System

Abstract

An object of a system according to an embodiment is to monitor water quality of a water area in real time and quickly detect an abnormality.SOLUTION: A system according to an embodiment includes a sensor device, a data collection unit, an analysis unit, and an evaluation unit. The sensor device collects water quality data. The data collector collects the water quality data collected by the sensor device. The analysis unit analyzes the water quality data collected by the data collection unit. The evaluation unit evaluates the water quality data analyzed by the analysis 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 has had the problem of making it difficult to monitor the water quality of a body of water in real time and quickly detect abnormalities.

[0005] The system according to the embodiment aims to monitor the water quality of a water body in real time and quickly detect abnormalities. [Means for solving the problem]

[0006] The system according to the embodiment includes a sensor device, a data collection unit, an analysis unit, and an evaluation unit. The sensor device collects water quality data. The data collection unit collects the water quality data collected by the sensor device. The analysis unit analyzes the water quality data collected by the data collection unit. The evaluation unit evaluates the water quality data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can monitor the water quality of a water body in real time and quickly detect abnormalities. [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 water quality monitoring system according to an embodiment of the present invention uses sensor devices to collect water quality data from a body of water in real time, and then analyzes and evaluates the data using AI. This allows the water quality monitoring system to detect abnormal water quality patterns in a body of water, identify pollution sources, predict impacts on wildlife, and propose appropriate measures.

[0029] A water quality monitoring system according to an embodiment includes a sensor device, a data collection unit, an analysis unit, and an evaluation unit. The sensor device is installed in a body of water and collects water quality data, such as water temperature, pH, dissolved oxygen, turbidity, and conductivity, in real time. For example, the sensor device is installed in a body of water, such as a river, lake, or ocean, and periodically collects data. This allows for continuous monitoring of fluctuations in water quality. The data collection unit collects the water quality data collected by the sensor device. For example, the data collection unit receives data transmitted from the sensor device and stores it in a database. The data collection unit can also preprocess the collected data and pass it to the analysis unit. The analysis unit analyzes the water quality data collected by the data collection unit. For example, the analysis unit uses AI to analyze the water quality data and detect abnormal patterns. The AI ​​compares the data with past data to detect sudden changes that differ from normal water quality patterns or increases in the concentration of specific harmful substances. The evaluation unit evaluates the water quality data analyzed by the analysis unit. For example, the evaluation unit identifies pollution sources and predicts their impact on wildlife based on abnormal patterns detected by the AI. The AI ​​estimates the location and type of pollution sources based on past data and geographic information. The AI ​​also uses an ecosystem model to simulate the impact of water quality fluctuations on the ecosystem. This allows the water quality monitoring system according to the embodiment to collect, analyze, and evaluate water quality data in real time. For example, the output unit displays the evaluation results to relevant organizations and residents via a web application or mobile application. If feedback on paper is desired, the results are printed using a printer. Sending the results via email provides rapid feedback by sending the results directly to relevant organizations and residents.

[0030] Sensor devices can be mounted on drones or autonomous underwater robots to collect water quality data over a wide area and across multiple layers. For example, a sensor device can be mounted on a drone to collect water quality data while flying over a wide area of ​​water. For example, the sensor device can monitor the water quality of an entire river and detect abnormal patterns. Alternatively, a sensor device can be mounted on an autonomous underwater robot to collect multiple layers of water quality data while moving underwater. For example, water quality data can be collected by collecting water quality data at each depth and comparing the water quality of different layers. Sensor devices can also be combined with a drone and an autonomous underwater robot to collect water quality data over a wide area and across multiple layers. For example, a drone can collect water quality data over a wide area from the sky, and an autonomous underwater robot can collect multiple layers of data from underwater. This allows the collection of water quality data over a wide area and across multiple layers.

[0031] Sensor devices have a self-repair function, enabling stable data collection over a long period of time. For example, a sensor device may be equipped with a self-repair function and automatically repair itself in the event of a failure or damage. For example, if the sensor surface is scratched, it can be repaired using a self-repair material. Sensor devices may also have a self-diagnosis function and automatically repair themselves if they detect signs of failure. For example, they may constantly monitor the operating status of the sensor and perform self-repair if an abnormality is detected. Sensor devices may also have an automatic part replacement function and automatically replace broken parts. For example, if part of the sensor breaks down, it can automatically replace it with a spare part. This enables stable data collection over a long period of time.

[0032] Sensor devices can also be applied to monitoring agricultural water and industrial water, enabling water quality management for different purposes. For example, sensor devices can be applied to monitoring agricultural water to monitor the effects of pesticides and fertilizers. For example, they can measure the pH and dissolved oxygen of agricultural water in real time. Sensor devices can also be applied to monitoring industrial water to monitor the quality of industrial wastewater. For example, they can measure the conductivity and concentration of harmful substances in industrial water. Sensor devices can also be applied to monitoring both agricultural water and industrial water, enabling water quality management for different purposes. For example, water quality data from agricultural water and industrial water can be compared to manage water quality for different purposes. This makes it possible to manage water quality for different purposes.

[0033] Sensor devices can be installed in urban drainage systems to manage water quality throughout the city. For example, sensor devices can be installed in urban drainage systems to monitor the quality of wastewater in real time. For example, sensors can be installed in sewers and drainage pipes to measure the concentration of harmful substances. Sensor devices can also be installed in urban drainage systems to collect water quality data at multiple points. For example, sensors can be installed in multiple drainage pipes within a city to collect water quality data at each point. Sensor devices can also be linked to urban drainage systems to optimize the operation of wastewater treatment facilities. For example, they can monitor the operating status of wastewater treatment facilities in real time and adjust operating conditions as needed. This makes it possible to manage water quality throughout the city.

[0034] When detecting an anomaly, the analysis unit can integrate past meteorological data and geographical information to analyze the cause of the anomaly from multiple angles. For example, when detecting an anomaly, the analysis unit integrates past meteorological data to analyze the cause of the anomaly. For example, it evaluates whether an abnormal water quality pattern is related to meteorological conditions. The analysis unit can also integrate geographical information to analyze the cause of the anomaly. For example, it can identify the location of a pollution source based on geographical information and analyze the cause of the anomaly. The analysis unit can also combine meteorological data and geographical information to analyze the cause of the anomaly from multiple angles. For example, it can identify the cause of an abnormal water quality pattern based on meteorological data and geographical information. This allows for a multifaceted analysis of the cause of the anomaly.

[0035] The analysis unit can integrate the data with other environmental data to perform a comprehensive environmental assessment. The analysis unit can, for example, integrate the data with other environmental data (e.g., air quality data and soil data) to perform a comprehensive environmental assessment. For example, water quality data and air quality data can be integrated to perform a comprehensive environmental assessment. The analysis unit can also integrate soil data to perform a comprehensive environmental assessment. For example, water quality data and soil data can be integrated to perform a comprehensive environmental assessment. The analysis unit can also combine multiple pieces of environmental data to perform a comprehensive environmental assessment. For example, water quality data, air quality data, and soil data can be integrated to perform a comprehensive environmental assessment. This allows for a comprehensive environmental assessment.

[0036] The analysis unit can visualize the results of anomaly detection and provide a dashboard that the user can intuitively understand. The analysis unit, for example, visualizes the results of anomaly detection and provides a dashboard that the user can intuitively understand. For example, abnormal water quality patterns are displayed in graphs and charts. The analysis unit can also update the results of anomaly detection in real time to provide the user with the latest information. For example, the analysis unit can update the results of anomaly detection in real time on a dashboard to provide the user with the latest information. The analysis unit can also display the results of anomaly detection interactively to allow the user to check detailed information. For example, detailed data can be displayed by clicking on a graph or chart. This visualizes the results of anomaly detection and allows the user to intuitively understand it.

[0037] The evaluation unit can identify the location of the pollution source with high accuracy using a geographic information system (GIS). The evaluation unit, for example, uses a geographic information system (GIS) to identify the location of the pollution source with high accuracy. For example, the location of the pollution source is identified based on GIS data and displayed on a map. The evaluation unit can also identify the location of the pollution source by combining GIS data with water quality data. For example, the evaluation unit can identify the location of the pollution source by combining abnormal patterns in water quality data with GIS data. The evaluation unit can also update the location of the pollution source in real time using GIS data. For example, if the location of the pollution source changes, the location information is updated based on GIS data. This allows the location of the pollution source to be identified with high accuracy.

[0038] The evaluation unit can refer to the database of past contamination cases and detect similar patterns to identify the source of contamination. The evaluation unit, for example, refers to the database of past contamination cases and detects similar patterns to identify the source of contamination. For example, the evaluation unit identifies the source of contamination based on past data. The evaluation unit can also use a pattern matching algorithm to compare past contamination cases with current data to detect similar patterns. For example, the evaluation unit can use a pattern matching algorithm to compare past contamination cases with current data to detect similar patterns. The evaluation unit can also use the database of past contamination cases to set criteria for identifying the source of contamination. For example, the evaluation unit sets criteria for identifying the source of contamination based on past data. This makes it possible to refer to the database of past contamination cases and detect similar patterns to identify the source of contamination.

[0039] The evaluation unit can be expanded to identify different types of pollution sources. For example, the evaluation unit can be expanded to identify different types of pollution sources (e.g., air pollution sources and soil pollution sources). For example, the evaluation unit can analyze air quality data and soil data to identify pollution sources. The evaluation unit can also develop algorithms for identifying different types of pollution sources. For example, the evaluation unit can develop algorithms for identifying air pollution sources and soil pollution sources. The evaluation unit can also build a database for identifying different types of pollution sources. For example, the evaluation unit can build a database of air pollution sources and soil pollution sources and identify pollution sources based on that data. In this way, the evaluation unit can be expanded to identify different types of pollution sources.

[0040] The evaluation unit can build a system that notifies relevant organizations and residents of the results of identifying the pollution source in real time. The evaluation unit, for example, builds a system that notifies relevant organizations and residents of the results of identifying the pollution source in real time. For example, the evaluation unit may send emails or app notifications to relevant organizations and residents. The evaluation unit can also provide multiple notification methods so that relevant organizations and residents can choose from. For example, methods such as email notifications, app notifications, and voice alerts can be provided so that relevant organizations and residents can choose from. The evaluation unit can also customize the content of the notifications and provide appropriate information to relevant organizations and residents. For example, the evaluation unit can customize the content of the notifications depending on the results of identifying the pollution source and provide appropriate information to relevant organizations and residents. In this way, a system can be built that notifies relevant organizations and residents of the results of identifying the pollution source in real time.

[0041] The evaluation unit can use the ecosystem model to simulate multiple scenarios and propose measures with the least impact. The evaluation unit, for example, uses the ecosystem model to simulate multiple scenarios and propose measures with the least impact. For example, it compares different countermeasure scenarios and selects the optimal countermeasure. The evaluation unit can also use the ecosystem model to evaluate the effectiveness of the countermeasure based on the simulation results. For example, it evaluates the effectiveness of the countermeasure based on the simulation results and proposes the optimal countermeasure. The evaluation unit can also use the ecosystem model to update the simulation results in real time. For example, it updates the simulation results in real time and proposes countermeasures based on the latest information. In this way, it is possible to simulate multiple scenarios and propose measures with the least impact.

[0042] The assessment unit can be expanded so that it can predict impacts on other ecosystems. The assessment unit is expanded so that it can predict impacts on other ecosystems (for example, forests and wetlands). For example, a forest ecosystem model is used to predict impacts. The assessment unit can also predict impacts using a wetland ecosystem model. For example, a wetland ecosystem model is used to predict impacts. The assessment unit can also combine multiple ecosystem models to predict impacts on other ecosystems. For example, a forest and wetland ecosystem model are combined to predict impacts. In this way, the system can be expanded so that it can predict impacts on other ecosystems.

[0043] The evaluation department can share the results of the impact prediction with educational institutions and research institutions to promote collaborative research. The evaluation department, for example, shares the results of the impact prediction with educational institutions and research institutions to promote collaborative research. For example, it can launch a collaborative research project based on the prediction results. The evaluation department can also build a platform for sharing the results of the impact prediction. For example, it can build an online platform and share the prediction results with educational institutions and research institutions. The evaluation department can also set a collaborative research theme based on the results of the impact prediction. For example, it can set a collaborative research theme based on the prediction results and advance research. In this way, the results of the impact prediction can be shared with educational institutions and research institutions to promote collaborative research.

[0044] The evaluation unit can refer to past success cases and propose the most effective measures. The evaluation unit, for example, refers to past success cases and proposes the most effective measures. For example, the optimal measures are selected based on past data. The evaluation unit can also build a database of success cases and refer to past success cases. For example, the evaluation unit can build a database of success cases and propose the optimal measures based on that data. The evaluation unit can also evaluate the effectiveness of measures based on success cases. For example, the evaluation unit can evaluate the effectiveness of measures based on success cases and propose the optimal measures. In this way, the most effective measures can be proposed by referring to past success cases.

[0045] The evaluation unit can simulate the feasibility of proposed measures and select the optimal measures. The evaluation unit, for example, simulates the feasibility of proposed measures and selects the optimal measures. For example, it determines the priority of measures based on the simulation results. The evaluation unit can also build a simulation model for evaluating feasibility. For example, it builds a simulation model for evaluating feasibility and selects the optimal measures using the model. The evaluation unit can also evaluate the effectiveness of measures based on the results of the simulation. For example, it evaluates the effectiveness of measures based on the results of the simulation and selects the optimal measures. In this way, it is possible to simulate the feasibility of proposed measures and select the optimal measures.

[0046] The evaluation unit can be expanded so that it can propose measures for different environmental problems. The evaluation unit can be expanded, for example, so that it can propose measures for different environmental problems (e.g., air pollution and soil pollution). For example, it analyzes air quality data and soil data and proposes optimal measures. The evaluation unit can also develop algorithms for proposing measures for different environmental problems. For example, it develops algorithms for proposing measures for air pollution and soil pollution. The evaluation unit can also build a database for proposing measures for different environmental problems. For example, it builds a database for air pollution and soil pollution and proposes optimal measures based on that data. In this way, the evaluation unit can be expanded so that it can propose measures for different environmental problems.

[0047] The evaluation unit can build a system that monitors the effectiveness of proposed measures and improves the measures based on feedback. The evaluation unit, for example, builds a system that monitors the effectiveness of proposed measures and improves the measures based on feedback. For example, it collects data after the measures are implemented and evaluates their effectiveness. The evaluation unit can also conduct a questionnaire survey to collect feedback. For example, it conducts a questionnaire survey of residents and related organizations and collects feedback. The evaluation unit can also analyze sensor data and evaluate the effectiveness of the measures. For example, it analyzes sensor data, evaluates the effectiveness of the measures, and improves the measures based on feedback. In this way, it is possible to build a system that monitors the effectiveness of proposed measures and improves the measures based on feedback.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The water quality monitoring system can further include a prediction unit. The prediction unit predicts future water quality fluctuations based on the collected water quality data. For example, it combines past data and current data to predict future water quality fluctuations. The prediction unit can also integrate meteorological data and geographical information to make more accurate predictions. For example, it can predict water quality fluctuations based on meteorological data, taking into account fluctuations in rainfall and temperature. The prediction unit can also provide the prediction results to relevant organizations and residents to encourage them to take advance measures. For example, it can propose appropriate measures to relevant organizations based on the prediction results. This makes it possible to take advance measures against future water quality fluctuations.

[0050] Sensor devices can also be equipped with energy-saving modes to further improve energy efficiency. For example, they can adjust the frequency of data collection and collect data at high frequencies only when necessary. Sensor devices can also be self-charged using solar or wind power. For example, they can be equipped with solar panels and charge during the day. Sensor devices can also transmit data using low-power communication technologies. For example, low-power communication technologies such as LoRa and NB-IoT are used to transmit data efficiently. This improves energy efficiency and enables long-term operation.

[0051] Sensor devices can be equipped with encryption functions to further strengthen data security. For example, collected data can be encrypted before transmission to prevent unauthorized access. Sensor devices can also use hash functions to detect data tampering. For example, by calculating a hash value of the data and adding it when transmitting, the integrity of the data can be confirmed on the receiving side. Sensor devices can also be equipped with authentication functions to ensure that only authorized devices can transmit data. For example, an authentication key can be set for each device, so that only authenticated devices can transmit data. This strengthens data security.

[0052] Sensor devices also have self-learning capabilities, allowing them to automatically optimize parameters based on collected data. For example, they can adjust the sensitivity of sensors based on past data to improve the accuracy of anomaly detection. Sensor devices can also automatically change settings in response to changes in the environment. For example, they can adjust the frequency and items of data collection according to changes in the season or weather. Sensor devices can also use their self-learning capabilities to detect signs of malfunction and perform maintenance in advance. For example, they can learn the sensor's operating patterns and issue an alert if abnormal operation is detected. This improves the performance of sensor devices and enables them to operate for a long period of time.

[0053] When detecting an anomaly, the analysis unit can integrate past meteorological data and geographical information to perform a multifaceted analysis of the cause of the anomaly. For example, when detecting an anomaly, past meteorological data can be integrated to analyze the cause of the anomaly. For example, whether an abnormal water quality pattern is related to meteorological conditions can be evaluated. The analysis unit can also integrate geographical information to analyze the cause of the anomaly. For example, the location of a pollution source can be identified based on geographical information and the cause of the anomaly can be analyzed. The analysis unit can also combine meteorological data and geographical information to perform a multifaceted analysis of the cause of the anomaly. For example, the cause of an abnormal water quality pattern can be identified based on meteorological data and geographical information. This allows for a multifaceted analysis of the cause of the anomaly.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: Sensor devices are installed in bodies of water to collect water quality data such as water temperature, pH, dissolved oxygen, turbidity, and conductivity in real time. For example, sensor devices are installed in bodies of water such as rivers, lakes, and oceans, and data is collected periodically. This allows for continuous monitoring of fluctuations in water quality. Step 2: The data collection unit collects the water quality data collected by the sensor devices. For example, the data collection unit receives the data sent from the sensor devices and stores it in a database. The data collection unit can also preprocess the collected data and pass it to the analysis unit. Step 3: The analysis unit analyzes the water quality data collected by the data collection unit. For example, the analysis unit uses AI to analyze the water quality data and detect abnormal patterns. The AI ​​compares the data with past data and detects sudden changes that differ from normal water quality patterns or increases in the concentration of specific harmful substances. Step 4: The evaluation unit evaluates the water quality data analyzed by the analysis unit. For example, the evaluation unit identifies pollution sources and predicts their impact on wildlife based on abnormal patterns detected by the AI. The AI ​​estimates the location and type of pollution source based on past data and geographic information. The AI ​​also uses an ecosystem model to simulate the impact of water quality fluctuations on the ecosystem.

[0056] (Example 2) The water quality monitoring system according to an embodiment of the present invention uses sensor devices to collect water quality data from a body of water in real time, and then analyzes and evaluates the data using AI. This allows the water quality monitoring system to detect abnormal water quality patterns in a body of water, identify pollution sources, predict impacts on wildlife, and propose appropriate measures.

[0057] A water quality monitoring system according to an embodiment includes a sensor device, a data collection unit, an analysis unit, and an evaluation unit. The sensor device is installed in a body of water and collects water quality data, such as water temperature, pH, dissolved oxygen, turbidity, and conductivity, in real time. For example, the sensor device is installed in a body of water, such as a river, lake, or ocean, and periodically collects data. This allows for continuous monitoring of fluctuations in water quality. The data collection unit collects the water quality data collected by the sensor device. For example, the data collection unit receives data transmitted from the sensor device and stores it in a database. The data collection unit can also preprocess the collected data and pass it to the analysis unit. The analysis unit analyzes the water quality data collected by the data collection unit. For example, the analysis unit uses AI to analyze the water quality data and detect abnormal patterns. The AI ​​compares the data with past data to detect sudden changes that differ from normal water quality patterns or increases in the concentration of specific harmful substances. The evaluation unit evaluates the water quality data analyzed by the analysis unit. For example, the evaluation unit identifies pollution sources and predicts their impact on wildlife based on abnormal patterns detected by the AI. The AI ​​estimates the location and type of pollution sources based on past data and geographic information. The AI ​​also uses an ecosystem model to simulate the impact of water quality fluctuations on the ecosystem. This allows the water quality monitoring system according to the embodiment to collect, analyze, and evaluate water quality data in real time. For example, the output unit displays the evaluation results to relevant organizations and residents via a web application or mobile application. If feedback on paper is desired, the results are printed using a printer. Sending the results via email provides rapid feedback by sending the results directly to relevant organizations and residents.

[0058] The sensor device is equipped with an emotion estimation function and can collect emotion data in response to the surrounding environment and human activities. For example, the sensor device is equipped with an emotion estimation function and collects emotion data in response to the surrounding environment and human activities. For example, emotion data of residents in the area where the sensor is installed is collected and analyzed to determine whether a specific emotion is related to fluctuations in water quality. The sensor device can also analyze the facial expressions of residents using facial recognition technology to collect emotion data. For example, the facial recognition technology can be used to estimate emotions from the facial expressions of residents and collect the data. The sensor device can also analyze the tone and speed of residents' voices using voice analysis technology to collect emotion data. For example, the voice analysis technology can be used to estimate emotions from the tone and speed of residents' voices and collect the data. By collecting emotion data, the cause of water quality fluctuations can be identified in more detail.

[0059] Sensor devices can be mounted on drones or autonomous underwater robots to collect water quality data over a wide area and across multiple layers. For example, a sensor device can be mounted on a drone to collect water quality data while flying over a wide area of ​​water. For example, the sensor device can monitor the water quality of an entire river and detect abnormal patterns. Alternatively, a sensor device can be mounted on an autonomous underwater robot to collect multiple layers of water quality data while moving underwater. For example, water quality data can be collected by collecting water quality data at each depth and comparing the water quality of different layers. Sensor devices can also be combined with a drone and an autonomous underwater robot to collect water quality data over a wide area and across multiple layers. For example, a drone can collect water quality data over a wide area from the sky, and an autonomous underwater robot can collect multiple layers of data from underwater. This allows the collection of water quality data over a wide area and across multiple layers.

[0060] Sensor devices have a self-repair function, enabling stable data collection over a long period of time. For example, a sensor device may be equipped with a self-repair function and automatically repair itself in the event of a failure or damage. For example, if the sensor surface is scratched, it can be repaired using a self-repair material. Sensor devices may also have a self-diagnosis function and automatically repair themselves if they detect signs of failure. For example, they may constantly monitor the operating status of the sensor and perform self-repair if an abnormality is detected. Sensor devices may also have an automatic part replacement function and automatically replace broken parts. For example, if part of the sensor breaks down, it can automatically replace it with a spare part. This enables stable data collection over a long period of time.

[0061] Sensor devices can also be applied to monitoring agricultural water and industrial water, enabling water quality management for different purposes. For example, sensor devices can be applied to monitoring agricultural water to monitor the effects of pesticides and fertilizers. For example, they can measure the pH and dissolved oxygen of agricultural water in real time. Sensor devices can also be applied to monitoring industrial water to monitor the quality of industrial wastewater. For example, they can measure the conductivity and concentration of harmful substances in industrial water. Sensor devices can also be applied to monitoring both agricultural water and industrial water, enabling water quality management for different purposes. For example, water quality data from agricultural water and industrial water can be compared to manage water quality for different purposes. This makes it possible to manage water quality for different purposes.

[0062] Sensor devices can be installed in urban drainage systems to manage water quality throughout the city. For example, sensor devices can be installed in urban drainage systems to monitor the quality of wastewater in real time. For example, sensors can be installed in sewers and drainage pipes to measure the concentration of harmful substances. Sensor devices can also be installed in urban drainage systems to collect water quality data at multiple points. For example, sensors can be installed in multiple drainage pipes within a city to collect water quality data at each point. Sensor devices can also be linked to urban drainage systems to optimize the operation of wastewater treatment facilities. For example, they can monitor the operating status of wastewater treatment facilities in real time and adjust operating conditions as needed. This makes it possible to manage water quality throughout the city.

[0063] The sensor device is equipped with an emotion estimation function and can collect emotion data of local residents, which can be used to improve environmental awareness in the area. For example, the sensor device is equipped with an emotion estimation function and collects emotion data of local residents. For example, a campaign to improve environmental awareness can be carried out based on the emotion data of residents. The sensor device can also use facial recognition technology to analyze residents' facial expressions and collect emotion data. For example, facial recognition technology can be used to estimate emotions from residents' facial expressions and collect this data. The sensor device can also use voice analysis technology to analyze the tone and speed of residents' voices and collect emotion data. For example, voice analysis technology can be used to estimate emotions from the tone and speed of residents' voices and collect this data. This can be used to improve environmental awareness in the area.

[0064] The analysis unit can use the emotion estimation function to analyze residents' emotional responses to the collected data and evaluate their level of interest in environmental issues. The analysis unit can, for example, use the emotion estimation function to analyze residents' emotional responses to collected water quality data. For example, the analysis unit can evaluate the level of interest in environmental issues based on residents' emotional data. The analysis unit can also use facial recognition technology to analyze residents' facial expressions and analyze their emotional responses. For example, the analysis unit can use facial recognition technology to estimate emotions from residents' facial expressions and analyze the data. The analysis unit can also use voice analysis technology to analyze residents' tone and speed of voice and analyze their emotional responses. For example, the analysis unit can use voice analysis technology to estimate emotions from residents' tone and speed of voice and analyze the data. In this way, the analysis of residents' emotional responses can be used to evaluate their level of interest in environmental issues.

[0065] When detecting an anomaly, the analysis unit can integrate past meteorological data and geographical information to analyze the cause of the anomaly from multiple angles. For example, when detecting an anomaly, the analysis unit integrates past meteorological data to analyze the cause of the anomaly. For example, it evaluates whether an abnormal water quality pattern is related to meteorological conditions. The analysis unit can also integrate geographical information to analyze the cause of the anomaly. For example, it can identify the location of a pollution source based on geographical information and analyze the cause of the anomaly. The analysis unit can also combine meteorological data and geographical information to analyze the cause of the anomaly from multiple angles. For example, it can identify the cause of an abnormal water quality pattern based on meteorological data and geographical information. This allows for a multifaceted analysis of the cause of the anomaly.

[0066] The analysis unit can integrate the data with other environmental data to perform a comprehensive environmental assessment. The analysis unit can, for example, integrate the data with other environmental data (e.g., air quality data and soil data) to perform a comprehensive environmental assessment. For example, water quality data and air quality data can be integrated to perform a comprehensive environmental assessment. The analysis unit can also integrate soil data to perform a comprehensive environmental assessment. For example, water quality data and soil data can be integrated to perform a comprehensive environmental assessment. The analysis unit can also combine multiple pieces of environmental data to perform a comprehensive environmental assessment. For example, water quality data, air quality data, and soil data can be integrated to perform a comprehensive environmental assessment. This allows for a comprehensive environmental assessment.

[0067] The analysis unit can visualize the results of anomaly detection and provide a dashboard that the user can intuitively understand. The analysis unit, for example, visualizes the results of anomaly detection and provides a dashboard that the user can intuitively understand. For example, abnormal water quality patterns are displayed in graphs and charts. The analysis unit can also update the results of anomaly detection in real time to provide the user with the latest information. For example, the analysis unit can update the results of anomaly detection in real time on a dashboard to provide the user with the latest information. The analysis unit can also display the results of anomaly detection interactively to allow the user to check detailed information. For example, detailed data can be displayed by clicking on a graph or chart. This visualizes the results of anomaly detection and allows the user to intuitively understand it.

[0068] The analysis unit can use the emotion estimation function to collect the user's emotional reactions to the analysis results and use the collected data to improve the analysis algorithm. For example, the analysis unit uses the emotion estimation function to collect the user's emotional reactions to the analysis results. For example, the analysis algorithm is improved based on the user's emotional data. The analysis unit can also use facial recognition technology to analyze the user's facial expressions and collect the emotional reactions. For example, the facial recognition technology can be used to estimate emotions from the user's facial expressions and collect the data. The analysis unit can also use voice analysis technology to analyze the tone and speed of the user's voice and collect the emotional reactions. For example, the voice analysis technology can be used to estimate emotions from the tone and speed of the user's voice and collect the data. In this way, the user's emotional reactions can be collected and used to improve the analysis algorithm.

[0069] The evaluation unit can use the emotion estimation function to analyze residents' emotional reactions to the pollution source and evaluate the social impact. The evaluation unit, for example, uses the emotion estimation function to analyze residents' emotional reactions to the pollution source. For example, the social impact of the pollution source is evaluated based on residents' emotional data. The evaluation unit can also use facial recognition technology to analyze residents' facial expressions and analyze their emotional reactions. For example, facial recognition technology can be used to estimate emotions from residents' facial expressions and analyze the data. The evaluation unit can also use voice analysis technology to analyze residents' tone and speed of voice and analyze their emotional reactions. For example, voice analysis technology can be used to estimate emotions from residents' tone and speed of voice and analyze the data. This makes it possible to analyze residents' emotional reactions to the pollution source and evaluate the social impact.

[0070] The evaluation unit can identify the location of the pollution source with high accuracy using a geographic information system (GIS). The evaluation unit, for example, uses a geographic information system (GIS) to identify the location of the pollution source with high accuracy. For example, the location of the pollution source is identified based on GIS data and displayed on a map. The evaluation unit can also identify the location of the pollution source by combining GIS data with water quality data. For example, the evaluation unit can identify the location of the pollution source by combining abnormal patterns in water quality data with GIS data. The evaluation unit can also update the location of the pollution source in real time using GIS data. For example, if the location of the pollution source changes, the location information is updated based on GIS data. This allows the location of the pollution source to be identified with high accuracy.

[0071] The evaluation unit can refer to the database of past contamination cases and detect similar patterns to identify the source of contamination. The evaluation unit, for example, refers to the database of past contamination cases and detects similar patterns to identify the source of contamination. For example, the evaluation unit identifies the source of contamination based on past data. The evaluation unit can also use a pattern matching algorithm to compare past contamination cases with current data to detect similar patterns. For example, the evaluation unit can use a pattern matching algorithm to compare past contamination cases with current data to detect similar patterns. The evaluation unit can also use the database of past contamination cases to set criteria for identifying the source of contamination. For example, the evaluation unit sets criteria for identifying the source of contamination based on past data. This makes it possible to refer to the database of past contamination cases and detect similar patterns to identify the source of contamination.

[0072] The evaluation unit can be expanded to identify different types of pollution sources. For example, the evaluation unit can be expanded to identify different types of pollution sources (e.g., air pollution sources and soil pollution sources). For example, the evaluation unit can analyze air quality data and soil data to identify pollution sources. The evaluation unit can also develop algorithms for identifying different types of pollution sources. For example, the evaluation unit can develop algorithms for identifying air pollution sources and soil pollution sources. The evaluation unit can also build a database for identifying different types of pollution sources. For example, the evaluation unit can build a database of air pollution sources and soil pollution sources and identify pollution sources based on that data. In this way, the evaluation unit can be expanded to identify different types of pollution sources.

[0073] The evaluation unit can build a system that notifies relevant organizations and residents of the results of identifying the pollution source in real time. The evaluation unit, for example, builds a system that notifies relevant organizations and residents of the results of identifying the pollution source in real time. For example, the evaluation unit may send emails or app notifications to relevant organizations and residents. The evaluation unit can also provide multiple notification methods so that relevant organizations and residents can choose from. For example, methods such as email notifications, app notifications, and voice alerts can be provided so that relevant organizations and residents can choose from. The evaluation unit can also customize the content of the notifications and provide appropriate information to relevant organizations and residents. For example, the evaluation unit can customize the content of the notifications depending on the results of identifying the pollution source and provide appropriate information to relevant organizations and residents. In this way, a system can be built that notifies relevant organizations and residents of the results of identifying the pollution source in real time.

[0074] The evaluation unit can use the emotion estimation function to collect residents' emotional responses to the results of pollution source identification and determine the priorities of pollution countermeasures. The evaluation unit, for example, uses the emotion estimation function to collect residents' emotional responses to the results of pollution source identification. For example, the priorities of pollution countermeasures are determined based on the residents' emotional data. The evaluation unit can also use facial recognition technology to analyze residents' facial expressions and collect emotional responses. For example, facial recognition technology can be used to estimate emotions from residents' facial expressions and collect the data. The evaluation unit can also use voice analysis technology to analyze residents' tone and speed of voice and collect emotional responses. For example, voice analysis technology can be used to estimate emotions from residents' tone and speed of voice and collect the data. In this way, residents' emotional responses to the results of pollution source identification can be collected and the priorities of pollution countermeasures can be determined.

[0075] The evaluation unit can use the emotion estimation function to analyze residents' emotional reactions to wildlife protection activities and evaluate the effectiveness of the protection activities. The evaluation unit, for example, uses the emotion estimation function to analyze residents' emotional reactions to wildlife protection activities. For example, the effectiveness of the protection activities is evaluated based on residents' emotional data. The evaluation unit can also use facial recognition technology to analyze residents' facial expressions and analyze their emotional reactions. For example, facial recognition technology can be used to estimate emotions from residents' facial expressions and analyze the data. The evaluation unit can also use voice analysis technology to analyze residents' tone and speed of voice and analyze their emotional reactions. For example, voice analysis technology can be used to estimate emotions from residents' tone and speed of voice and analyze the data. This makes it possible to analyze residents' emotional reactions to wildlife protection activities and evaluate the effectiveness of the protection activities.

[0076] The evaluation unit can use the ecosystem model to simulate multiple scenarios and propose measures with the least impact. The evaluation unit, for example, uses the ecosystem model to simulate multiple scenarios and propose measures with the least impact. For example, it compares different countermeasure scenarios and selects the optimal countermeasure. The evaluation unit can also use the ecosystem model to evaluate the effectiveness of the countermeasure based on the simulation results. For example, it evaluates the effectiveness of the countermeasure based on the simulation results and proposes the optimal countermeasure. The evaluation unit can also use the ecosystem model to update the simulation results in real time. For example, it updates the simulation results in real time and proposes countermeasures based on the latest information. In this way, it is possible to simulate multiple scenarios and propose measures with the least impact.

[0077] The assessment unit can be expanded so that it can predict impacts on other ecosystems. The assessment unit is expanded so that it can predict impacts on other ecosystems (for example, forests and wetlands). For example, a forest ecosystem model is used to predict impacts. The assessment unit can also predict impacts using a wetland ecosystem model. For example, a wetland ecosystem model is used to predict impacts. The assessment unit can also combine multiple ecosystem models to predict impacts on other ecosystems. For example, a forest and wetland ecosystem model are combined to predict impacts. In this way, the system can be expanded so that it can predict impacts on other ecosystems.

[0078] The evaluation department can share the results of the impact prediction with educational institutions and research institutions to promote collaborative research. The evaluation department, for example, shares the results of the impact prediction with educational institutions and research institutions to promote collaborative research. For example, it can launch a collaborative research project based on the prediction results. The evaluation department can also build a platform for sharing the results of the impact prediction. For example, it can build an online platform and share the prediction results with educational institutions and research institutions. The evaluation department can also set a collaborative research theme based on the results of the impact prediction. For example, it can set a collaborative research theme based on the prediction results and advance research. In this way, the results of the impact prediction can be shared with educational institutions and research institutions to promote collaborative research.

[0079] The evaluation unit can use the emotion estimation function to collect residents' emotional responses to the impact prediction results, and use the collected data to raise awareness of conservation activities. The evaluation unit, for example, uses the emotion estimation function to collect residents' emotional responses to the impact prediction results. For example, the evaluation unit raises awareness of conservation activities based on residents' emotional data. The evaluation unit can also use facial recognition technology to analyze residents' facial expressions and collect emotional responses. For example, facial recognition technology can be used to estimate emotions from residents' facial expressions and collect the data. The evaluation unit can also use voice analysis technology to analyze residents' tone and speed of voice and collect emotional responses. For example, voice analysis technology can be used to estimate emotions from residents' tone and speed of voice and collect the data. In this way, residents' emotional responses to the impact prediction results can be collected, and used to raise awareness of conservation activities.

[0080] The evaluation unit can use the emotion estimation function to analyze residents' emotional reactions to proposed measures and propose measures that are easy to accept. The evaluation unit, for example, uses the emotion estimation function to analyze residents' emotional reactions to proposed measures. For example, it proposes measures that are easy to accept based on residents' emotional data. The evaluation unit can also use facial recognition technology to analyze residents' facial expressions and analyze their emotional reactions. For example, it can use facial recognition technology to infer emotions from residents' facial expressions and analyze the data. The evaluation unit can also use voice analysis technology to analyze residents' tone and speed of voice and analyze their emotional reactions. For example, it can use voice analysis technology to infer emotions from residents' tone and speed of voice and analyze the data. This makes it possible to analyze residents' emotional reactions to proposed measures and propose measures that are easy to accept.

[0081] The evaluation unit can refer to past success cases and propose the most effective measures. The evaluation unit, for example, refers to past success cases and proposes the most effective measures. For example, the optimal measures are selected based on past data. The evaluation unit can also build a database of success cases and refer to past success cases. For example, the evaluation unit can build a database of success cases and propose the optimal measures based on that data. The evaluation unit can also evaluate the effectiveness of measures based on success cases. For example, the evaluation unit can evaluate the effectiveness of measures based on success cases and propose the optimal measures. In this way, the most effective measures can be proposed by referring to past success cases.

[0082] The evaluation unit can simulate the feasibility of proposed measures and select the optimal measures. The evaluation unit, for example, simulates the feasibility of proposed measures and selects the optimal measures. For example, it determines the priority of measures based on the simulation results. The evaluation unit can also build a simulation model for evaluating feasibility. For example, it builds a simulation model for evaluating feasibility and selects the optimal measures using the model. The evaluation unit can also evaluate the effectiveness of measures based on the results of the simulation. For example, it evaluates the effectiveness of measures based on the results of the simulation and selects the optimal measures. In this way, it is possible to simulate the feasibility of proposed measures and select the optimal measures.

[0083] The evaluation unit can be expanded so that it can propose measures for different environmental problems. The evaluation unit can be expanded, for example, so that it can propose measures for different environmental problems (e.g., air pollution and soil pollution). For example, it analyzes air quality data and soil data and proposes optimal measures. The evaluation unit can also develop algorithms for proposing measures for different environmental problems. For example, it develops algorithms for proposing measures for air pollution and soil pollution. The evaluation unit can also build a database for proposing measures for different environmental problems. For example, it builds a database for air pollution and soil pollution and proposes optimal measures based on that data. In this way, the evaluation unit can be expanded so that it can propose measures for different environmental problems.

[0084] The evaluation unit can build a system that monitors the effectiveness of proposed measures and improves the measures based on feedback. The evaluation unit, for example, builds a system that monitors the effectiveness of proposed measures and improves the measures based on feedback. For example, it collects data after the measures are implemented and evaluates their effectiveness. The evaluation unit can also conduct a questionnaire survey to collect feedback. For example, it conducts a questionnaire survey of residents and related organizations and collects feedback. The evaluation unit can also analyze sensor data and evaluate the effectiveness of the measures. For example, it analyzes sensor data, evaluates the effectiveness of the measures, and improves the measures based on feedback. In this way, it is possible to build a system that monitors the effectiveness of proposed measures and improves the measures based on feedback.

[0085] The evaluation unit can use the emotion estimation function to collect residents' emotional reactions to the proposed measures and formulate a strategy for gaining the community's cooperation. For example, the evaluation unit uses the emotion estimation function to collect residents' emotional reactions to the proposed measures. For example, the evaluation unit formulates a strategy for gaining the community's cooperation based on the residents' emotional data. The evaluation unit can also use facial recognition technology to analyze residents' facial expressions and collect emotional reactions. For example, the facial recognition technology can be used to infer emotions from residents' facial expressions and collect the data. The evaluation unit can also use voice analysis technology to analyze residents' tone and speed of voice and collect emotional reactions. For example, the voice analysis technology can be used to infer emotions from residents' tone and speed of voice and collect the data. In this way, it is possible to collect residents' emotional reactions to the proposed measures and formulate a strategy for gaining the community's cooperation.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The water quality monitoring system can further include a prediction unit. The prediction unit predicts future water quality fluctuations based on the collected water quality data. For example, it combines past data and current data to predict future water quality fluctuations. The prediction unit can also integrate meteorological data and geographical information to make more accurate predictions. For example, it can predict water quality fluctuations based on meteorological data, taking into account fluctuations in rainfall and temperature. The prediction unit can also provide the prediction results to relevant organizations and residents to encourage them to take advance measures. For example, it can propose appropriate measures to relevant organizations based on the prediction results. This makes it possible to take advance measures against future water quality fluctuations.

[0088] The sensor device can use its emotion estimation function to collect emotion data from local residents and use it to help raise environmental awareness in the area. For example, a campaign to raise environmental awareness can be carried out based on the emotion data of residents. The sensor device can also use facial recognition technology to analyze residents' facial expressions and collect emotion data. For example, facial recognition technology can be used to estimate emotions from residents' facial expressions and collect that data. The sensor device can also use voice analysis technology to analyze the tone and speed of residents' voices and collect emotion data. For example, voice analysis technology can be used to estimate emotions from the tone and speed of residents' voices and collect that data. This can be used to help raise environmental awareness in the area.

[0089] Sensor devices can also be equipped with energy-saving modes to further improve energy efficiency. For example, they can adjust the frequency of data collection and collect data at high frequencies only when necessary. Sensor devices can also be self-charged using solar or wind power. For example, they can be equipped with solar panels and charge during the day. Sensor devices can also transmit data using low-power communication technologies. For example, low-power communication technologies such as LoRa and NB-IoT are used to transmit data efficiently. This improves energy efficiency and enables long-term operation.

[0090] The sensor device can use its emotion estimation function to collect emotion data from local residents and use it to help raise environmental awareness in the area. For example, a campaign to raise environmental awareness can be carried out based on the emotion data of residents. The sensor device can also use facial recognition technology to analyze residents' facial expressions and collect emotion data. For example, facial recognition technology can be used to estimate emotions from residents' facial expressions and collect that data. The sensor device can also use voice analysis technology to analyze the tone and speed of residents' voices and collect emotion data. For example, voice analysis technology can be used to estimate emotions from the tone and speed of residents' voices and collect that data. This can be used to help raise environmental awareness in the area.

[0091] Sensor devices can be equipped with encryption functions to further strengthen data security. For example, collected data can be encrypted before transmission to prevent unauthorized access. Sensor devices can also use hash functions to detect data tampering. For example, by calculating a hash value of the data and adding it when transmitting, the integrity of the data can be confirmed on the receiving side. Sensor devices can also be equipped with authentication functions to ensure that only authorized devices can transmit data. For example, an authentication key can be set for each device, so that only authenticated devices can transmit data. This strengthens data security.

[0092] The sensor device can use its emotion estimation function to collect emotion data from local residents and use it to help raise environmental awareness in the area. For example, a campaign to raise environmental awareness can be carried out based on the emotion data of residents. The sensor device can also use facial recognition technology to analyze residents' facial expressions and collect emotion data. For example, facial recognition technology can be used to estimate emotions from residents' facial expressions and collect that data. The sensor device can also use voice analysis technology to analyze the tone and speed of residents' voices and collect emotion data. For example, voice analysis technology can be used to estimate emotions from the tone and speed of residents' voices and collect that data. This can be used to help raise environmental awareness in the area.

[0093] Sensor devices also have self-learning capabilities, allowing them to automatically optimize parameters based on collected data. For example, they can adjust the sensitivity of sensors based on past data to improve the accuracy of anomaly detection. Sensor devices can also automatically change settings in response to changes in the environment. For example, they can adjust the frequency and items of data collection according to changes in the season or weather. Sensor devices can also use their self-learning capabilities to detect signs of malfunction and perform maintenance in advance. For example, they can learn the sensor's operating patterns and issue an alert if abnormal operation is detected. This improves the performance of sensor devices and enables them to operate for a long period of time.

[0094] The analysis unit can use the emotion estimation function to analyze residents' emotional responses to the collected data and evaluate their level of interest in environmental issues. For example, the level of interest in environmental issues can be evaluated based on residents' emotional data. The analysis unit can also use facial recognition technology to analyze residents' facial expressions and analyze their emotional responses. For example, facial recognition technology can be used to estimate emotions from residents' facial expressions and analyze the data. The analysis unit can also use voice analysis technology to analyze residents' tone and speed of voice and analyze their emotional responses. For example, voice analysis technology can be used to estimate emotions from residents' tone and speed of voice and analyze the data. In this way, the level of interest in environmental issues can be evaluated by analyzing residents' emotional responses.

[0095] When detecting an anomaly, the analysis unit can integrate past meteorological data and geographical information to perform a multifaceted analysis of the cause of the anomaly. For example, when detecting an anomaly, past meteorological data can be integrated to analyze the cause of the anomaly. For example, whether an abnormal water quality pattern is related to meteorological conditions can be evaluated. The analysis unit can also integrate geographical information to analyze the cause of the anomaly. For example, the location of a pollution source can be identified based on geographical information and the cause of the anomaly can be analyzed. The analysis unit can also combine meteorological data and geographical information to perform a multifaceted analysis of the cause of the anomaly. For example, the cause of an abnormal water quality pattern can be identified based on meteorological data and geographical information. This allows for a multifaceted analysis of the cause of the anomaly.

[0096] The evaluation unit can use the emotion estimation function to analyze residents' emotional reactions to the pollution source and evaluate the social impact. For example, the social impact of the pollution source is evaluated based on residents' emotional data. The evaluation unit can also use facial recognition technology to analyze residents' facial expressions and analyze their emotional reactions. For example, facial recognition technology can be used to estimate emotions from residents' facial expressions and analyze the data. The evaluation unit can also use voice analysis technology to analyze residents' tone and speed of voice and analyze their emotional reactions. For example, voice analysis technology can be used to estimate emotions from residents' tone and speed of voice and analyze the data. This makes it possible to analyze residents' emotional reactions to the pollution source and evaluate the social impact.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: Sensor devices are installed in bodies of water to collect water quality data such as water temperature, pH, dissolved oxygen, turbidity, and conductivity in real time. For example, sensor devices are installed in bodies of water such as rivers, lakes, and oceans, and data is collected periodically. This allows for continuous monitoring of fluctuations in water quality. Step 2: The data collection unit collects the water quality data collected by the sensor devices. For example, the data collection unit receives the data sent from the sensor devices and stores it in a database. The data collection unit can also preprocess the collected data and pass it to the analysis unit. Step 3: The analysis unit analyzes the water quality data collected by the data collection unit. For example, the analysis unit uses AI to analyze the water quality data and detect abnormal patterns. The AI ​​compares the data with past data and detects sudden changes that differ from normal water quality patterns or increases in the concentration of specific harmful substances. Step 4: The evaluation unit evaluates the water quality data analyzed by the analysis unit. For example, the evaluation unit identifies pollution sources and predicts their impact on wildlife based on abnormal patterns detected by the AI. The AI ​​estimates the location and type of pollution source based on past data and geographic information. The AI ​​also uses an ecosystem model to simulate the impact of water quality fluctuations on the ecosystem.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[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 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.

[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 (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).

[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] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

[0166] 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 sensor device; a data collection unit that collects water quality data collected by the sensor device; an analysis unit that analyzes the water quality data collected by the data collection unit; an evaluation unit that evaluates the water quality data analyzed by the analysis unit; A system characterized by:

2. The sensor device It will be installed on drones and autonomous underwater vehicles to collect wide-ranging, multi-layered water quality data. The system of claim 1 .

3. The sensor device Installed in urban drainage systems to manage water quality throughout the city The system of claim 1 .

4. The analysis unit Using emotion estimation, we analyze residents' emotional responses to collected data and assess their level of interest in environmental issues. The system of claim 1 .

5. The evaluation unit Using emotion estimation capabilities, we analyze residents' emotional responses to pollution sources and assess their social impact. The system of claim 1 .

6. The evaluation unit Using emotion estimation capabilities, we analyze residents' emotional responses to wildlife conservation efforts and evaluate the effectiveness of those efforts. The system of claim 1 .

7. The evaluation unit Using emotion estimation function, analyze residents' emotional reactions to proposed measures and propose measures that are more acceptable to them. The system of claim 1 .

8. The sensor device Equipped with an emotion estimation function, it collects emotional data on the surrounding environment and human activities. The system of claim 1 .

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