System

Abstract

An object of a system according to an embodiment is to quickly detect an abnormality and immediately notify a related infrastructure.SOLUTION: A system includes a sensor part, a generation AI, and a notifying part. The sensor unit collects data from various sensors. The generation AI analyzes the date collected by the sensor unit and detects an abnormality. The notifier notifies a related infrastructure facility of the abnormality detected by the generated AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it takes time to detect an abnormality and notify each infrastructure organization, making it difficult to respond quickly.

[0005] The system according to the embodiment aims to quickly detect anomalies and immediately notify relevant infrastructure organizations. [Means for solving the problem]

[0006] The system according to the embodiment includes a sensor unit, a generation AI, and a notification unit. The sensor unit collects data from various sensors. The generation AI analyzes the data collected by the sensor unit and detects anomalies. The notification unit notifies relevant infrastructure organizations of anomalies detected by the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can quickly detect anomalies and immediately notify relevant infrastructure organizations. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The security system according to the embodiment of the present invention uses AI to detect abnormalities in advance before the emergency button is pressed, and realizes a prompt and effective security system for each infrastructure facility. This enables the security system to take measures before an emergency occurs, minimizing damage.

[0029] A security system according to an embodiment includes a sensor unit, a generation AI, and a notification unit. The sensor unit collects data from various sensors. Examples of the sensors include a temperature sensor, a humidity sensor, and a pressure sensor. The sensor unit collects temperature data using a temperature sensor, for example. The sensor unit can also collect humidity data using a humidity sensor, for example. The sensor unit can also collect pressure data using a pressure sensor, for example. The generation AI analyzes the data collected by the sensor unit and detects anomalies. For example, the generation AI analyzes the data using statistical analysis. The generation AI can also analyze the data using a machine learning algorithm. The generation AI can also detect anomalies using pattern recognition. For example, the generation AI detects abnormal values ​​in the data using statistical analysis. The generation AI learns abnormal patterns in the data using a machine learning algorithm and detects anomalies. The generation AI detects abnormal patterns in the data using pattern recognition. The notification unit notifies relevant infrastructure organizations of the anomaly detected by the generation AI. For example, the notification unit notifies an electric power company of the anomaly. The notification unit can also notify a waterworks bureau of the anomaly. The notification unit can also notify a telecommunications company of the abnormality. For example, the notification unit can notify the abnormality by email. The notification unit can also notify the abnormality by SMS. The notification unit can also notify the abnormality by using a dedicated application. This allows the security system according to the embodiment to detect the abnormality before the emergency button is operated, enabling a quick and effective response.

[0030] The sensor unit can add an environmental sound sensor and an odor sensor to detect signs of abnormality from multiple angles. The sensor unit, for example, is equipped with an environmental sound sensor to detect abnormal sounds. For example, it detects strange sounds or impact sounds that differ from normal operating sounds and analyzes them as signs of abnormality. The sensor unit can also incorporate an odor sensor to detect gas leaks or abnormal emissions of chemical substances. For example, it can issue an alarm if the concentration of a specific chemical substance increases. The sensor unit can also combine the environmental sound and odor sensors to detect signs of abnormality from multiple angles. For example, if an abnormal sound and an abnormal odor are detected simultaneously, it can issue an alarm with a higher priority. This improves detection accuracy by detecting signs of abnormality from multiple angles.

[0031] The sensor unit can use drones to focus on monitoring areas where abnormalities are likely to occur. For example, the sensor unit mounts sensors on drones and has them patrol areas where abnormalities are likely to occur. For example, it can focus on monitoring high-temperature areas in a factory or storage areas for hazardous materials. The sensor unit can also dynamically change the drone's flight route to intensively monitor areas where abnormalities are detected. For example, if an abnormal temperature rise is detected, it can focus on patrolling that area. The sensor unit can also use multiple drones to efficiently monitor a wide area. For example, drones can be deployed to cover the entire factory, allowing for quick response to areas where abnormalities occur. This allows for focused monitoring of areas where abnormalities are likely to occur, making it possible to detect abnormalities early.

[0032] The sensor unit can share data with other infrastructure organizations and build a cooperative system for mutual monitoring of abnormalities. For example, the sensor unit could build a system that shares data from sensors with other infrastructure organizations in real time. For example, an electric power company and a waterworks bureau could share data and mutually monitor for abnormalities. The sensor unit could also develop a system that automatically notifies related infrastructure organizations when an abnormality is detected. For example, if a gas leak is detected, the sensor unit could notify the electric power company and the telecommunications company simultaneously. The sensor unit could also share data with other infrastructure organizations to achieve early detection of abnormalities and rapid response. For example, data on an area where an abnormality has occurred could be shared, and each organization could cooperate to take measures. In this way, data sharing enables early detection of abnormalities and rapid response.

[0033] The sensor unit collects data in an energy-efficient manner, enabling a sustainable monitoring system to be realized. The sensor unit, for example, uses energy-efficient sensors to collect data. For example, low-power temperature sensors and pressure sensors are introduced. The sensor unit also collects sensor data intermittently to reduce energy consumption. For example, data is collected at regular intervals, and data is collected continuously only when an abnormality is detected. The sensor unit also builds a system that uses renewable energy to operate the sensors. For example, solar power or wind power is used to supply power to the sensors. This allows a sustainable monitoring system to be realized by collecting data in an energy-efficient manner.

[0034] Generative AI can learn from past abnormal data and improve the accuracy of anomaly predictions. Generative AI, for example, learns from past abnormal data to improve the accuracy of anomaly predictions. For example, it learns abnormality patterns based on past data on temperature increases and pressure fluctuations. Generative AI also regularly updates abnormal data and builds a system that predicts abnormalities based on the latest data. For example, it adds newly occurring abnormal data and updates the AI ​​learning model. Generative AI also shares abnormal data on the cloud and develops a system in which multiple facilities and infrastructure organizations can jointly learn abnormal data. For example, it integrates abnormal data from different facilities and strengthens the AI ​​learning model. In this way, by learning from past abnormal data, the accuracy of anomaly predictions improves.

[0035] Generative AI performs detailed analysis to identify the cause of an abnormality, allowing for swift countermeasures to be taken. For example, when an abnormality is detected, generative AI performs detailed analysis to identify the cause of the anomaly. For example, it analyzes data from related sensors to identify the cause of an abnormal temperature rise. Generative AI also builds a system that references past data and related information to identify the cause of the anomaly. For example, it identifies the cause based on past abnormality cases and maintenance history. Generative AI also develops a system that, after identifying the cause of the anomaly, makes suggestions for swift countermeasures to be taken. For example, if an abnormal temperature rise is detected, it suggests adjusting the cooling system. This makes it possible to identify the cause of the abnormality and take swift countermeasures, minimizing damage.

[0036] Generative AI can share anomaly detection algorithms with other infrastructure organizations to promote early detection of anomalies. For example, generative AI can share anomaly detection algorithms with other infrastructure organizations to build systems that promote early detection of anomalies. For example, electric power companies and waterworks bureaus use the same algorithm to detect anomalies. Generative AI can also share anomaly detection algorithms on the cloud to develop systems in which multiple infrastructure organizations jointly detect anomalies. For example, data from different facilities can be integrated to improve the accuracy of anomaly detection. Generative AI can also regularly update anomaly detection algorithms to build systems that detect anomalies based on the latest technology and data. For example, newly discovered abnormal patterns can be reflected in the algorithm. In this way, sharing anomaly detection algorithms promotes early detection of anomalies.

[0037] Generative AI can visualize and display the results of anomaly detection, providing an interface that is intuitively easy to understand. For example, generative AI builds a system that visualizes the results of anomaly detection and provides an interface that is intuitively easy to understand. For example, it displays the area where the anomaly occurred on a map. Generative AI can also display the results of anomaly detection in graphs and charts, allowing the details of the anomaly to be visually grasped. For example, it displays graphs of temperature rise and pressure fluctuation. Generative AI can also develop an interface that updates the results of anomaly detection in real time and always displays the latest information. For example, maps and graphs are automatically updated each time an anomaly is detected. In this way, by visualizing the results of anomaly detection, it is possible to provide an interface that is intuitively easy to understand.

[0038] The notification unit can simultaneously provide detailed information about the abnormality and recommended countermeasures, supporting a rapid response. For example, the notification unit will build a system that, when an abnormality is detected, provides detailed information about the abnormality at the same time as the notification. For example, the cause and extent of the impact of an abnormal temperature rise will be included in the notification. The notification unit will also simultaneously provide recommended countermeasures in the notification, supporting a rapid response. For example, when an abnormal pressure fluctuation is detected, the notification will include pressure adjustment procedures. The notification unit will also develop a system that automatically generates detailed information about the abnormality and recommended countermeasures and includes them in the notification. For example, when an abnormal vibration pattern is detected, the cause of the vibration and countermeasures will be included in the notification. This allows for a rapid response by providing detailed information about the abnormality and countermeasures at the same time.

[0039] The notification unit can set the priority of anomalies and encourage a response according to their importance. The notification unit, for example, builds a notification system that sets the priority of anomalies and encourages a response according to their importance. For example, it issues a high-priority notification for serious anomalies and encourages a prompt response. The notification unit also develops a system that automatically determines the priority of anomalies and reflects this in notifications. For example, it sets the priority based on the scope of the anomaly's impact and frequency of occurrence. The notification unit also issues notifications according to priority, allowing infrastructure organizations to respond efficiently. For example, it encourages regular monitoring for low-priority anomalies and encourages an immediate response for high-priority anomalies. In this way, by setting the priority of anomalies, it becomes possible to respond according to their importance.

[0040] The notification department can integrate notification systems between different infrastructure agencies and build a platform for sharing anomaly information. For example, the notification department integrates notification systems between different infrastructure agencies and builds a platform for sharing anomaly information. For example, an electric power company and a waterworks bureau share anomaly information using the same platform. The notification department also develops a system for sharing anomaly information in real time, enabling each infrastructure agency to respond quickly. For example, when an anomaly is detected, the relevant infrastructure agency is automatically notified. The notification department also builds a platform that promotes information sharing between different infrastructure agencies and enables early detection of anomalies and rapid response. For example, anomaly information is shared on the cloud, allowing each agency to access it. In this way, sharing anomaly information enables early detection of anomalies and rapid response.

[0041] The notification unit can diversify notification methods and provide notifications through voice assistants and smart devices. The notification unit, for example, diversifies notification methods and builds a system that provides notifications through voice assistants. For example, if an abnormality is detected, the voice assistant notifies the responsible person. The notification unit also develops a system that provides notifications through smart devices. For example, notifications can be sent to smartphones or smartwatches so that the responsible person can respond immediately. The notification unit also diversifies notification methods and builds a system that provides notifications through multiple devices. For example, notifications can be sent by a combination of email, SMS, voice assistants, and smart devices. This diversification of notification methods enables quick and effective notifications.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The sensor unit may be equipped with, for example, an environmental sound sensor to detect abnormal sounds. For example, it may detect unusual sounds or impact sounds that differ from normal operating sounds and analyze them as signs of an abnormality. The sensor unit may also incorporate an odor sensor to detect gas leaks or abnormal emissions of chemical substances. For example, it may issue an alarm if the concentration of a specific chemical substance increases. The sensor unit may also combine environmental sound and odor sensors to detect signs of an abnormality from multiple angles. For example, if an abnormal sound and an abnormal odor are detected simultaneously, it may issue an alarm with a higher priority. This improves detection accuracy by detecting signs of an abnormality from multiple angles.

[0044] The sensor unit, for example, mounts sensors on a drone and has it patrol areas where abnormalities are likely to occur. For example, it can focus on monitoring high-temperature areas in a factory or storage areas for hazardous materials. The sensor unit also dynamically changes the drone's flight route to intensively monitor areas where abnormalities are detected. For example, if an abnormal temperature rise is detected, it will focus on patrolling that area. The sensor unit can also use multiple drones to efficiently monitor a wide area. For example, drones can be deployed to cover the entire factory, and areas where abnormalities occur can be quickly responded to. This enables early detection of abnormalities by focusing on monitoring areas where abnormalities are likely to occur.

[0045] The sensor unit can share data with other infrastructure organizations and build a cooperative system for mutual monitoring of abnormalities. For example, the sensor unit could build a system that shares data from sensors with other infrastructure organizations in real time. For example, an electric power company and a waterworks bureau could share data and mutually monitor for abnormalities. The sensor unit could also develop a system that automatically notifies related infrastructure organizations when an abnormality is detected. For example, if a gas leak is detected, the sensor unit could notify the electric power company and the telecommunications company simultaneously. The sensor unit could also share data with other infrastructure organizations to achieve early detection of abnormalities and rapid response. For example, data on an area where an abnormality has occurred could be shared, and each organization could cooperate to take measures. In this way, data sharing enables early detection of abnormalities and rapid response.

[0046] The sensor unit collects data in an energy-efficient manner, enabling a sustainable monitoring system to be realized. The sensor unit, for example, uses energy-efficient sensors to collect data. For example, low-power temperature sensors and pressure sensors are introduced. The sensor unit also collects sensor data intermittently to reduce energy consumption. For example, data is collected at regular intervals, and data is collected continuously only when an abnormality is detected. The sensor unit also builds a system that uses renewable energy to operate the sensors. For example, solar power or wind power is used to supply power to the sensors. This allows a sustainable monitoring system to be realized by collecting data in an energy-efficient manner.

[0047] Generative AI can learn from past abnormal data and improve the accuracy of anomaly predictions. Generative AI, for example, learns from past abnormal data to improve the accuracy of anomaly predictions. For example, it learns abnormality patterns based on past data on temperature increases and pressure fluctuations. Generative AI also regularly updates abnormal data and builds a system that predicts abnormalities based on the latest data. For example, it adds newly occurring abnormal data and updates the AI ​​learning model. Generative AI also shares abnormal data on the cloud and develops a system in which multiple facilities and infrastructure organizations can jointly learn abnormal data. For example, it integrates abnormal data from different facilities and strengthens the AI ​​learning model. In this way, by learning from past abnormal data, the accuracy of anomaly predictions improves.

[0048] Generative AI performs detailed analysis to identify the cause of an abnormality, allowing for swift countermeasures to be taken. For example, when an abnormality is detected, generative AI performs detailed analysis to identify the cause of the anomaly. For example, it analyzes data from related sensors to identify the cause of an abnormal temperature rise. Generative AI also builds a system that references past data and related information to identify the cause of the anomaly. For example, it identifies the cause based on past abnormality cases and maintenance history. Generative AI also develops a system that, after identifying the cause of the anomaly, makes suggestions for swift countermeasures to be taken. For example, if an abnormal temperature rise is detected, it suggests adjusting the cooling system. This makes it possible to identify the cause of the abnormality and take swift countermeasures, minimizing damage.

[0049] The notification unit can simultaneously provide detailed information about the abnormality and recommended countermeasures, supporting a rapid response. For example, the notification unit will build a system that, when an abnormality is detected, provides detailed information about the abnormality at the same time as the notification. For example, the cause and extent of the impact of an abnormal temperature rise will be included in the notification. The notification unit will also simultaneously provide recommended countermeasures in the notification, supporting a rapid response. For example, when an abnormal pressure fluctuation is detected, the notification will include pressure adjustment procedures. The notification unit will also develop a system that automatically generates detailed information about the abnormality and recommended countermeasures and includes them in the notification. For example, when an abnormal vibration pattern is detected, the cause of the vibration and countermeasures will be included in the notification. This allows for a rapid response by providing detailed information about the abnormality and countermeasures at the same time.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The sensor unit collects data from various sensors. For example, these include a temperature sensor, a humidity sensor, and a pressure sensor. The sensor unit collects temperature data using the temperature sensor, humidity data using the humidity sensor, and pressure data using the pressure sensor. Step 2: The generation AI analyzes the data collected by the sensor unit and detects abnormalities. The generation AI analyzes the data using statistical analysis, machine learning algorithms, and pattern recognition to detect outliers and abnormal patterns. Step 3: The notification unit notifies the relevant infrastructure organizations of the anomaly detected by the generation AI. The notification unit notifies the electric power company, waterworks bureau, telecommunications company, etc. of the anomaly via email, SMS, or a dedicated application.

[0052] (Example 2) The security system according to the embodiment of the present invention uses AI to detect abnormalities in advance before the emergency button is pressed, and realizes a prompt and effective security system for each infrastructure facility. This enables the security system to take measures before an emergency occurs, minimizing damage.

[0053] A security system according to an embodiment includes a sensor unit, a generation AI, and a notification unit. The sensor unit collects data from various sensors. Examples of the sensors include a temperature sensor, a humidity sensor, and a pressure sensor. The sensor unit collects temperature data using a temperature sensor, for example. The sensor unit can also collect humidity data using a humidity sensor, for example. The sensor unit can also collect pressure data using a pressure sensor, for example. The generation AI analyzes the data collected by the sensor unit and detects anomalies. For example, the generation AI analyzes the data using statistical analysis. The generation AI can also analyze the data using a machine learning algorithm. The generation AI can also detect anomalies using pattern recognition. For example, the generation AI detects abnormal values ​​in the data using statistical analysis. The generation AI learns abnormal patterns in the data using a machine learning algorithm and detects anomalies. The generation AI detects abnormal patterns in the data using pattern recognition. The notification unit notifies relevant infrastructure organizations of the anomaly detected by the generation AI. For example, the notification unit notifies an electric power company of the anomaly. The notification unit can also notify a waterworks bureau of the anomaly. The notification unit can also notify a telecommunications company of the abnormality. For example, the notification unit can notify the abnormality by email. The notification unit can also notify the abnormality by SMS. The notification unit can also notify the abnormality by using a dedicated application. This allows the security system according to the embodiment to detect the abnormality before the emergency button is operated, enabling a quick and effective response.

[0054] The sensor unit can add an environmental sound sensor and an odor sensor to detect signs of abnormality from multiple angles. The sensor unit, for example, is equipped with an environmental sound sensor to detect abnormal sounds. For example, it detects strange sounds or impact sounds that differ from normal operating sounds and analyzes them as signs of abnormality. The sensor unit can also incorporate an odor sensor to detect gas leaks or abnormal emissions of chemical substances. For example, it can issue an alarm if the concentration of a specific chemical substance increases. The sensor unit can also combine the environmental sound and odor sensors to detect signs of abnormality from multiple angles. For example, if an abnormal sound and an abnormal odor are detected simultaneously, it can issue an alarm with a higher priority. This improves detection accuracy by detecting signs of abnormality from multiple angles.

[0055] The sensor unit can use drones to focus on monitoring areas where abnormalities are likely to occur. For example, the sensor unit mounts sensors on drones and has them patrol areas where abnormalities are likely to occur. For example, it can focus on monitoring high-temperature areas in a factory or storage areas for hazardous materials. The sensor unit can also dynamically change the drone's flight route to intensively monitor areas where abnormalities are detected. For example, if an abnormal temperature rise is detected, it can focus on patrolling that area. The sensor unit can also use multiple drones to efficiently monitor a wide area. For example, drones can be deployed to cover the entire factory, allowing for quick response to areas where abnormalities occur. This allows for focused monitoring of areas where abnormalities are likely to occur, making it possible to detect abnormalities early.

[0056] The sensor unit can use the emotion estimation function to collect emotional data of people in the facility and use it as a sign of abnormalities. For example, the sensor unit uses the emotion estimation function to analyze the facial expressions and voices of people in the facility to collect emotional data. For example, it detects increased stress or anxiety. The sensor unit also builds a system that detects signs of abnormalities early based on the emotional data. For example, it issues an alarm if multiple people are feeling anxious at the same time. The sensor unit also combines and analyzes the emotional data with data from other sensors to detect signs of abnormalities from multiple angles. For example, if a rise in temperature and people's anxiety are detected simultaneously, it issues an alarm with a higher priority. In this way, by using emotional data, signs of abnormalities can be detected early.

[0057] The sensor unit can share data with other infrastructure organizations and build a cooperative system for mutual monitoring of abnormalities. For example, the sensor unit could build a system that shares data from sensors with other infrastructure organizations in real time. For example, an electric power company and a waterworks bureau could share data and mutually monitor for abnormalities. The sensor unit could also develop a system that automatically notifies related infrastructure organizations when an abnormality is detected. For example, if a gas leak is detected, the sensor unit could notify the electric power company and the telecommunications company simultaneously. The sensor unit could also share data with other infrastructure organizations to achieve early detection of abnormalities and rapid response. For example, data on an area where an abnormality has occurred could be shared, and each organization could cooperate to take measures. In this way, data sharing enables early detection of abnormalities and rapid response.

[0058] The sensor unit collects data in an energy-efficient manner, enabling a sustainable monitoring system to be realized. The sensor unit, for example, uses energy-efficient sensors to collect data. For example, low-power temperature sensors and pressure sensors are introduced. The sensor unit also collects sensor data intermittently to reduce energy consumption. For example, data is collected at regular intervals, and data is collected continuously only when an abnormality is detected. The sensor unit also builds a system that uses renewable energy to operate the sensors. For example, solar power or wind power is used to supply power to the sensors. This allows a sustainable monitoring system to be realized by collecting data in an energy-efficient manner.

[0059] The sensor unit uses the emotion estimation function to monitor the stress levels of people in the facility, which can be useful for early detection of abnormalities. The sensor unit, for example, uses the emotion estimation function to monitor the stress levels of people in the facility in real time. For example, it analyzes facial expressions and voice to calculate a stress score. The sensor unit also builds a system that detects signs of abnormalities early based on stress level data. For example, it issues an alarm if the stress level suddenly rises. The sensor unit also combines stress level data with data from other sensors and analyzes it to detect signs of abnormalities from multiple angles. For example, if an increase in temperature and an increase in stress level are detected simultaneously, it issues an alarm with a higher priority. In this way, monitoring stress levels enables early detection of abnormalities.

[0060] Generative AI can learn from past abnormal data and improve the accuracy of anomaly predictions. Generative AI, for example, learns from past abnormal data to improve the accuracy of anomaly predictions. For example, it learns abnormality patterns based on past data on temperature increases and pressure fluctuations. Generative AI also regularly updates abnormal data and builds a system that predicts abnormalities based on the latest data. For example, it adds newly occurring abnormal data and updates the AI ​​learning model. Generative AI also shares abnormal data on the cloud and develops a system in which multiple facilities and infrastructure organizations can jointly learn abnormal data. For example, it integrates abnormal data from different facilities and strengthens the AI ​​learning model. In this way, by learning from past abnormal data, the accuracy of anomaly predictions improves.

[0061] Generative AI performs detailed analysis to identify the cause of an abnormality, allowing for swift countermeasures to be taken. For example, when an abnormality is detected, generative AI performs detailed analysis to identify the cause of the anomaly. For example, it analyzes data from related sensors to identify the cause of an abnormal temperature rise. Generative AI also builds a system that references past data and related information to identify the cause of the anomaly. For example, it identifies the cause based on past abnormality cases and maintenance history. Generative AI also develops a system that, after identifying the cause of the anomaly, makes suggestions for swift countermeasures to be taken. For example, if an abnormal temperature rise is detected, it suggests adjusting the cooling system. This makes it possible to identify the cause of the abnormality and take swift countermeasures, minimizing damage.

[0062] The generation AI can use its emotion estimation function to analyze changes in the emotions of people in a facility when an abnormality is detected and assess the extent of the anomaly's impact. For example, the generation AI uses its emotion estimation function to analyze changes in the emotions of people in a facility in real time when an abnormality is detected. For example, it can detect feelings of anxiety or fear among people in the area where the abnormality occurred. The generation AI also builds a system to assess the extent of the anomaly's impact based on the emotion data. For example, if the emotional changes are widespread, it can determine that the anomaly has a large impact. The generation AI also analyzes the emotion data in combination with data from other sensors to assess the extent of the anomaly's impact from multiple angles. For example, if an abnormal temperature rise and people's anxiety are detected simultaneously, it can set a wide impact range. This makes it possible to evaluate the extent of the anomaly's impact by analyzing emotional changes and take appropriate action.

[0063] Generative AI can share anomaly detection algorithms with other infrastructure organizations to promote early detection of anomalies. For example, generative AI can share anomaly detection algorithms with other infrastructure organizations to build systems that promote early detection of anomalies. For example, electric power companies and waterworks bureaus use the same algorithm to detect anomalies. Generative AI can also share anomaly detection algorithms on the cloud to develop systems in which multiple infrastructure organizations jointly detect anomalies. For example, data from different facilities can be integrated to improve the accuracy of anomaly detection. Generative AI can also regularly update anomaly detection algorithms to build systems that detect anomalies based on the latest technology and data. For example, newly discovered abnormal patterns can be reflected in the algorithm. In this way, sharing anomaly detection algorithms promotes early detection of anomalies.

[0064] Generative AI can visualize and display the results of anomaly detection, providing an interface that is intuitively easy to understand. For example, generative AI builds a system that visualizes the results of anomaly detection and provides an interface that is intuitively easy to understand. For example, it displays the area where the anomaly occurred on a map. Generative AI can also display the results of anomaly detection in graphs and charts, allowing the details of the anomaly to be visually grasped. For example, it displays graphs of temperature rise and pressure fluctuation. Generative AI can also develop an interface that updates the results of anomaly detection in real time and always displays the latest information. For example, maps and graphs are automatically updated each time an anomaly is detected. In this way, by visualizing the results of anomaly detection, it is possible to provide an interface that is intuitively easy to understand.

[0065] The generative AI can use its emotion estimation function to collect emotional data from people in a facility when an abnormality is detected and evaluate the impact of the abnormality. For example, the generative AI uses its emotion estimation function to collect emotional data from people in a facility in real time when an abnormality is detected. For example, it can detect emotions of anxiety or fear from people in the area where the abnormality occurred. The generative AI then builds a system to evaluate the impact of the abnormality based on the emotional data. For example, if emotional changes are widespread, it can determine that the impact of the abnormality is significant. The generative AI can also analyze the emotional data in combination with data from other sensors to evaluate the impact of the abnormality from multiple perspectives. For example, if an abnormal temperature rise and people's anxiety are detected simultaneously, it can set a wide impact range. This makes it possible to evaluate the impact of the abnormality by collecting emotional data and take appropriate action.

[0066] The notification unit can simultaneously provide detailed information about the abnormality and recommended countermeasures, supporting a rapid response. For example, the notification unit will build a system that, when an abnormality is detected, provides detailed information about the abnormality at the same time as the notification. For example, the cause and extent of the impact of an abnormal temperature rise will be included in the notification. The notification unit will also simultaneously provide recommended countermeasures in the notification, supporting a rapid response. For example, when an abnormal pressure fluctuation is detected, the notification will include pressure adjustment procedures. The notification unit will also develop a system that automatically generates detailed information about the abnormality and recommended countermeasures and includes them in the notification. For example, when an abnormal vibration pattern is detected, the cause of the vibration and countermeasures will be included in the notification. This allows for a rapid response by providing detailed information about the abnormality and countermeasures at the same time.

[0067] The notification unit can set the priority of anomalies and encourage a response according to their importance. The notification unit, for example, builds a notification system that sets the priority of anomalies and encourages a response according to their importance. For example, it issues a high-priority notification for serious anomalies and encourages a prompt response. The notification unit also develops a system that automatically determines the priority of anomalies and reflects this in notifications. For example, it sets the priority based on the scope of the anomaly's impact and frequency of occurrence. The notification unit also issues notifications according to priority, allowing infrastructure organizations to respond efficiently. For example, it encourages regular monitoring for low-priority anomalies and encourages an immediate response for high-priority anomalies. In this way, by setting the priority of anomalies, it becomes possible to respond according to their importance.

[0068] The notification unit uses the emotion estimation function to consider the emotional state of the person in charge receiving the notification and can provide the notification at the optimal timing. The notification unit, for example, uses the emotion estimation function to analyze the emotional state of the person in charge receiving the notification in real time. For example, if the person in charge is feeling stressed, the notification unit delays the notification. The notification unit also builds a system that considers the emotional state of the person in charge and provides the notification at the optimal timing. For example, the notification is provided when the person in charge is relaxed. The notification unit also develops a system that dynamically adjusts the timing of the notification based on the emotion estimation data. For example, the notification is provided when the person in charge's emotional state has improved. In this way, the notification can be provided at the optimal timing by considering the emotional state of the person in charge.

[0069] The notification department can integrate notification systems between different infrastructure agencies and build a platform for sharing anomaly information. For example, the notification department integrates notification systems between different infrastructure agencies and builds a platform for sharing anomaly information. For example, an electric power company and a waterworks bureau share anomaly information using the same platform. The notification department also develops a system for sharing anomaly information in real time, enabling each infrastructure agency to respond quickly. For example, when an anomaly is detected, the relevant infrastructure agency is automatically notified. The notification department also builds a platform that promotes information sharing between different infrastructure agencies and enables early detection of anomalies and rapid response. For example, anomaly information is shared on the cloud, allowing each agency to access it. In this way, sharing anomaly information enables early detection of anomalies and rapid response.

[0070] The notification unit can diversify notification methods and provide notifications through voice assistants and smart devices. The notification unit, for example, diversifies notification methods and builds a system that provides notifications through voice assistants. For example, if an abnormality is detected, the voice assistant notifies the responsible person. The notification unit also develops a system that provides notifications through smart devices. For example, notifications can be sent to smartphones or smartwatches so that the responsible person can respond immediately. The notification unit also diversifies notification methods and builds a system that provides notifications through multiple devices. For example, notifications can be sent by a combination of email, SMS, voice assistants, and smart devices. This diversification of notification methods enables quick and effective notifications.

[0071] The notification unit can use the emotion estimation function to monitor the emotional state of the person in charge who receives the notification and provide support to reduce stress. The notification unit, for example, uses the emotion estimation function to monitor the emotional state of the person in charge who receives the notification in real time. For example, if the person in charge is feeling stressed, the notification unit provides advice on how to relax. The notification unit also builds a system that provides support to reduce stress based on the emotional state of the person in charge. For example, if stress is increasing, the notification unit sends a notification encouraging the person in charge to take a break. The notification unit also develops a system that provides support to reduce stress for the person in charge based on the emotion estimation data. For example, the system provides music or a meditation guide to help them relax. This enables effective response by monitoring the emotional state of the person in charge and providing support to reduce stress.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The sensor unit may be equipped with, for example, an environmental sound sensor to detect abnormal sounds. For example, it may detect unusual sounds or impact sounds that differ from normal operating sounds and analyze them as signs of an abnormality. The sensor unit may also incorporate an odor sensor to detect gas leaks or abnormal emissions of chemical substances. For example, it may issue an alarm if the concentration of a specific chemical substance increases. The sensor unit may also combine environmental sound and odor sensors to detect signs of an abnormality from multiple angles. For example, if an abnormal sound and an abnormal odor are detected simultaneously, it may issue an alarm with a higher priority. This improves detection accuracy by detecting signs of an abnormality from multiple angles.

[0074] The sensor unit, for example, mounts sensors on a drone and has it patrol areas where abnormalities are likely to occur. For example, it can focus on monitoring high-temperature areas in a factory or storage areas for hazardous materials. The sensor unit also dynamically changes the drone's flight route to intensively monitor areas where abnormalities are detected. For example, if an abnormal temperature rise is detected, it will focus on patrolling that area. The sensor unit can also use multiple drones to efficiently monitor a wide area. For example, drones can be deployed to cover the entire factory, and areas where abnormalities occur can be quickly responded to. This enables early detection of abnormalities by focusing on monitoring areas where abnormalities are likely to occur.

[0075] The sensor unit can use the emotion estimation function to collect emotional data of people in the facility and use it as a sign of abnormalities. For example, the sensor unit uses the emotion estimation function to analyze the facial expressions and voices of people in the facility to collect emotional data. For example, it detects increased stress or anxiety. The sensor unit also builds a system that detects signs of abnormalities early based on the emotional data. For example, it issues an alarm if multiple people are feeling anxious at the same time. The sensor unit also combines and analyzes the emotional data with data from other sensors to detect signs of abnormalities from multiple angles. For example, if a rise in temperature and people's anxiety are detected simultaneously, it issues an alarm with a higher priority. In this way, by using emotional data, signs of abnormalities can be detected early.

[0076] The sensor unit can share data with other infrastructure organizations and build a cooperative system for mutual monitoring of abnormalities. For example, the sensor unit could build a system that shares data from sensors with other infrastructure organizations in real time. For example, an electric power company and a waterworks bureau could share data and mutually monitor for abnormalities. The sensor unit could also develop a system that automatically notifies related infrastructure organizations when an abnormality is detected. For example, if a gas leak is detected, the sensor unit could notify the electric power company and the telecommunications company simultaneously. The sensor unit could also share data with other infrastructure organizations to achieve early detection of abnormalities and rapid response. For example, data on an area where an abnormality has occurred could be shared, and each organization could cooperate to take measures. In this way, data sharing enables early detection of abnormalities and rapid response.

[0077] The sensor unit collects data in an energy-efficient manner, enabling a sustainable monitoring system to be realized. The sensor unit, for example, uses energy-efficient sensors to collect data. For example, low-power temperature sensors and pressure sensors are introduced. The sensor unit also collects sensor data intermittently to reduce energy consumption. For example, data is collected at regular intervals, and data is collected continuously only when an abnormality is detected. The sensor unit also builds a system that uses renewable energy to operate the sensors. For example, solar power or wind power is used to supply power to the sensors. This allows a sustainable monitoring system to be realized by collecting data in an energy-efficient manner.

[0078] The sensor unit uses the emotion estimation function to monitor the stress levels of people in the facility, which can be useful for early detection of abnormalities. The sensor unit, for example, uses the emotion estimation function to monitor the stress levels of people in the facility in real time. For example, it analyzes facial expressions and voice to calculate a stress score. The sensor unit also builds a system that detects signs of abnormalities early based on stress level data. For example, it issues an alarm if the stress level suddenly rises. The sensor unit also combines stress level data with data from other sensors and analyzes it to detect signs of abnormalities from multiple angles. For example, if an increase in temperature and an increase in stress level are detected simultaneously, it issues an alarm with a higher priority. In this way, monitoring stress levels enables early detection of abnormalities.

[0079] Generative AI can learn from past abnormal data and improve the accuracy of anomaly predictions. Generative AI, for example, learns from past abnormal data to improve the accuracy of anomaly predictions. For example, it learns abnormality patterns based on past data on temperature increases and pressure fluctuations. Generative AI also regularly updates abnormal data and builds a system that predicts abnormalities based on the latest data. For example, it adds newly occurring abnormal data and updates the AI ​​learning model. Generative AI also shares abnormal data on the cloud and develops a system in which multiple facilities and infrastructure organizations can jointly learn abnormal data. For example, it integrates abnormal data from different facilities and strengthens the AI ​​learning model. In this way, by learning from past abnormal data, the accuracy of anomaly predictions improves.

[0080] Generative AI performs detailed analysis to identify the cause of an abnormality, allowing for swift countermeasures to be taken. For example, when an abnormality is detected, generative AI performs detailed analysis to identify the cause of the anomaly. For example, it analyzes data from related sensors to identify the cause of an abnormal temperature rise. Generative AI also builds a system that references past data and related information to identify the cause of the anomaly. For example, it identifies the cause based on past abnormality cases and maintenance history. Generative AI also develops a system that, after identifying the cause of the anomaly, makes suggestions for swift countermeasures to be taken. For example, if an abnormal temperature rise is detected, it suggests adjusting the cooling system. This makes it possible to identify the cause of the abnormality and take swift countermeasures, minimizing damage.

[0081] The generation AI can use its emotion estimation function to analyze changes in the emotions of people in a facility when an abnormality is detected and assess the extent of the anomaly's impact. For example, the generation AI uses its emotion estimation function to analyze changes in the emotions of people in a facility in real time when an abnormality is detected. For example, it can detect feelings of anxiety or fear among people in the area where the abnormality occurred. The generation AI also builds a system to assess the extent of the anomaly's impact based on the emotion data. For example, if the emotional changes are widespread, it can determine that the anomaly has a large impact. The generation AI also analyzes the emotion data in combination with data from other sensors to assess the extent of the anomaly's impact from multiple angles. For example, if an abnormal temperature rise and people's anxiety are detected simultaneously, it can set a wide impact range. This makes it possible to evaluate the extent of the anomaly's impact by analyzing emotional changes and take appropriate action.

[0082] The notification unit can simultaneously provide detailed information about the abnormality and recommended countermeasures, supporting a rapid response. For example, the notification unit will build a system that, when an abnormality is detected, provides detailed information about the abnormality at the same time as the notification. For example, the cause and extent of the impact of an abnormal temperature rise will be included in the notification. The notification unit will also simultaneously provide recommended countermeasures in the notification, supporting a rapid response. For example, when an abnormal pressure fluctuation is detected, the notification will include pressure adjustment procedures. The notification unit will also develop a system that automatically generates detailed information about the abnormality and recommended countermeasures and includes them in the notification. For example, when an abnormal vibration pattern is detected, the cause of the vibration and countermeasures will be included in the notification. This allows for a rapid response by providing detailed information about the abnormality and countermeasures at the same time.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The sensor unit collects data from various sensors. For example, these include a temperature sensor, a humidity sensor, and a pressure sensor. The sensor unit collects temperature data using the temperature sensor, humidity data using the humidity sensor, and pressure data using the pressure sensor. Step 2: The generation AI analyzes the data collected by the sensor unit and detects abnormalities. The generation AI analyzes the data using statistical analysis, machine learning algorithms, and pattern recognition to detect outliers and abnormal patterns. Step 3: The notification unit notifies the relevant infrastructure organizations of the anomaly detected by the generation AI. The notification unit notifies the electric power company, waterworks bureau, telecommunications company, etc. of the anomaly via email, SMS, or a dedicated application.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

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

[0121] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

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

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

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] 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.

[0144] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a sensor unit that collects data from various sensors; A generation AI that analyzes data collected by the sensor unit and detects abnormalities; A notification unit that notifies related infrastructure organizations of an abnormality detected by the generation AI. A system characterized by:

2. The sensor unit Adding environmental sound sensors and odor sensors to detect signs of abnormalities from multiple angles 2. The system of claim 1.

3. The sensor unit Using drones to focus on areas prone to abnormalities 2. The system of claim 1.

4. The sensor unit Collecting emotional data from people in the facility and using it as a sign of abnormalities 2. The system of claim 1.

5. The sensor unit Build a cooperative system to share data with other infrastructure organizations and monitor each other for abnormalities 2. The system of claim 1.

6. The sensor unit Energy-efficient data collection for sustainable monitoring systems 2. The system of claim 1.

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