system

Abstract

The system, as described in this example, aims to monitor environmental data in real time and detect anomalies and potential risks. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, an anomaly detection unit, and a prediction unit. The data collection unit collects environmental data. The analysis unit analyzes the data collected by the data collection unit. The anomaly detection unit detects anomalies based on the data analyzed by the analysis unit. The prediction unit predicts environmental changes based on the anomalies detected by the anomaly detection unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, real-time monitoring and anomaly detection of environmental data are not sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to monitor environmental data in real time and detect anomalies and potential risks.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an anomaly detection unit, and a prediction unit. The data collection unit collects environmental data. The analysis unit analyzes the data collected by the data collection unit. The anomaly detection unit detects anomalies based on the data analyzed by the analysis unit. The prediction unit predicts environmental changes based on the anomalies detected by the anomaly detection unit. [Effects of the Invention]

[0007] The system, as described above, can monitor environmental data in real time and detect anomalies and potential risks. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) An environmental monitoring system according to an embodiment of the present invention is a system that uses AI to monitor environmental data in real time and detect anomalies and potential risks. This environmental monitoring system uses sensors to collect environmental data such as air quality, water quality, and climate change in real time, and the AI ​​analyzes the collected data to identify patterns and trends. Furthermore, the AI ​​predicts environmental changes. This system can support governments and companies in making strategic decisions toward environmental protection and disaster management. For example, possible types of sensors include sensors that measure carbon dioxide concentration in the air, sensors that measure the pH value of water, and sensors that measure temperature. These sensors are placed in appropriate locations to collect data in real time. Next, the collected data is analyzed by AI. Specifically, the AI ​​compares past data with current data to detect anomalies and risks. For example, possible anomaly detection algorithms include anomaly detection and trend analysis. Furthermore, the AI ​​predicts environmental changes. For example, it uses time series analysis and machine learning algorithms to predict future environmental changes. This allows governments and companies to make strategic decisions based on the prediction results. One usage scenario is that if an anomaly is detected, countermeasures can be taken quickly. Specifically, on-site investigations can be conducted at locations where anomalies are detected, and necessary countermeasures can be taken. Furthermore, environmental protection and disaster management plans can be developed based on the prediction results. This allows the environmental monitoring system to monitor environmental data in real time, detect anomalies and potential risks, and predict environmental changes.

[0029] The environmental monitoring system according to this embodiment comprises a collection unit, an analysis unit, an anomaly detection unit, and a prediction unit. The collection unit collects environmental data. Environmental data includes, but is not limited to, temperature, humidity, and carbon dioxide concentration. The collection unit collects data using, for example, a sensor that measures the carbon dioxide concentration in the atmosphere. The collection unit can also collect data using a sensor that measures the pH value of water. Furthermore, the collection unit can also collect data using a sensor that measures temperature. For example, the collection unit places a sensor that measures the carbon dioxide concentration in the atmosphere in an appropriate location and collects data in real time. The collection unit can also place a sensor that measures the pH value of water in an appropriate location and collect data in real time. Furthermore, the collection unit can also place a sensor that measures temperature in an appropriate location and collect data in real time. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these examples. For example, the analysis unit compares past data with current data to detect anomalies and risks. The analysis unit can also detect anomalies using an anomaly detection algorithm. Furthermore, the analysis unit can also predict environmental changes using time series analysis and machine learning algorithms. For example, the analysis unit compares past data with current data to detect anomalies and risks. The analysis unit can also detect anomalies using anomaly detection algorithms. Furthermore, the analysis unit can also predict environmental changes using time series analysis and machine learning algorithms. The anomaly detection unit detects anomalies based on the data analyzed by the analysis unit. Anomalies are, for example, values ​​exceeding a threshold or abnormal patterns, but are not limited to such examples. For example, the anomaly detection unit detects anomalies using an anomaly detection algorithm. Furthermore, the anomaly detection unit can also detect anomalies using trend analysis. Furthermore, the anomaly detection unit can also detect anomalies using machine learning algorithms. The prediction unit predicts environmental changes based on the anomalies detected by the anomaly detection unit. Predictions are made, for example, using time series analysis and machine learning algorithms, but are not limited to such examples. For example, the prediction unit predicts environmental changes using time series analysis.Furthermore, the prediction unit can also predict environmental changes using machine learning algorithms. Additionally, the prediction unit can predict environmental changes using statistical models. As a result, the environmental monitoring system according to this embodiment can monitor environmental data in real time, detect anomalies and potential risks, and predict environmental changes.

[0030] The data collection unit may be equipped with a sensor for measuring the carbon dioxide concentration in the atmosphere. The data collection unit collects data using, for example, a sensor for measuring the carbon dioxide concentration in the atmosphere. The carbon dioxide concentration is measured, for example, in units of ppm (parts per million). The data collection unit, for example, places the sensor for measuring the carbon dioxide concentration in the atmosphere in an appropriate location and collects data in real time. The data collection unit can also store the carbon dioxide concentration measurement results in a database and provide them to the analysis unit. This allows for accurate measurement of the carbon dioxide concentration in the atmosphere. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input the carbon dioxide concentration measurement results into the AI, which can then analyze the data.

[0031] The data collection unit may be equipped with a sensor for measuring the pH value of water. The data collection unit collects data using, for example, a sensor for measuring the pH value of water. The pH value is measured using, for example, a pH meter. The data collection unit can, for example, place the sensor for measuring the pH value of water in an appropriate location and collect data in real time. The data collection unit can also store the pH measurement results in a database and provide them to the analysis unit. This allows for accurate measurement of the pH value of water. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the pH measurement results into the AI, which can then analyze the data.

[0032] The data collection unit may be equipped with a sensor for measuring temperature. The data collection unit collects data using, for example, a temperature sensor. The temperature is measured using, for example, a temperature sensor. The data collection unit places, for example, a temperature sensor in an appropriate location and collects data in real time. The data collection unit can also store the temperature measurement results in a database and provide them to the analysis unit. This allows for accurate temperature measurement. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the temperature measurement results into the AI, which can then analyze the data.

[0033] The analysis unit can compare historical data with current data to detect anomalies and risks. For example, the analysis unit can compare historical data with current data to detect anomalies and risks. Historical data may include, for example, data from the past year, but is not limited to such examples. Current data may include, for example, real-time data or data from the last hour, but is not limited to such examples. The analysis unit can also detect anomalies using an anomaly detection algorithm. Furthermore, the analysis unit may also detect anomalies using trend analysis. This allows for accurate detection of anomalies and risks by comparing historical data with current data. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input historical and current data into AI, which can then detect anomalies and risks.

[0034] The anomaly detection unit can detect anomalies using an anomaly detection algorithm. For example, the anomaly detection unit can detect anomalies using an anomaly detection algorithm. Anomaly detection algorithms include, but are not limited to, statistical methods and machine learning algorithms. The anomaly detection unit can also detect anomalies using statistical methods. Furthermore, the anomaly detection unit can detect anomalies using machine learning algorithms. In addition, the anomaly detection unit can detect anomalies using trend analysis. This allows for accurate detection of anomalies by using an anomaly detection algorithm. Some or all of the above-described processes in the anomaly detection unit may be performed using, for example, AI, or without AI. For example, the anomaly detection unit can input an anomaly detection algorithm into an AI, which can then detect the anomaly.

[0035] The prediction unit can predict environmental changes using time series analysis and machine learning algorithms. For example, the prediction unit predicts environmental changes using time series analysis and machine learning algorithms. Time series analysis includes, but is not limited to, ARIMA models and LSTMs. Machine learning algorithms include, but is not limited to, regression analysis and neural networks. The prediction unit can predict environmental changes using time series analysis. Furthermore, the prediction unit can also predict environmental changes using machine learning algorithms. In addition, the prediction unit can predict environmental changes using statistical models. This allows for accurate prediction of environmental changes using time series analysis and machine learning algorithms. Some or all of the above-described processes in the prediction unit may be performed using, for example, AI, or not. For example, the prediction unit can input time series analysis and machine learning algorithms into an AI, which can then predict environmental changes.

[0036] The prediction unit can support strategic decisions based on prediction results. For example, the prediction unit can support strategic decisions based on prediction results. These strategic decisions include, but are not limited to, environmental protection measures and disaster management plans. For example, the prediction unit can formulate environmental protection measures based on prediction results. It can also develop disaster management plans based on prediction results. Furthermore, the prediction unit can construct risk management strategies based on prediction results. This contributes to environmental protection and disaster management by supporting strategic decisions based on prediction results. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or not using AI. For example, the prediction unit can input prediction results into AI, which can then support strategic decisions.

[0037] The data collection unit can analyze historical environmental data and select the optimal sensor placement. For example, the data collection unit can analyze historical air quality data and place sensors in areas with high carbon dioxide concentrations. It can also analyze historical water quality data and place sensors in areas where pollution frequently occurs. Furthermore, it can analyze historical temperature data and place sensors in areas with significant temperature fluctuations. In this way, the optimal sensor placement can be selected by analyzing historical environmental data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input historical environmental data into AI, which can then select the optimal sensor placement.

[0038] The data collection unit can adjust the sensitivity of sensors based on specific environmental conditions during data collection. For example, the unit can increase the sensitivity of the temperature sensor to collect detailed data when temperatures are high. It can also increase the sensitivity of the water quality sensor to detect pollutants during rainy weather. Furthermore, the unit can increase the sensitivity of sensors to collect accurate data when the atmospheric carbon dioxide concentration is high. This allows for the collection of detailed data by adjusting the sensor sensitivity based on specific environmental conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input specific environmental conditions into the AI, which can then adjust the sensor sensitivity.

[0039] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of data from areas where pollution frequently occurs. It can also prioritize the collection of data from areas with extreme temperature fluctuations. Furthermore, it can prioritize the collection of data from areas with high carbon dioxide concentrations. In this way, by considering geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into AI, and the AI ​​can prioritize the collection of highly relevant data.

[0040] The data collection unit can analyze social media activity and collect relevant environmental data during data collection. For example, the data collection unit can collect data for a given area based on pollution information reported on social media. The data collection unit can also analyze discussions about climate change on social media and collect relevant data. Furthermore, the data collection unit can collect data for a given area based on reports of extreme weather events on social media. In this way, relevant environmental data can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity into AI, and the AI ​​can collect relevant environmental data.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit will perform a detailed analysis on important data. It can also perform a simplified analysis on less important data. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an anomaly detection algorithm to atmospheric data. It can also apply a trend analysis algorithm to water quality data. Furthermore, it can apply a time series analysis algorithm to climate data. By applying different analysis algorithms depending on the data category, the analysis unit can perform the most optimal analysis for each category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can apply different analysis algorithms.

[0043] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. Alternatively, the analysis unit may prioritize the analysis of current data while referring to past data. Furthermore, the analysis unit can adjust the priority of analysis in stages according to the data collection timing. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI, and the AI ​​can determine the priority of analysis.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis in stages according to the relevance of the data. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, which can then adjust the order of analysis.

[0045] The anomaly detection unit can improve the accuracy of detection by considering the interrelationships of data when detecting anomalies. For example, the anomaly detection unit can detect anomalies by considering the interrelationships of air quality data and water quality data. It can also detect anomalies by considering the interrelationships of temperature data and carbon dioxide concentration data. Furthermore, the anomaly detection unit can detect anomalies by considering the interrelationships of multiple data categories. In this way, the accuracy of anomaly detection can be improved by considering the interrelationships of data. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input the interrelationships of data into AI, and the AI ​​can detect anomalies.

[0046] The anomaly detection unit can perform detection while considering the attribute information of the data provider when detecting anomalies. For example, the anomaly detection unit can detect anomalies by considering the reliability of the data provider. The anomaly detection unit can also detect anomalies by considering the data provider's past data provision history. Furthermore, the anomaly detection unit can detect anomalies by considering the expertise of the data provider. In this way, the accuracy of anomaly detection can be improved by considering the attribute information of the data provider. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without using AI. For example, the anomaly detection unit can input the attribute information of the data provider into the AI, and the AI ​​can detect anomalies.

[0047] The anomaly detection unit can perform anomaly detection while considering the geographical distribution of the data. For example, the anomaly detection unit can prioritize detecting data in areas where anomalies have occurred. The anomaly detection unit can also detect anomalies by considering geographically relevant data. Furthermore, the anomaly detection unit can adjust the range of anomaly detection based on the geographical distribution. This improves the accuracy of anomaly detection by considering the geographical distribution of the data. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input the geographical distribution of the data into the AI, which can then detect anomalies.

[0048] The anomaly detection unit can improve the accuracy of its detection by referring to relevant literature when detecting an anomaly. For example, the anomaly detection unit may refer to relevant literature to improve the accuracy of its anomaly detection algorithm. Furthermore, the anomaly detection unit may also improve detection accuracy by referring to appropriate literature depending on the type of anomaly. In addition, the anomaly detection unit may refer to relevant literature to verify the results of the anomaly detection. Thus, by referring to relevant literature, the accuracy of anomaly detection can be improved. Some or all of the above-described processes in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input relevant literature into AI, which can then detect anomalies.

[0049] The prediction unit can optimize the current prediction by referring to past prediction data during the prediction process. For example, the prediction unit can modify the current prediction based on past prediction data. The prediction unit can also compare past prediction data with current data to make the optimal prediction. Furthermore, the prediction unit can improve the accuracy of the prediction by referring to past prediction data. In this way, the current prediction can be optimized by referring to past prediction data. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past prediction data into AI, and the AI ​​can optimize the current prediction.

[0050] The prediction unit can apply different prediction methods to each data category during the prediction process. For example, the prediction unit can apply time series analysis to atmospheric data for prediction. It can also apply machine learning algorithms to water quality data for prediction. Furthermore, it can apply statistical models to climate data for prediction. This allows for optimal predictions for each data category by applying different prediction methods. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data categories into the AI, which can then apply different prediction methods.

[0051] The prediction unit can analyze changes in predictions based on the data collection timing during the prediction process. For example, the prediction unit can analyze changes in predictions based on the latest data. The prediction unit can also analyze changes in predictions by comparing past data with current data. Furthermore, the prediction unit can analyze changes in predictions in stages according to the data collection timing. This allows for predictions based on the latest data by analyzing changes in predictions based on the data collection timing. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the data collection timing into the AI, which can then analyze changes in predictions.

[0052] The forecasting unit can analyze its forecasts by referring to relevant market data during the forecasting process. For example, the forecasting unit can compare environmental data with relevant market data and analyze the forecast. The forecasting unit can also predict environmental changes by considering market trends. Furthermore, the forecasting unit can improve the accuracy of its forecasts by referring to relevant market data. In this way, the accuracy of the forecast can be improved by referring to relevant market data. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input relevant market data into AI, and the AI ​​can analyze the forecast.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The analysis unit can dynamically adjust the data collection frequency. For example, if an anomaly is detected, the analysis unit can instruct the data collection unit to increase the data collection frequency. Conversely, if the anomaly is resolved, the analysis unit can instruct the data collection unit to decrease the data collection frequency. Furthermore, the analysis unit can automatically adjust the collection frequency when specific environmental conditions occur. This allows the analysis unit to dynamically adjust the data collection frequency in response to environmental changes, enabling more accurate data analysis.

[0055] The data collection unit can be equipped with a sensor self-diagnostic function. For example, the data collection unit can periodically check the operating status of the sensor and issue an alert if an abnormality is detected. The data collection unit can also automatically calibrate the sensor. Furthermore, the data collection unit can predict the sensor's lifespan and notify the user when it is time for replacement. This allows the data collection unit to maintain sensor reliability and collect accurate data.

[0056] The analysis unit can propose countermeasures based on the type of anomaly detected in the data. For example, if an anomaly in carbon dioxide concentration is detected, the analysis unit can recommend ventilation. If an anomaly in water quality is detected, it can suggest the use of a water purification system. Furthermore, if an anomaly in temperature is detected, it can suggest adjusting heating and cooling systems. In this way, the analysis unit can support a rapid response by proposing specific countermeasures tailored to the type of anomaly.

[0057] The data collection unit can optimize energy consumption during data acquisition. For example, it can adjust the sensor's operating mode to reduce energy consumption. It can also adjust the timing of data collection to maximize energy efficiency. Furthermore, it can select and use sensors with lower energy consumption. By optimizing energy consumption, the data collection unit can achieve sustainable data collection.

[0058] The analysis unit can visually display the results of data analysis. For example, it can display anomaly detection results in graphs and charts. It can also display predictions of environmental changes on a map. Furthermore, it can display data trends in time-series graphs. In this way, the analysis unit can help users intuitively understand the analysis results through visual displays.

[0059] The data collection unit can automatically switch the sensor's operating mode according to environmental conditions during data acquisition. For example, the unit can increase the sensor's sensitivity at night to collect more detailed data. It can also change the sensor's operating mode during adverse weather conditions to improve data collection accuracy. Furthermore, the unit can automatically switch the sensor's operating mode when specific environmental conditions occur. This allows the unit to optimize the sensor's operating mode according to environmental conditions, enabling accurate data collection.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The collection unit collects environmental data. This environmental data includes temperature, humidity, and carbon dioxide concentration. The collection unit collects data using sensors to measure atmospheric carbon dioxide concentration, water pH, and temperature. These sensors are placed in appropriate locations to collect data in real time. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis is performed using statistical analysis and machine learning algorithms. The analysis unit compares past data with current data to detect anomalies and risks. It can also detect anomalies using anomaly detection algorithms. Furthermore, it can predict environmental changes using time series analysis and machine learning algorithms. Step 3: The anomaly detection unit detects anomalies based on the data analyzed by the analysis unit. Anomalies include values ​​exceeding a threshold or abnormal patterns. The anomaly detection unit detects anomalies using anomaly detection algorithms, trend analysis, and machine learning algorithms. Step 4: The prediction unit predicts environmental changes based on the anomalies detected by the anomaly detection unit. Predictions are made using time series analysis, machine learning algorithms, and statistical models.

[0062] (Example of form 2) An environmental monitoring system according to an embodiment of the present invention is a system that uses AI to monitor environmental data in real time and detect anomalies and potential risks. This environmental monitoring system uses sensors to collect environmental data such as air quality, water quality, and climate change in real time, and the AI ​​analyzes the collected data to identify patterns and trends. Furthermore, the AI ​​predicts environmental changes. This system can support governments and companies in making strategic decisions toward environmental protection and disaster management. For example, possible types of sensors include sensors that measure carbon dioxide concentration in the air, sensors that measure the pH value of water, and sensors that measure temperature. These sensors are placed in appropriate locations to collect data in real time. Next, the collected data is analyzed by AI. Specifically, the AI ​​compares past data with current data to detect anomalies and risks. For example, possible anomaly detection algorithms include anomaly detection and trend analysis. Furthermore, the AI ​​predicts environmental changes. For example, it uses time series analysis and machine learning algorithms to predict future environmental changes. This allows governments and companies to make strategic decisions based on the prediction results. One usage scenario is that if an anomaly is detected, countermeasures can be taken quickly. Specifically, on-site investigations can be conducted at locations where anomalies are detected, and necessary countermeasures can be taken. Furthermore, environmental protection and disaster management plans can be developed based on the prediction results. This allows the environmental monitoring system to monitor environmental data in real time, detect anomalies and potential risks, and predict environmental changes.

[0063] The environmental monitoring system according to this embodiment comprises a collection unit, an analysis unit, an anomaly detection unit, and a prediction unit. The collection unit collects environmental data. Environmental data includes, but is not limited to, temperature, humidity, and carbon dioxide concentration. The collection unit collects data using, for example, a sensor that measures the carbon dioxide concentration in the atmosphere. The collection unit can also collect data using a sensor that measures the pH value of water. Furthermore, the collection unit can also collect data using a sensor that measures temperature. For example, the collection unit places a sensor that measures the carbon dioxide concentration in the atmosphere in an appropriate location and collects data in real time. The collection unit can also place a sensor that measures the pH value of water in an appropriate location and collect data in real time. Furthermore, the collection unit can also place a sensor that measures temperature in an appropriate location and collect data in real time. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these examples. For example, the analysis unit compares past data with current data to detect anomalies and risks. The analysis unit can also detect anomalies using an anomaly detection algorithm. Furthermore, the analysis unit can also predict environmental changes using time series analysis and machine learning algorithms. For example, the analysis unit compares past data with current data to detect anomalies and risks. The analysis unit can also detect anomalies using anomaly detection algorithms. Furthermore, the analysis unit can also predict environmental changes using time series analysis and machine learning algorithms. The anomaly detection unit detects anomalies based on the data analyzed by the analysis unit. Anomalies are, for example, values ​​exceeding a threshold or abnormal patterns, but are not limited to such examples. For example, the anomaly detection unit detects anomalies using an anomaly detection algorithm. Furthermore, the anomaly detection unit can also detect anomalies using trend analysis. Furthermore, the anomaly detection unit can also detect anomalies using machine learning algorithms. The prediction unit predicts environmental changes based on the anomalies detected by the anomaly detection unit. Predictions are made, for example, using time series analysis and machine learning algorithms, but are not limited to such examples. For example, the prediction unit predicts environmental changes using time series analysis.Furthermore, the prediction unit can also predict environmental changes using machine learning algorithms. Additionally, the prediction unit can predict environmental changes using statistical models. As a result, the environmental monitoring system according to this embodiment can monitor environmental data in real time, detect anomalies and potential risks, and predict environmental changes.

[0064] The data collection unit may be equipped with a sensor for measuring the carbon dioxide concentration in the atmosphere. The data collection unit collects data using, for example, a sensor for measuring the carbon dioxide concentration in the atmosphere. The carbon dioxide concentration is measured, for example, in units of ppm (parts per million). The data collection unit, for example, places the sensor for measuring the carbon dioxide concentration in the atmosphere in an appropriate location and collects data in real time. The data collection unit can also store the carbon dioxide concentration measurement results in a database and provide them to the analysis unit. This allows for accurate measurement of the carbon dioxide concentration in the atmosphere. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input the carbon dioxide concentration measurement results into the AI, which can then analyze the data.

[0065] The data collection unit may be equipped with a sensor for measuring the pH value of water. The data collection unit collects data using, for example, a sensor for measuring the pH value of water. The pH value is measured using, for example, a pH meter. The data collection unit can, for example, place the sensor for measuring the pH value of water in an appropriate location and collect data in real time. The data collection unit can also store the pH measurement results in a database and provide them to the analysis unit. This allows for accurate measurement of the pH value of water. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the pH measurement results into the AI, which can then analyze the data.

[0066] The data collection unit may be equipped with a sensor for measuring temperature. The data collection unit collects data using, for example, a temperature sensor. The temperature is measured using, for example, a temperature sensor. The data collection unit places, for example, a temperature sensor in an appropriate location and collects data in real time. The data collection unit can also store the temperature measurement results in a database and provide them to the analysis unit. This allows for accurate temperature measurement. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the temperature measurement results into the AI, which can then analyze the data.

[0067] The analysis unit can compare historical data with current data to detect anomalies and risks. For example, the analysis unit can compare historical data with current data to detect anomalies and risks. Historical data may include, for example, data from the past year, but is not limited to such examples. Current data may include, for example, real-time data or data from the last hour, but is not limited to such examples. The analysis unit can also detect anomalies using an anomaly detection algorithm. Furthermore, the analysis unit may also detect anomalies using trend analysis. This allows for accurate detection of anomalies and risks by comparing historical data with current data. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input historical and current data into AI, which can then detect anomalies and risks.

[0068] The anomaly detection unit can detect anomalies using an anomaly detection algorithm. For example, the anomaly detection unit can detect anomalies using an anomaly detection algorithm. Anomaly detection algorithms include, but are not limited to, statistical methods and machine learning algorithms. The anomaly detection unit can also detect anomalies using statistical methods. Furthermore, the anomaly detection unit can detect anomalies using machine learning algorithms. In addition, the anomaly detection unit can detect anomalies using trend analysis. This allows for accurate detection of anomalies by using an anomaly detection algorithm. Some or all of the above-described processes in the anomaly detection unit may be performed using, for example, AI, or without AI. For example, the anomaly detection unit can input an anomaly detection algorithm into an AI, which can then detect the anomaly.

[0069] The prediction unit can predict environmental changes using time series analysis and machine learning algorithms. For example, the prediction unit predicts environmental changes using time series analysis and machine learning algorithms. Time series analysis includes, but is not limited to, ARIMA models and LSTMs. Machine learning algorithms include, but is not limited to, regression analysis and neural networks. The prediction unit can predict environmental changes using time series analysis. Furthermore, the prediction unit can also predict environmental changes using machine learning algorithms. In addition, the prediction unit can predict environmental changes using statistical models. This allows for accurate prediction of environmental changes using time series analysis and machine learning algorithms. Some or all of the above-described processes in the prediction unit may be performed using, for example, AI, or not. For example, the prediction unit can input time series analysis and machine learning algorithms into an AI, which can then predict environmental changes.

[0070] The prediction unit can support strategic decisions based on prediction results. For example, the prediction unit can support strategic decisions based on prediction results. These strategic decisions include, but are not limited to, environmental protection measures and disaster management plans. For example, the prediction unit can formulate environmental protection measures based on prediction results. It can also develop disaster management plans based on prediction results. Furthermore, the prediction unit can construct risk management strategies based on prediction results. This contributes to environmental protection and disaster management by supporting strategic decisions based on prediction results. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or not using AI. For example, the prediction unit can input prediction results into AI, which can then support strategic decisions.

[0071] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of only important data and process it quickly. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then adjust the timing of data collection.

[0072] The data collection unit can analyze historical environmental data and select the optimal sensor placement. For example, the data collection unit can analyze historical air quality data and place sensors in areas with high carbon dioxide concentrations. It can also analyze historical water quality data and place sensors in areas where pollution frequently occurs. Furthermore, it can analyze historical temperature data and place sensors in areas with significant temperature fluctuations. In this way, the optimal sensor placement can be selected by analyzing historical environmental data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input historical environmental data into AI, which can then select the optimal sensor placement.

[0073] The data collection unit can adjust the sensitivity of sensors based on specific environmental conditions during data collection. For example, the unit can increase the sensitivity of the temperature sensor to collect detailed data when temperatures are high. It can also increase the sensitivity of the water quality sensor to detect pollutants during rainy weather. Furthermore, the unit can increase the sensitivity of sensors to collect accurate data when the atmospheric carbon dioxide concentration is high. This allows for the collection of detailed data by adjusting the sensor sensitivity based on specific environmental conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input specific environmental conditions into the AI, which can then adjust the sensor sensitivity.

[0074] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important data. If the user is relaxed, the data collection unit can also prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. In this way, by prioritizing the data to be collected according to the user's emotions, important data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI, and the AI ​​can determine the priority of data to collect.

[0075] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of data from areas where pollution frequently occurs. It can also prioritize the collection of data from areas with extreme temperature fluctuations. Furthermore, it can prioritize the collection of data from areas with high carbon dioxide concentrations. In this way, by considering geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into AI, and the AI ​​can prioritize the collection of highly relevant data.

[0076] The data collection unit can analyze social media activity and collect relevant environmental data during data collection. For example, the data collection unit can collect data for a given area based on pollution information reported on social media. The data collection unit can also analyze discussions about climate change on social media and collect relevant data. Furthermore, the data collection unit can collect data for a given area based on reports of extreme weather events on social media. In this way, relevant environmental data can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity into AI, and the AI ​​can collect relevant environmental data.

[0077] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI, and the AI ​​can adjust the presentation of the analysis.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit will perform a detailed analysis on important data. It can also perform a simplified analysis on less important data. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0079] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an anomaly detection algorithm to atmospheric data. It can also apply a trend analysis algorithm to water quality data. Furthermore, it can apply a time series analysis algorithm to climate data. By applying different analysis algorithms depending on the data category, the analysis unit can perform the most optimal analysis for each category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can apply different analysis algorithms.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide an analysis of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI, and the AI ​​can adjust the length of the analysis.

[0081] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. Alternatively, the analysis unit may prioritize the analysis of current data while referring to past data. Furthermore, the analysis unit can adjust the priority of analysis in stages according to the data collection timing. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI, and the AI ​​can determine the priority of analysis.

[0082] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis in stages according to the relevance of the data. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, which can then adjust the order of analysis.

[0083] The anomaly detection unit can estimate the user's emotions and adjust the anomaly detection criteria based on the estimated emotions. For example, if the user is tense, the anomaly detection unit can detect anomalies with stricter criteria. Conversely, if the user is relaxed, the anomaly detection unit can detect anomalies with more lenient criteria. Furthermore, if the user is in a hurry, the anomaly detection unit can set criteria for rapid anomaly detection. This allows for appropriate anomaly detection for the user by adjusting the anomaly detection criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the anomaly detection unit may be performed using AI, or not using AI. For example, the anomaly detection unit can input user emotion data into an AI, which can then adjust the anomaly detection criteria.

[0084] The anomaly detection unit can improve the accuracy of detection by considering the interrelationships of data when detecting anomalies. For example, the anomaly detection unit can detect anomalies by considering the interrelationships of air quality data and water quality data. It can also detect anomalies by considering the interrelationships of temperature data and carbon dioxide concentration data. Furthermore, the anomaly detection unit can detect anomalies by considering the interrelationships of multiple data categories. In this way, the accuracy of anomaly detection can be improved by considering the interrelationships of data. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input the interrelationships of data into AI, and the AI ​​can detect anomalies.

[0085] The anomaly detection unit can perform detection while considering the attribute information of the data provider when detecting anomalies. For example, the anomaly detection unit can detect anomalies by considering the reliability of the data provider. The anomaly detection unit can also detect anomalies by considering the data provider's past data provision history. Furthermore, the anomaly detection unit can detect anomalies by considering the expertise of the data provider. In this way, the accuracy of anomaly detection can be improved by considering the attribute information of the data provider. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without using AI. For example, the anomaly detection unit can input the attribute information of the data provider into the AI, and the AI ​​can detect anomalies.

[0086] The anomaly detection unit can estimate the user's emotions and adjust the order in which anomaly detection results are displayed based on the estimated user emotions. For example, if the user is tense, the anomaly detection unit can prioritize displaying important anomalies. It can also sequentially display detailed anomalies if the user is relaxed. Furthermore, if the user is in a hurry, the anomaly detection unit can prioritize displaying anomalies that require immediate attention. This allows for prioritizing the display of anomaly detection results according to the user's emotions, thereby prioritizing the display of anomalies that are important to the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the anomaly detection unit may be performed using AI, or not. For example, the anomaly detection unit can input user emotion data into an AI, which can then adjust the order in which anomaly detection results are displayed.

[0087] The anomaly detection unit can perform anomaly detection while considering the geographical distribution of the data. For example, the anomaly detection unit can prioritize detecting data in areas where anomalies have occurred. The anomaly detection unit can also detect anomalies by considering geographically relevant data. Furthermore, the anomaly detection unit can adjust the range of anomaly detection based on the geographical distribution. This improves the accuracy of anomaly detection by considering the geographical distribution of the data. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input the geographical distribution of the data into the AI, which can then detect anomalies.

[0088] The anomaly detection unit can improve the accuracy of its detection by referring to relevant literature when detecting an anomaly. For example, the anomaly detection unit may refer to relevant literature to improve the accuracy of its anomaly detection algorithm. Furthermore, the anomaly detection unit may also improve detection accuracy by referring to appropriate literature depending on the type of anomaly. In addition, the anomaly detection unit may refer to relevant literature to verify the results of the anomaly detection. Thus, by referring to relevant literature, the accuracy of anomaly detection can be improved. Some or all of the above-described processes in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input relevant literature into AI, which can then detect anomalies.

[0089] The prediction unit can estimate the user's emotions and adjust the display method of the prediction based on the estimated user emotions. For example, if the user is nervous, the prediction unit can provide a simple and highly visible display method. If the user is relaxed, the prediction unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the prediction unit can provide a concise display method. In this way, by adjusting the display method of the prediction according to the user's emotions, the prediction results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into the AI, and the AI ​​can adjust the display method of the prediction.

[0090] The prediction unit can optimize the current prediction by referring to past prediction data during the prediction process. For example, the prediction unit can modify the current prediction based on past prediction data. The prediction unit can also compare past prediction data with current data to make the optimal prediction. Furthermore, the prediction unit can improve the accuracy of the prediction by referring to past prediction data. In this way, the current prediction can be optimized by referring to past prediction data. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past prediction data into AI, and the AI ​​can optimize the current prediction.

[0091] The prediction unit can apply different prediction methods to each data category during the prediction process. For example, the prediction unit can apply time series analysis to atmospheric data for prediction. It can also apply machine learning algorithms to water quality data for prediction. Furthermore, it can apply statistical models to climate data for prediction. This allows for optimal predictions for each data category by applying different prediction methods. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data categories into the AI, which can then apply different prediction methods.

[0092] The prediction unit can estimate the user's emotions and adjust the importance of predictions based on the estimated emotions. For example, if the user is stressed, the prediction unit can prioritize displaying important prediction results. It can also display detailed prediction results if the user is relaxed. Furthermore, if the user is in a hurry, the prediction unit can prioritize displaying prediction results that require immediate attention. This allows the system to prioritize prediction results that are important to the user by adjusting the importance of predictions according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, or not. For example, the prediction unit can input user emotion data into an AI, which can then adjust the importance of predictions.

[0093] The prediction unit can analyze changes in predictions based on the data collection timing during the prediction process. For example, the prediction unit can analyze changes in predictions based on the latest data. The prediction unit can also analyze changes in predictions by comparing past data with current data. Furthermore, the prediction unit can analyze changes in predictions in stages according to the data collection timing. This allows for predictions based on the latest data by analyzing changes in predictions based on the data collection timing. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the data collection timing into the AI, which can then analyze changes in predictions.

[0094] The forecasting unit can analyze its forecasts by referring to relevant market data during the forecasting process. For example, the forecasting unit can compare environmental data with relevant market data and analyze the forecast. The forecasting unit can also predict environmental changes by considering market trends. Furthermore, the forecasting unit can improve the accuracy of its forecasts by referring to relevant market data. In this way, the accuracy of the forecast can be improved by referring to relevant market data. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input relevant market data into AI, and the AI ​​can analyze the forecast. === Hard Collateral 1-1 === Each of the multiple elements described above, including the data collection unit, analysis unit, anomaly detection unit, and prediction unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects environmental data using the sensors of the smart device 14 and transmits it to the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The anomaly detection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and detects anomalies based on the analyzed data. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and predicts environmental changes. === Hard Collateral 1-2 === Each of the multiple elements described above, including the data collection unit, analysis unit, anomaly detection unit, and prediction unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects environmental data using the sensors of the smart glasses 214 and transmits it to the data processing unit 12. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The anomaly detection unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and detects anomalies based on the analyzed data. The prediction unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and predicts environmental changes. === Hard Collateral 1-3 === Each of the multiple elements described above, including the data collection unit, analysis unit, anomaly detection unit, and prediction unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects environmental data using the sensors of the headset terminal 314 and transmits it to the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The anomaly detection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and detects anomalies based on the analyzed data. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and predicts environmental changes. === Hard Collateral 1-4 === Each of the multiple elements described above, including the data collection unit, analysis unit, anomaly detection unit, and prediction unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects environmental data using the sensors of the robot 414 and transmits it to the data processing unit 12. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The anomaly detection unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and detects anomalies based on the analyzed data. The prediction unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and predicts environmental changes.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The analysis unit can dynamically adjust the data collection frequency. For example, if an anomaly is detected, the analysis unit can instruct the data collection unit to increase the data collection frequency. Conversely, if the anomaly is resolved, the analysis unit can instruct the data collection unit to decrease the data collection frequency. Furthermore, the analysis unit can automatically adjust the collection frequency when specific environmental conditions occur. This allows the analysis unit to dynamically adjust the data collection frequency in response to environmental changes, enabling more accurate data analysis.

[0097] The data collection unit can be equipped with a sensor self-diagnostic function. For example, the data collection unit can periodically check the operating status of the sensor and issue an alert if an abnormality is detected. The data collection unit can also automatically calibrate the sensor. Furthermore, the data collection unit can predict the sensor's lifespan and notify the user when it is time for replacement. This allows the data collection unit to maintain sensor reliability and collect accurate data.

[0098] The analysis unit can propose countermeasures based on the type of anomaly detected in the data. For example, if an anomaly in carbon dioxide concentration is detected, the analysis unit can recommend ventilation. If an anomaly in water quality is detected, it can suggest the use of a water purification system. Furthermore, if an anomaly in temperature is detected, it can suggest adjusting heating and cooling systems. In this way, the analysis unit can support a rapid response by proposing specific countermeasures tailored to the type of anomaly.

[0099] The data collection unit can optimize energy consumption during data acquisition. For example, it can adjust the sensor's operating mode to reduce energy consumption. It can also adjust the timing of data collection to maximize energy efficiency. Furthermore, it can select and use sensors with lower energy consumption. By optimizing energy consumption, the data collection unit can achieve sustainable data collection.

[0100] The analysis unit can visually display the results of data analysis. For example, it can display anomaly detection results in graphs and charts. It can also display predictions of environmental changes on a map. Furthermore, it can display data trends in time-series graphs. In this way, the analysis unit can help users intuitively understand the analysis results through visual displays.

[0101] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can send a concise notification. If the user is relaxed, it can also send a detailed analysis result. Furthermore, if the user is in a hurry, it can prioritize notifying only the most important information. In this way, the analysis unit can reduce the user's burden by selecting the appropriate notification method according to the user's emotions.

[0102] The data collection unit can estimate the user's emotions and adjust the sensor placement based on those emotions. For example, if the user is feeling anxious, the unit can place more sensors to collect more detailed data. Conversely, if the user is relaxed, the unit can reduce the number of sensors to conserve energy. Furthermore, if the user is in a hurry, the unit can concentrate sensors at key locations to collect data quickly. In this way, the data collection unit can achieve efficient data collection by adjusting the sensor placement according to the user's emotions.

[0103] The anomaly detection unit can estimate the user's emotions and adjust the notification method for anomalies based on those emotions. For example, if the user is stressed, the anomaly detection unit can send a concise notification. If the user is relaxed, it can also send detailed anomaly information. Furthermore, if the user is in a hurry, it can prioritize notifying only of important anomalies. In this way, the anomaly detection unit can reduce the user's burden by selecting the appropriate notification method according to the user's emotions.

[0104] The prediction unit can estimate the user's emotions and adjust how the prediction results are displayed based on those emotions. For example, if the user is stressed, the prediction unit can display concise results. If the user is relaxed, it can display detailed prediction results. Furthermore, if the user is in a hurry, it can prioritize displaying only the most important prediction results. In this way, the prediction unit can reduce the user's burden by selecting the appropriate display method according to the user's emotions.

[0105] The data collection unit can automatically switch the sensor's operating mode according to environmental conditions during data acquisition. For example, the unit can increase the sensor's sensitivity at night to collect more detailed data. It can also change the sensor's operating mode during adverse weather conditions to improve data collection accuracy. Furthermore, the unit can automatically switch the sensor's operating mode when specific environmental conditions occur. This allows the unit to optimize the sensor's operating mode according to environmental conditions, enabling accurate data collection.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The collection unit collects environmental data. This environmental data includes temperature, humidity, and carbon dioxide concentration. The collection unit collects data using sensors to measure atmospheric carbon dioxide concentration, water pH, and temperature. These sensors are placed in appropriate locations to collect data in real time. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis is performed using statistical analysis and machine learning algorithms. The analysis unit compares past data with current data to detect anomalies and risks. It can also detect anomalies using anomaly detection algorithms. Furthermore, it can predict environmental changes using time series analysis and machine learning algorithms. Step 3: The anomaly detection unit detects anomalies based on the data analyzed by the analysis unit. Anomalies include values ​​exceeding a threshold or abnormal patterns. The anomaly detection unit detects anomalies using anomaly detection algorithms, trend analysis, and machine learning algorithms. Step 4: The prediction unit predicts environmental changes based on the anomalies detected by the anomaly detection unit. Predictions are made using time series analysis, machine learning algorithms, and statistical models.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0110] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0111] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0115] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0116] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0118] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0119] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0120] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0121] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0122] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0126] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0152] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0161] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0162] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0163] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0164] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0165] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0166] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0168] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0169] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0170] 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.

[0171] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0172] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0173] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0174] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0175] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0176] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0177] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0178] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0179] [Explanation of Symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects environmental data, An analysis unit analyzes the data collected by the aforementioned collection unit, An anomaly detection unit that detects anomalies based on the data analyzed by the analysis unit, The system includes a prediction unit that predicts environmental changes based on the abnormality detected by the abnormality detection unit. A system characterized by the following features.

2. The aforementioned collection unit is It is equipped with a sensor that measures the concentration of carbon dioxide in the atmosphere. The system according to feature 1.

3. The aforementioned collection unit is It is equipped with a sensor that measures the pH value of water. The system according to feature 1.

4. The aforementioned collection unit is Equipped with a sensor to measure temperature. The system according to feature 1.

5. The aforementioned analysis unit, By comparing past and present data, anomalies and risks are detected. The system according to feature 1.

6. The abnormality detection unit, Anomalies are detected using an anomaly detection algorithm. The system according to feature 1.

7. The prediction unit, Predicting environmental changes using time series analysis and machine learning algorithms The system according to feature 1.

8. The prediction unit, Supporting strategic decisions based on prediction results. The system according to feature 1.

9. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

Images