a clean environment.

[0069] The Aqua Guardian System can also be equipped with a temperature control unit to maintain a uniform temperature in the water. The temperature control unit monitors the temperature in the water in real time and makes adjustments to keep it uniform. For example, it uses a heater or cooler to adjust the temperature. The temperature control unit can also use AI to compare it with past data and maintain the optimal temperature. This helps maintain a uniform temperature in the water and maintain the health of the living organisms.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The water quality monitor monitors water quality parameters. For example, the water quality monitor constantly monitors parameters such as pH, chlorine concentration, temperature, and oxygen concentration. The water quality monitor can also issue an alert if these parameters fall outside of the appropriate range. Step 2: The adjusting unit ensures proper water quality maintenance based on the water quality parameters monitored by the water quality monitoring unit. For example, the adjusting unit automatically adjusts the pH value if it is outside the proper range. Step 3: The drowning detection unit uses underwater cameras and sensor devices to detect situations that could lead to drowning. For example, the drowning detection unit analyzes a person's movements and posture, and issues a warning if it detects abnormal movements. Step 4: The warning unit issues a warning for accident prevention based on the drowning-prone situation detected by the drowning detection unit. For example, the warning unit can issue an audio alarm or a visual warning. Step 5: The filtering optimization unit optimizes the water filtering and disinfection process. For example, the filtering optimization unit analyzes water quality data and indicates the optimal timing for filtering and disinfection.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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]

[0139] 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 water quality monitoring unit that monitors water quality parameters; an adjusting unit that ensures that the water quality is maintained at an appropriate level based on the water quality parameters monitored by the water quality monitoring unit; a drowning detection unit that detects situations that make it easy to drown using an underwater camera and a sensor device; a warning unit that issues a warning to prevent accidents based on the drowning-prone situation detected by the drowning detection unit; a filtering optimization unit that optimizes the water filtering and disinfection process. A system characterized by:

2. The water quality monitoring unit Learning the fluctuation patterns of the water quality parameters, building a predictive model to detect abnormalities in advance 2. The system of claim 1.

3. The drowning detection unit is Using voice recognition technology, the call for help is detected and the alert is issued immediately.

2. The system of claim 1.

4. The filtering optimization unit Analyzing water quality data and automatically adjusting the filtering and disinfection processes 2. The system of claim 1.

5. The filtering optimization unit Adjusting the filtering and disinfection process based on visitor sentiment data to improve visitor satisfaction.

2. The system of claim 1.

Images