System

Abstract

An object of a system according to an embodiment is to automate monitoring of a water level of a river to evacuation guidance and to enable quick and accurate response.SOLUTION: A system according to an embodiment includes a sensor unit, an alarm unit, an analysis unit, a route creation unit, a control unit, a sound broadcasting unit, and an image transfer unit. The sensor unit monitors a water level of a river. The alarm unit issues an alarm based on the data collected by the sensor unit. The analysis unit analyzes the weather map and predicts a damage assumed area. The route creation unit creates a flight route of the drone based on the damage assumed area predicted by the analysis unit. The control unit causes the drone to fly automatically based on the flight route created by the route creation unit. In the voice broadcasting unit, the drone flying by the control unit performs evacuation guidance to residents in the damage assumed area by voice. The image transfer unit transfers an image captured by the drone flying by the control unit to the local government staff.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, monitoring river water levels, predicting potential damage areas, and guiding evacuation routes were often done manually, making it difficult to respond quickly and accurately.

[0005] The system according to the embodiment aims to automate everything from monitoring river water levels to providing evacuation guidance, enabling rapid and accurate responses. [Means for solving the problem]

[0006] The system according to the embodiment comprises a sensor unit, an alarm unit, an analysis unit, a route creation unit, a control unit, an audio broadcasting unit, and an image transfer unit. The sensor unit monitors the water level of rivers. The alarm unit issues an alarm based on data collected by the sensor unit. The analysis unit analyzes weather maps and predicts areas expected to be damaged. The route creation unit creates a flight path for the drone based on the areas expected to be damaged predicted by the analysis unit. The control unit automatically flies the drone based on the flight path created by the route creation unit. The audio broadcasting unit causes the drone flown by the control unit to provide evacuation guidance to residents in areas expected to be damaged via audio. The image transfer unit transfers images taken by the drone flown by the control unit to local government officials. [Effects of the Invention]

[0007] The system according to the embodiment automates everything from monitoring river water levels to guiding people to evacuations, enabling rapid and accurate responses. [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) A system according to an embodiment of the present invention installs sensors in rivers and sends out drones to assess the situation when the water level reaches a dangerous level. This system uses sensors installed in rivers to monitor water levels and issue an alert when the water level reaches a dangerous level. Next, AI predicts potential damage areas based on weather maps and creates a flight path for the drone. The drone flies automatically and provides evacuation guidance to residents in the potential damage area via voice from the air. The drone also transmits images taken from the air to local government officials. This allows the system to respond quickly when the river water level reaches a dangerous level, ensuring the safety of residents. Local government officials can also grasp the local situation in real time and take prompt action.

[0029] The system according to the embodiment includes a sensor unit, an alarm unit, an analysis unit, a route creation unit, a control unit, an audio broadcast unit, and an image transfer unit. The sensor unit monitors the water level of a river. For example, the sensor unit can measure the water level of the river in real time using an ultrasonic sensor or a laser sensor and collect data. The sensor unit can also issue an alarm when a certain water level is reached. The alarm unit issues an alarm based on the data collected by the sensor unit. For example, the alarm unit can issue an audio alert or a text message. The analysis unit analyzes weather maps and predicts areas expected to be affected by damage. For example, the analysis unit can analyze meteorological data and identify areas expected to experience heavy rain or areas expected to experience river flooding. The route creation unit creates a flight path for the drone based on the expected damage area predicted by the analysis unit. For example, the route creation unit can create an optimal flight path using GPS data and topographical data. The control unit automatically flies the drone based on the flight path created by the route creation unit. For example, the control unit can control the drone in real time using an autopilot system. The audio broadcasting unit controls the drone to fly under the control of the control unit and provides evacuation guidance to residents in the predicted damage area via audio. For example, the audio broadcasting unit can broadcast a message using a speaker, such as "This area is dangerous. Please evacuate immediately." The image transmission unit controls the control unit to transmit images taken by the drone to local government officials. For example, the image transmission unit can transmit images taken from the air to local government officials in real time using 4G or 5G mobile communications. This allows the system according to the embodiment to respond quickly when the water level of a river reaches a dangerous level and ensure the safety of residents.

[0030] The sensor unit can monitor the water level of a river using at least one of an ultrasonic sensor and a laser sensor. The ultrasonic sensor is used, for example, to measure the water level of a river with high accuracy. The ultrasonic sensor measures the water level by emitting ultrasonic waves and receiving the reflected waves. The laser sensor measures the water level using, for example, laser light. The laser sensor measures the water level by emitting laser light and receiving the reflected light. This allows the sensor unit to monitor the water level of a river with high accuracy.

[0031] The analysis unit can analyze weather data and predict areas of potential damage. The analysis unit predicts areas of potential damage based on weather data, for example. Weather data includes temperature, precipitation, wind speed, etc. The analysis unit can analyze this data and identify areas where heavy rain is expected or areas where river flooding is expected. This allows the analysis unit to accurately predict areas of potential damage based on weather data.

[0032] The route creation unit can create a flight route for the drone based on the estimated damage area. The route creation unit creates a flight route for the drone based on, for example, the estimated damage area. The estimated damage area is predicted using a flood prediction model, a geographic information system (GIS), or the like. The route creation unit can create an optimal flight route based on this data. This allows the route creation unit to create an optimal flight route based on the estimated damage area.

[0033] The control unit can automatically fly the drone using GPS. The control unit can automatically fly the drone using GPS, for example. GPS provides real-time location information of the drone. The control unit can control the flight path of the drone based on this information. As a result, the control unit can accurately control the automatic flight of the drone by using GPS.

[0034] The voice broadcasting unit can use a speaker to provide evacuation guidance to residents. The voice broadcasting unit can use a speaker, for example, to provide evacuation guidance to residents. The speaker is used to broadcast voice messages. For example, the voice broadcasting unit can broadcast a message such as, "This area is dangerous. Please evacuate immediately." In this way, the voice broadcasting unit can use the speaker to effectively provide evacuation guidance to residents.

[0035] The image transfer unit can transfer images taken from the sky to local government officials using at least one of 4G and 5G mobile communications. The image transfer unit transfers images taken from the sky to local government officials, for example, using 4G or 5G mobile communications. 4G and 5G provide high-speed, stable communications. Based on this, the image transfer unit can transmit images taken from the sky to local government officials in real time. This allows the image transfer unit to transfer images quickly by using 4G or 5G mobile communications.

[0036] The control unit can analyze images taken by the drone and automatically classify the damage situation. The control unit can, for example, analyze images taken by the drone and automatically classify the damage situation. Machine learning algorithms and image recognition technology are used for image analysis. The control unit can automatically classify the damage situation based on this and quickly take countermeasures. As a result, the control unit can quickly take countermeasures by automatically classifying the damage situation.

[0037] The sensor unit can monitor not only the water level but also the flow rate and water quality. For example, the sensor unit can monitor not only the water level but also the flow rate and water quality. Flow rate is measured using a current meter. Water quality is monitored by measuring pH values, turbidity, chemical substance concentrations, etc. The sensor unit can integrate these data and perform a comprehensive risk assessment. This allows the sensor unit to integrate data on water level, flow rate, and water quality and perform a comprehensive risk assessment.

[0038] When the sensor unit detects an abnormality, it can work with other sensors to cross-check the data. When the sensor unit detects an abnormality, it can work with other sensors to cross-check the data. For example, when the sensor unit detects an abnormality, it can work with other sensors to cross-check the data. The cross-check uses the data match rate and an abnormality detection algorithm. Based on this, the sensor unit can confirm the accuracy of the abnormality and reduce false alarms. This allows the sensor unit to confirm the accuracy of the abnormality and reduce false alarms.

[0039] The sensor unit can be added with a function to periodically perform self-diagnosis and detect failures or abnormalities. For example, the sensor unit periodically performs self-diagnosis to detect failures or abnormalities. The self-diagnosis includes the frequency of diagnosis and the items to be diagnosed. Based on this, the sensor unit can detect failures or abnormalities early and notify the user of the need for maintenance. This allows the sensor unit to detect failures or abnormalities early and notify the user of the need for maintenance.

[0040] The sensor unit can monitor the water levels of not only rivers but also dams and lakes. The sensor unit can monitor the water levels of not only rivers but also dams and lakes. The water levels of dams and lakes are measured using a water level sensor. Based on this, the sensor unit can comprehensively monitor the water levels of rivers, dams, and lakes and evaluate the risk of flooding. Based on this, the sensor unit can comprehensively monitor the water levels of rivers, dams, and lakes and evaluate the risk of flooding.

[0041] The sensor unit can perform more accurate water level predictions in cooperation with meteorological data. The sensor unit can perform more accurate water level predictions in cooperation with meteorological data, for example. Meteorological data includes temperature, precipitation, wind speed, etc. The sensor unit can predict the water level based on this data and perform highly accurate predictions. This allows the sensor unit to perform highly accurate water level predictions based on meteorological data.

[0042] The sensor unit collects information from social media and can improve the accuracy of anomaly detection. The sensor unit collects information from social media, for example, and can improve the accuracy of anomaly detection. Social media information is collected using hashtag analysis, filtering of posted content, and the like. The sensor unit can identify the location of an anomaly based on this information and can improve the accuracy of anomaly detection. As a result, the sensor unit can identify the location of an anomaly based on social media information and can improve the accuracy of anomaly detection.

[0043] The alarm unit can use multiple alarm means when it detects an abnormality. For example, when it detects an abnormality, the alarm unit uses multiple alarm means (audio, text, visual). An audio alarm is issued using a speaker. A text message is sent via SMS or email. A visual alarm is visually notified using a display or LED indicator. This allows the alarm unit to notify of an abnormality from multiple angles, enabling a quick response.

[0044] The alarm unit can be added with a function that automatically sends notifications to nearby residents and related parties when it detects an abnormality. For example, the alarm unit can automatically send notifications to nearby residents and related parties when it detects an abnormality. Notifications are sent via text message, email, telephone, etc. Based on this, the alarm unit can quickly notify residents and related parties of abnormalities and ensure the safety of residents and related parties. This allows the alarm unit to quickly notify residents and related parties of abnormalities and ensure the safety of residents and related parties.

[0045] When an abnormality is detected, the alarm unit can improve the accuracy of the alarm by referring to past data. For example, when an abnormality is detected, the alarm unit can improve the accuracy of the alarm by referring to past data. Past data includes past abnormality detection data and alarm history. Based on this, the alarm unit can optimize the frequency and content of alarms and issue highly accurate alarms. This allows the alarm unit to improve the accuracy of alarms based on past data.

[0046] When an abnormality is detected, the warning unit can adjust the range for issuing a warning by taking geographical information into consideration. For example, when an abnormality is detected, the warning unit adjusts the range for issuing a warning by taking geographical information into consideration. Geographical information includes map data and GPS data. Based on this, the warning unit can issue a warning limited to areas where damage is expected. This allows the warning unit to adjust the range of the warning based on geographical information and issue a warning in an appropriate range.

[0047] The alarm unit can issue an alarm via social media when it detects an abnormality. For example, the alarm unit issues an alarm via social media when it detects an abnormality. Social media includes, but is not limited to, Twitter (registered trademark), Facebook (registered trademark), Instagram (registered trademark), etc. Based on this, the alarm unit can quickly notify a wide range of people of the alarm. This allows the alarm unit to quickly notify a wide range of people of the alarm by issuing an alarm via social media.

[0048] The alarm unit can issue an alarm in cooperation with a local government's disaster prevention system when it detects an abnormality. For example, the alarm unit issues an alarm in cooperation with a local government's disaster prevention system when it detects an abnormality. A local government's disaster prevention system includes an evacuation instruction system and a disaster information sharing system. The alarm unit can issue more effective evacuation instructions based on this. In this way, the alarm unit can issue more effective evacuation instructions by coordinating with a local government's disaster prevention system.

[0049] The analysis unit can use not only meteorological data but also topographical data and data on past disasters for its analysis. The analysis unit, for example, uses not only meteorological data but also topographical data and data on past disasters for its analysis. Topographical data includes elevation data and topographical maps. Past disaster data includes past flood data and disaster history. The analysis unit can integrate these data and perform comprehensive risk assessments. This allows the analysis unit to integrate meteorological data, topographical data, and data on past disasters for its analysis.

[0050] When the analysis unit detects an anomaly, it can improve the accuracy of the analysis by cooperating with other AI systems. For example, when the analysis unit detects an anomaly, it can improve the accuracy of the analysis by cooperating with other AI systems. Other AI systems include anomaly detection systems and predictive models. The analysis unit can share data based on this and improve the accuracy of the analysis. This allows the analysis unit to improve the accuracy of the analysis by cooperating with other AI systems.

[0051] The analysis unit can periodically perform self-learning to optimize the analysis algorithm. The analysis unit, for example, periodically performs self-learning to optimize the analysis algorithm. Self-learning includes the type of learning data and the learning frequency. The analysis unit can update the analysis algorithm based on this and improve the accuracy of the analysis. In this way, the analysis unit can optimize the analysis algorithm through self-learning and improve the accuracy of the analysis.

[0052] When an abnormality is detected, the analysis unit can provide an analysis result taking geographical information into consideration. For example, when an abnormality is detected, the analysis unit provides an analysis result taking geographical information into consideration. Geographical information includes map data and GPS data. Based on this, the analysis unit can provide analysis results for areas where damage is expected with priority. This allows the analysis unit to provide analysis results based on geographical information and provide analysis results for areas where damage is expected with priority.

[0053] When an abnormality is detected, the analysis unit can use information from social media for analysis. When an abnormality is detected, the analysis unit, for example, uses information from social media for analysis. Social media information is collected using hashtag analysis, filtering of posted content, etc. The analysis unit can identify the location of the abnormality based on this information and improve the accuracy of the analysis. This allows the analysis unit to identify the location of the abnormality based on social media information and improve the accuracy of the analysis.

[0054] When an abnormality is detected, the analysis unit can provide the analysis results in cooperation with the local government's disaster prevention system. For example, when an abnormality is detected, the analysis unit can provide the analysis results in cooperation with the local government's disaster prevention system. The local government's disaster prevention system includes an evacuation order system and a disaster information sharing system. The analysis unit can issue more effective evacuation orders based on this. In this way, the analysis unit can issue more effective evacuation orders by coordinating with the local government's disaster prevention system.

[0055] The route creation unit can create a route using not only weather data but also terrain data and past flight data. The route creation unit creates a route using, for example, not only weather data but also terrain data and past flight data. The terrain data includes elevation data and topographical maps. The past flight data includes flight logs and flight route history. The route creation unit can integrate these data to create an optimal flight route. This allows the route creation unit to integrate weather data, terrain data, and past flight data to create an optimal flight route.

[0056] When an abnormality is detected, the route creation unit can improve the accuracy of the route in cooperation with other AI systems. For example, when an abnormality is detected, the route creation unit can improve the accuracy of the route in cooperation with other AI systems. Other AI systems include anomaly detection systems and prediction models. Based on this, the route creation unit can share data and improve the accuracy of the route. In this way, the route creation unit can improve the accuracy of the route by cooperating with other AI systems.

[0057] The route creation unit can periodically perform self-learning to optimize the route creation algorithm. The route creation unit, for example, periodically performs self-learning to optimize the route creation algorithm. The self-learning includes the type of learning data and the learning frequency. The route creation unit can update the route creation algorithm based on this and improve the accuracy of the route. In this way, the route creation unit can optimize the route creation algorithm through self-learning and improve the accuracy of the route.

[0058] The route creation unit can create a route taking geographical information into consideration when detecting an abnormality. For example, when detecting an abnormality, the route creation unit creates a route taking geographical information into consideration. Geographical information includes map data and GPS data. Based on this, the route creation unit can provide routes in areas where damage is expected with priority. This allows the route creation unit to adjust the route based on the geographical information and provide routes in areas where damage is expected with priority.

[0059] When an abnormality is detected, the route creation unit can use information from social media to create a route. For example, when an abnormality is detected, the route creation unit uses information from social media to create a route. Social media information is collected using hashtag analysis, filtering of posted content, etc. Based on this, the route creation unit can identify the location of the abnormality and improve the accuracy of route creation. This allows the route creation unit to identify the location of the abnormality based on social media information and improve the accuracy of route creation.

[0060] The route creation unit can create a route in cooperation with the disaster prevention system of the local government when an abnormality is detected. For example, when an abnormality is detected, the route creation unit creates a route in cooperation with the disaster prevention system of the local government. The disaster prevention system of the local government includes an evacuation instruction system and a disaster information sharing system. The route creation unit can issue more effective evacuation instructions based on this. In this way, the route creation unit can issue more effective evacuation instructions by coordinating with the disaster prevention system of the local government.

[0061] When the control unit detects an abnormality, it can optimize the flight path in cooperation with other drones. When the control unit detects an abnormality, it can optimize the flight path in cooperation with other drones. The other drones include communication protocols and cooperation algorithms. The control unit can share data based on this, optimize the flight path, and identify the cause of the abnormality. This allows the control unit to optimize the flight path and identify the cause of the abnormality by cooperating with other drones.

[0062] When an abnormality is detected, the control unit can improve flight accuracy by referring to past flight data. For example, when an abnormality is detected, the control unit can improve flight accuracy by referring to past flight data. Past flight data includes flight logs and flight path history. The control unit can improve the accuracy of the flight path based on this. This allows the control unit to improve the accuracy of the flight path based on the past flight data.

[0063] The control unit can be added with a function to periodically perform self-diagnosis and detect failures or abnormalities. The control unit, for example, periodically performs self-diagnosis to detect failures or abnormalities. The self-diagnosis includes the frequency of diagnosis and the diagnosis items. Based on this, the control unit can detect failures or abnormalities early and notify the user of the need for maintenance. This allows the control unit to detect failures or abnormalities early and notify the user of the need for maintenance.

[0064] When an abnormality is detected, the control unit can adjust the flight route taking into account geographical information. For example, when an abnormality is detected, the control unit adjusts the flight route taking into account geographical information. Geographical information includes map data and GPS data. Based on this, the control unit can provide flight routes in areas where damage is expected with priority. This allows the control unit to adjust the flight route based on geographical information and provide flight routes in areas where damage is expected with priority.

[0065] When an abnormality is detected, the control unit can reflect information from social media in the flight route. For example, when an abnormality is detected, the control unit reflects information from social media in the flight route. Social media information is collected using hashtag analysis, filtering of posted content, etc. Based on this, the control unit can identify the location of the abnormality and improve the accuracy of the flight route. This allows the control unit to identify the location of the abnormality based on social media information and improve the accuracy of the flight route.

[0066] When an abnormality is detected, the control unit can adjust the flight path in cooperation with the disaster prevention system of the local government. For example, when an abnormality is detected, the control unit adjusts the flight path in cooperation with the disaster prevention system of the local government. The disaster prevention system of the local government includes an evacuation instruction system and a disaster information sharing system. The control unit can issue more effective evacuation instructions based on this. In this way, the control unit can issue more effective evacuation instructions by coordinating with the disaster prevention system of the local government.

[0067] The voice broadcasting unit can be added with a function to broadcast a voice message in multiple languages ​​when an abnormality is detected. For example, the voice broadcasting unit broadcasts a voice message in multiple languages ​​when an abnormality is detected. The multiple languages ​​include Japanese, English, Spanish, French, etc. Based on this, the voice broadcasting unit can notify the abnormality from multiple perspectives. This allows the voice broadcasting unit to broadcast a voice message in multiple languages ​​and notify the abnormality from multiple perspectives.

[0068] When an abnormality is detected, the audio broadcasting unit can improve the accuracy of the message by referring to past broadcast data. When an abnormality is detected, for example, the audio broadcasting unit can improve the accuracy of the message by referring to past broadcast data. Past broadcast data includes broadcast logs and broadcast content history. The audio broadcasting unit can optimize the content of the message based on this and provide appropriate evacuation guidance. This allows the audio broadcasting unit to optimize the content of the message based on past broadcast data and provide appropriate evacuation guidance.

[0069] The audio broadcasting unit can be added with a function to periodically perform self-diagnosis and detect failures or abnormalities. The audio broadcasting unit, for example, periodically performs self-diagnosis to detect failures or abnormalities. The self-diagnosis includes the frequency of diagnosis and the items to be diagnosed. Based on this, the audio broadcasting unit can detect failures or abnormalities early and notify the user of the need for maintenance. This allows the audio broadcasting unit to detect failures or abnormalities early and notify the user of the need for maintenance.

[0070] When an abnormality is detected, the audio broadcasting unit can broadcast an audio message taking into consideration geographical information. For example, when an abnormality is detected, the audio broadcasting unit broadcasts an audio message taking into consideration geographical information. Geographical information includes map data and GPS data. Based on this, the audio broadcasting unit can broadcast the audio message limited to areas where damage is expected. In this way, the audio broadcasting unit can adjust the range of the audio message based on the geographical information and broadcast the audio message limited to areas where damage is expected.

[0071] When an abnormality is detected, the voice broadcasting unit can reflect information from social media in the voice message. When an abnormality is detected, for example, the voice broadcasting unit reflects information from social media in the voice message. Social media information is collected using hashtag analysis, filtering of posted content, etc. The voice broadcasting unit can identify the location of the abnormality based on this information and improve the accuracy of the voice message. This allows the voice broadcasting unit to identify the location of the abnormality based on social media information and improve the accuracy of the voice message.

[0072] When an abnormality is detected, the audio broadcasting unit can broadcast an audio message in cooperation with a local government's disaster prevention system. For example, when an abnormality is detected, the audio broadcasting unit broadcasts an audio message in cooperation with a local government's disaster prevention system. The local government's disaster prevention system includes an evacuation instruction system and a disaster information sharing system. The audio broadcasting unit can issue more effective evacuation instructions based on this. In this way, the audio broadcasting unit can issue more effective evacuation instructions by coordinating with the local government's disaster prevention system.

[0073] The image transfer unit can be added with a function of using multiple communication means when an abnormality is detected. For example, the image transfer unit uses multiple communication means (Wi-Fi, satellite communication, etc.) when an abnormality is detected. The multiple communication means include Wi-Fi, satellite communication, 4G, 5G, etc. The image transfer unit can improve the accuracy of image transfer based on this. As a result, the image transfer unit can improve the accuracy of image transfer by using multiple communication means.

[0074] When an abnormality is detected, the image transfer unit can improve the accuracy of transfer by referring to past transfer data. For example, when an abnormality is detected, the image transfer unit can improve the accuracy of transfer by referring to past transfer data. Past transfer data includes a transfer log and a transfer content history. The image transfer unit can improve the accuracy of transfer based on this. This allows the image transfer unit to improve the accuracy of transfer based on the past transfer data.

[0075] The image transfer unit can be added with a function to periodically perform self-diagnosis and detect failures or abnormalities. The image transfer unit, for example, periodically performs self-diagnosis to detect failures or abnormalities. The self-diagnosis includes the frequency of diagnosis and the items to be diagnosed. Based on this, the image transfer unit can detect failures or abnormalities early and notify the user of the need for maintenance. This allows the image transfer unit to detect failures or abnormalities early and notify the user of the need for maintenance.

[0076] The image transfer unit can transfer images taking into consideration geographical information when an abnormality is detected. For example, the image transfer unit transfers images taking into consideration geographical information when an abnormality is detected. Geographical information includes map data and GPS data. Based on this, the image transfer unit can preferentially transfer images of areas where damage is expected. This allows the image transfer unit to adjust the image transfer range based on the geographical information and preferentially transfer images of areas where damage is expected.

[0077] When an abnormality is detected, the image transfer unit can reflect information from social media in the image transfer. For example, when an abnormality is detected, the image transfer unit reflects information from social media in the image transfer. Social media information is collected using hashtag analysis, filtering of posted content, etc. The image transfer unit can identify the location of the abnormality based on this information and improve the accuracy of image transfer. This allows the image transfer unit to identify the location of the abnormality based on social media information and improve the accuracy of image transfer.

[0078] The image transfer unit can transfer images in cooperation with a local government's disaster prevention system when it detects an abnormality. For example, the image transfer unit transfers images in cooperation with a local government's disaster prevention system when it detects an abnormality. The local government's disaster prevention system includes an evacuation instruction system and a disaster information sharing system. The image transfer unit can issue more effective evacuation instructions based on this. In this way, the image transfer unit can issue more effective evacuation instructions by coordinating with the local government's disaster prevention system.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The sensor unit can monitor not only the river's water level, but also its flow rate and water quality. For example, flow rate is measured using a current meter, and water quality is monitored by measuring pH, turbidity, and chemical concentrations. This allows the sensor unit to integrate data on the river's water level, flow rate, and water quality to perform comprehensive risk assessments. Furthermore, when an abnormality is detected, the sensor unit can cross-check the data in conjunction with other sensors. The cross-check uses data consistency and anomaly detection algorithms, which confirms the accuracy of the abnormality and reduces false alarms. Furthermore, the sensor unit periodically performs self-diagnosis to detect malfunctions and abnormalities early and notify the need for maintenance.

[0081] The analysis unit can use not only meteorological data, but also topographical data and data on past disasters for its analysis. For example, topographical data includes elevation data and topographical maps, and past disaster data includes past flood data and disaster history. This allows the analysis unit to integrate meteorological data, topographical data, and past disaster data to perform comprehensive risk assessments. Furthermore, when the analysis unit detects an anomaly, it can collaborate with other AI systems to improve the accuracy of the analysis. Other AI systems include anomaly detection systems and predictive models, which allow data sharing and improved analysis accuracy. Furthermore, the analysis unit can periodically perform self-learning to optimize its analysis algorithm. Self-learning includes the type of learning data and the learning frequency, which allows it to update the analysis algorithm and improve analysis accuracy.

[0082] The route creation unit can create routes using not only weather data but also terrain data and past flight data. For example, terrain data includes elevation data and topographical maps, and past flight data includes flight logs and flight route history. This allows the route creation unit to integrate weather data, terrain data, and past flight data to create an optimal flight route. Furthermore, when an anomaly is detected, the route creation unit can work with other AI systems to improve route accuracy. Other AI systems include anomaly detection systems and predictive models, which can share data and improve route accuracy. Furthermore, the route creation unit can periodically perform self-learning to optimize its route creation algorithm. Self-learning includes the type of learning data and the learning frequency, which allows the route creation algorithm to be updated and the route accuracy to be improved.

[0083] When the control unit detects an abnormality, it can optimize the flight path in cooperation with other drones. For example, the other drones may include communication protocols and coordination algorithms, which allow them to share data, optimize the flight path, and identify the cause of the abnormality. The control unit can also improve flight accuracy by referencing past flight data. Past flight data includes flight logs and flight path history, which can improve flight path accuracy. Furthermore, the control unit can periodically perform self-diagnosis to detect malfunctions or abnormalities early and notify the user of the need for maintenance. The self-diagnosis includes diagnostic frequency and diagnostic items, which allows for early detection of malfunctions or abnormalities and notify the user of the need for maintenance.

[0084] The voice broadcasting unit can be equipped with a function that broadcasts voice messages in multiple languages ​​when an abnormality is detected. For example, the multiple languages ​​can include Japanese, English, Spanish, and French, allowing for multifaceted notification of abnormalities. The voice broadcasting unit can also improve the accuracy of messages by referencing past broadcast data. Past broadcast data includes broadcast logs and broadcast content history, which can be used to optimize the content of messages and provide appropriate evacuation guidance. Furthermore, the voice broadcasting unit can periodically perform self-diagnosis to detect malfunctions or abnormalities early and notify the need for maintenance. The self-diagnosis includes diagnostic frequency and diagnostic items, allowing for early detection of malfunctions or abnormalities and notification of the need for maintenance.

[0085] The processing flow of the first embodiment will be briefly explained below.

[0086] Step 1: The sensor unit monitors the water level of the river. For example, the sensor unit can measure the water level of the river in real time using an ultrasonic sensor or a laser sensor and collect data. The sensor unit can also issue an alarm when a certain water level is reached. Step 2: The alarm unit issues an alarm based on the data collected by the sensor unit. For example, the alarm unit can issue a voice alert or a text message. Step 3: The analysis unit analyzes weather maps and predicts areas where damage is likely to occur. For example, the analysis unit can analyze meteorological data to identify areas where heavy rain is expected or areas where river flooding is expected. Step 4: The route creation unit creates a flight path for the drone based on the estimated damage area predicted by the analysis unit. For example, the route creation unit can create an optimal flight path using GPS data and topographical data. Step 5: The control unit automatically flies the drone based on the flight path created by the path creation unit. For example, the control unit can control the drone in real time using an autopilot system. Step 6: The voice broadcasting unit, controlled by the control unit, allows the drone to fly and provide evacuation guidance to residents in the affected area via voice. For example, the voice broadcasting unit can use a speaker to broadcast a message such as, "This area is dangerous. Please evacuate immediately." Step 7: The image transmission unit transmits the images taken by the drone, which is flown by the control unit, to local government officials. For example, the image transmission unit can use 4G or 5G mobile communications to transmit images taken from the sky to local government officials in real time.

[0087] (Example 2) A system according to an embodiment of the present invention installs sensors in rivers and sends out drones to assess the situation when the water level reaches a dangerous level. This system uses sensors installed in rivers to monitor water levels and issue an alert when the water level reaches a dangerous level. Next, AI predicts potential damage areas based on weather maps and creates a flight path for the drone. The drone flies automatically and provides evacuation guidance to residents in the potential damage area via voice from the air. The drone also transmits images taken from the air to local government officials. This allows the system to respond quickly when the river water level reaches a dangerous level, ensuring the safety of residents. Local government officials can also grasp the local situation in real time and take prompt action.

[0088] The system according to the embodiment includes a sensor unit, an alarm unit, an analysis unit, a route creation unit, a control unit, an audio broadcast unit, and an image transfer unit. The sensor unit monitors the water level of a river. For example, the sensor unit can measure the water level of the river in real time using an ultrasonic sensor or a laser sensor and collect data. The sensor unit can also issue an alarm when a certain water level is reached. The alarm unit issues an alarm based on the data collected by the sensor unit. For example, the alarm unit can issue an audio alert or a text message. The analysis unit analyzes weather maps and predicts areas expected to be affected by damage. For example, the analysis unit can analyze meteorological data and identify areas expected to experience heavy rain or areas expected to experience river flooding. The route creation unit creates a flight path for the drone based on the expected damage area predicted by the analysis unit. For example, the route creation unit can create an optimal flight path using GPS data and topographical data. The control unit automatically flies the drone based on the flight path created by the route creation unit. For example, the control unit can control the drone in real time using an autopilot system. The audio broadcasting unit controls the drone to fly under the control of the control unit and provides evacuation guidance to residents in the predicted damage area via audio. For example, the audio broadcasting unit can broadcast a message using a speaker, such as "This area is dangerous. Please evacuate immediately." The image transmission unit controls the control unit to transmit images taken by the drone to local government officials. For example, the image transmission unit can transmit images taken from the air to local government officials in real time using 4G or 5G mobile communications. This allows the system according to the embodiment to respond quickly when the water level of a river reaches a dangerous level and ensure the safety of residents.

[0089] The sensor unit can monitor the water level of a river using at least one of an ultrasonic sensor and a laser sensor. The ultrasonic sensor is used, for example, to measure the water level of a river with high accuracy. The ultrasonic sensor measures the water level by emitting ultrasonic waves and receiving the reflected waves. The laser sensor measures the water level using, for example, laser light. The laser sensor measures the water level by emitting laser light and receiving the reflected light. This allows the sensor unit to monitor the water level of a river with high accuracy.

[0090] The analysis unit can analyze weather data and predict areas of potential damage. The analysis unit predicts areas of potential damage based on weather data, for example. Weather data includes temperature, precipitation, wind speed, etc. The analysis unit can analyze this data and identify areas where heavy rain is expected or areas where river flooding is expected. This allows the analysis unit to accurately predict areas of potential damage based on weather data.

[0091] The route creation unit can create a flight route for the drone based on the estimated damage area. The route creation unit creates a flight route for the drone based on, for example, the estimated damage area. The estimated damage area is predicted using a flood prediction model, a geographic information system (GIS), or the like. The route creation unit can create an optimal flight route based on this data. This allows the route creation unit to create an optimal flight route based on the estimated damage area.

[0092] The control unit can automatically fly the drone using GPS. The control unit can automatically fly the drone using GPS, for example. GPS provides real-time location information of the drone. The control unit can control the flight path of the drone based on this information. As a result, the control unit can accurately control the automatic flight of the drone by using GPS.

[0093] The voice broadcasting unit can use a speaker to provide evacuation guidance to residents. The voice broadcasting unit can use a speaker, for example, to provide evacuation guidance to residents. The speaker is used to broadcast voice messages. For example, the voice broadcasting unit can broadcast a message such as, "This area is dangerous. Please evacuate immediately." In this way, the voice broadcasting unit can use the speaker to effectively provide evacuation guidance to residents.

[0094] The image transfer unit can transfer images taken from the sky to local government officials using at least one of 4G and 5G mobile communications. The image transfer unit transfers images taken from the sky to local government officials, for example, using 4G or 5G mobile communications. 4G and 5G provide high-speed, stable communications. Based on this, the image transfer unit can transmit images taken from the sky to local government officials in real time. This allows the image transfer unit to transfer images quickly by using 4G or 5G mobile communications.

[0095] The control unit can analyze images taken by the drone and automatically classify the damage situation. The control unit can, for example, analyze images taken by the drone and automatically classify the damage situation. Machine learning algorithms and image recognition technology are used for image analysis. The control unit can automatically classify the damage situation based on this and quickly take countermeasures. As a result, the control unit can quickly take countermeasures by automatically classifying the damage situation.

[0096] The sensor unit can estimate the user's emotion and adjust the sensitivity of the sensor based on the estimated user's emotion. The sensor unit, for example, estimates the user's emotion and adjusts the sensitivity of the sensor based on the estimated user's emotion. The user's emotion is estimated using facial expression recognition, voice analysis, or the like. The sensor unit can adjust the sensitivity of the sensor based on this, reduce false alarms, and issue an alarm at an appropriate time. As a result, the sensor unit can reduce false alarms and issue an alarm at an appropriate time by adjusting the sensitivity of the sensor according to the user's emotion.

[0097] The sensor unit can monitor not only the water level but also the flow rate and water quality. For example, the sensor unit can monitor not only the water level but also the flow rate and water quality. Flow rate is measured using a current meter. Water quality is monitored by measuring pH values, turbidity, chemical substance concentrations, etc. The sensor unit can integrate these data and perform a comprehensive risk assessment. This allows the sensor unit to integrate data on water level, flow rate, and water quality and perform a comprehensive risk assessment.

[0098] When the sensor unit detects an abnormality, it can work with other sensors to cross-check the data. When the sensor unit detects an abnormality, it can work with other sensors to cross-check the data. For example, when the sensor unit detects an abnormality, it can work with other sensors to cross-check the data. The cross-check uses the data match rate and an abnormality detection algorithm. Based on this, the sensor unit can confirm the accuracy of the abnormality and reduce false alarms. This allows the sensor unit to confirm the accuracy of the abnormality and reduce false alarms.

[0099] The sensor unit can be added with a function to periodically perform self-diagnosis and detect failures or abnormalities. For example, the sensor unit periodically performs self-diagnosis to detect failures or abnormalities. The self-diagnosis includes the frequency of diagnosis and the items to be diagnosed. Based on this, the sensor unit can detect failures or abnormalities early and notify the user of the need for maintenance. This allows the sensor unit to detect failures or abnormalities early and notify the user of the need for maintenance.

[0100] The sensor unit can estimate the user's emotion and optimize the installation location of the sensor based on the estimated user's emotion. The sensor unit, for example, estimates the user's emotion and optimizes the installation location of the sensor based on the estimated user's emotion. The user's emotion is estimated using facial expression recognition, voice analysis, or the like. The sensor unit can optimize the installation location of the sensor based on this and provide a sense of security. In this way, the sensor unit can optimize the installation location of the sensor according to the user's emotion and provide a sense of security.

[0101] The sensor unit can monitor the water levels of not only rivers but also dams and lakes. The sensor unit can monitor the water levels of not only rivers but also dams and lakes. The water levels of dams and lakes are measured using a water level sensor. Based on this, the sensor unit can comprehensively monitor the water levels of rivers, dams, and lakes and evaluate the risk of flooding. Based on this, the sensor unit can comprehensively monitor the water levels of rivers, dams, and lakes and evaluate the risk of flooding.

[0102] The sensor unit can perform more accurate water level predictions in cooperation with meteorological data. The sensor unit can perform more accurate water level predictions in cooperation with meteorological data, for example. Meteorological data includes temperature, precipitation, wind speed, etc. The sensor unit can predict the water level based on this data and perform highly accurate predictions. This allows the sensor unit to perform highly accurate water level predictions based on meteorological data.

[0103] The sensor unit collects information from social media and can improve the accuracy of anomaly detection. The sensor unit collects information from social media, for example, and can improve the accuracy of anomaly detection. Social media information is collected using hashtag analysis, filtering of posted content, and the like. The sensor unit can identify the location of an anomaly based on this information and can improve the accuracy of anomaly detection. As a result, the sensor unit can identify the location of an anomaly based on social media information and can improve the accuracy of anomaly detection.

[0104] The alarm unit can estimate the user's emotion and adjust the volume and content of the alarm based on the estimated user's emotion. The alarm unit, for example, estimates the user's emotion and adjusts the volume and content of the alarm based on the estimated user's emotion. The user's emotion is estimated using facial expression recognition, voice analysis, or the like. The alarm unit can adjust the volume and content of the alarm based on this and issue an appropriate alarm. This allows the alarm unit to adjust the volume and content of the alarm according to the user's emotion and issue an appropriate alarm.

[0105] The alarm unit can use multiple alarm means when it detects an abnormality. For example, when it detects an abnormality, the alarm unit uses multiple alarm means (audio, text, visual). An audio alarm is issued using a speaker. A text message is sent via SMS or email. A visual alarm is visually notified using a display or LED indicator. This allows the alarm unit to notify of an abnormality from multiple angles, enabling a quick response.

[0106] The alarm unit can be added with a function that automatically sends notifications to nearby residents and related parties when it detects an abnormality. For example, the alarm unit can automatically send notifications to nearby residents and related parties when it detects an abnormality. Notifications are sent via text message, email, telephone, etc. Based on this, the alarm unit can quickly notify residents and related parties of abnormalities and ensure the safety of residents and related parties. This allows the alarm unit to quickly notify residents and related parties of abnormalities and ensure the safety of residents and related parties.

[0107] When an abnormality is detected, the alarm unit can improve the accuracy of the alarm by referring to past data. For example, when an abnormality is detected, the alarm unit can improve the accuracy of the alarm by referring to past data. Past data includes past abnormality detection data and alarm history. Based on this, the alarm unit can optimize the frequency and content of alarms and issue highly accurate alarms. This allows the alarm unit to improve the accuracy of alarms based on past data.

[0108] The warning unit can estimate the user's emotion and determine the priority of warnings based on the estimated user's emotion. The warning unit, for example, estimates the user's emotion and determines the priority of warnings based on the estimated user's emotion. The user's emotion is estimated using facial expression recognition, voice analysis, or the like. The warning unit can determine the priority of warnings based on this and give priority to important warnings. This allows the warning unit to determine the priority of warnings according to the user's emotion and give priority to important warnings.

[0109] When an abnormality is detected, the warning unit can adjust the range for issuing a warning by taking geographical information into consideration. For example, when an abnormality is detected, the warning unit adjusts the range for issuing a warning by taking geographical information into consideration. Geographical information includes map data and GPS data. Based on this, the warning unit can issue a warning limited to areas where damage is expected. This allows the warning unit to adjust the range of the warning based on geographical information and issue a warning in an appropriate range.

[0110] The alarm unit can issue an alarm via social media when it detects an abnormality. For example, the alarm unit issues an alarm via social media when it detects an abnormality. Social media includes, but is not limited to, Twitter, Facebook, Instagram, etc. Based on this, the alarm unit can quickly notify a wide range of people of the alarm. This allows the alarm unit to quickly notify a wide range of people of the alarm by issuing an alarm via social media.

[0111] The alarm unit can issue an alarm in cooperation with a local government's disaster prevention system when it detects an abnormality. For example, the alarm unit issues an alarm in cooperation with a local government's disaster prevention system when it detects an abnormality. A local government's disaster prevention system includes an evacuation instruction system and a disaster information sharing system. The alarm unit can issue more effective evacuation instructions based on this. In this way, the alarm unit can issue more effective evacuation instructions by coordinating with a local government's disaster prevention system.

[0112] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis results based on the estimated user's emotion. The user's emotion is estimated using facial expression recognition, voice analysis, or the like. The analysis unit can adjust the display method of the analysis results based on this and improve visibility. In this way, the analysis unit can adjust the display method of the analysis results according to the user's emotion and improve visibility.

[0113] The analysis unit can use not only meteorological data but also topographical data and data on past disasters for its analysis. The analysis unit, for example, uses not only meteorological data but also topographical data and data on past disasters for its analysis. Topographical data includes elevation data and topographical maps. Past disaster data includes past flood data and disaster history. The analysis unit can integrate these data and perform comprehensive risk assessments. This allows the analysis unit to integrate meteorological data, topographical data, and data on past disasters for its analysis.

[0114] When the analysis unit detects an anomaly, it can improve the accuracy of the analysis by cooperating with other AI systems. For example, when the analysis unit detects an anomaly, it can improve the accuracy of the analysis by cooperating with other AI systems. Other AI systems include anomaly detection systems and predictive models. The analysis unit can share data based on this and improve the accuracy of the analysis. This allows the analysis unit to improve the accuracy of the analysis by cooperating with other AI systems.

[0115] The analysis unit can periodically perform self-learning to optimize the analysis algorithm. The analysis unit, for example, periodically performs self-learning to optimize the analysis algorithm. Self-learning includes the type of learning data and the learning frequency. The analysis unit can update the analysis algorithm based on this and improve the accuracy of the analysis. In this way, the analysis unit can optimize the analysis algorithm through self-learning and improve the accuracy of the analysis.

[0116] The analysis unit can estimate the user's emotion and determine the priority of the analysis results based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and determines the priority of the analysis results based on the estimated user's emotion. The user's emotion is estimated using facial expression recognition, voice analysis, or the like. The analysis unit can determine the priority of the analysis results based on this and preferentially display important analysis results. This allows the analysis unit to determine the priority of the analysis results according to the user's emotion and preferentially display important analysis results.

[0117] When an abnormality is detected, the analysis unit can provide an analysis result taking geographical information into consideration. For example, when an abnormality is detected, the analysis unit provides an analysis result taking geographical information into consideration. Geographical information includes map data and GPS data. Based on this, the analysis unit can provide analysis results for areas where damage is expected with priority. This allows the analysis unit to provide analysis results based on geographical information and provide analysis results for areas where damage is expected with priority.

[0118] When an abnormality is detected, the analysis unit can use information from social media for analysis. When an abnormality is detected, the analysis unit, for example, uses information from social media for analysis. Social media information is collected using hashtag analysis, filtering of posted content, etc. The analysis unit can identify the location of the abnormality based on this information and improve the accuracy of the analysis. This allows the analysis unit to identify the location of the abnormality based on social media information and improve the accuracy of the analysis.

[0119] When an abnormality is detected, the analysis unit can provide the analysis results in cooperation with the local government's disaster prevention system. For example, when an abnormality is detected, the analysis unit can provide the analysis results in cooperation with the local government's disaster prevention system. The local government's disaster prevention system includes an evacuation order system and a disaster information sharing system. The analysis unit can issue more effective evacuation orders based on this. In this way, the analysis unit can issue more effective evacuation orders by coordinating with the local government's disaster prevention system.

[0120] The route creation unit can estimate the user's emotions and adjust the flight route creation method based on the estimated user's emotions. The route creation unit, for example, estimates the user's emotions and adjusts the flight route creation method based on the estimated user's emotions. The user's emotions are estimated using facial expression recognition, voice analysis, etc. The route creation unit can adjust the flight route creation method based on this and provide an optimal route. This allows the route creation unit to adjust the flight route creation method according to the user's emotions and provide an optimal route.

[0121] The route creation unit can create a route using not only weather data but also terrain data and past flight data. The route creation unit creates a route using, for example, not only weather data but also terrain data and past flight data. The terrain data includes elevation data and topographical maps. The past flight data includes flight logs and flight route history. The route creation unit can integrate these data to create an optimal flight route. This allows the route creation unit to integrate weather data, terrain data, and past flight data to create an optimal flight route.

[0122] When an abnormality is detected, the route creation unit can improve the accuracy of the route in cooperation with other AI systems. For example, when an abnormality is detected, the route creation unit can improve the accuracy of the route in cooperation with other AI systems. Other AI systems include anomaly detection systems and prediction models. Based on this, the route creation unit can share data and improve the accuracy of the route. In this way, the route creation unit can improve the accuracy of the route by cooperating with other AI systems.

[0123] The route creation unit can periodically perform self-learning to optimize the route creation algorithm. The route creation unit, for example, periodically performs self-learning to optimize the route creation algorithm. The self-learning includes the type of learning data and the learning frequency. The route creation unit can update the route creation algorithm based on this and improve the accuracy of the route. In this way, the route creation unit can optimize the route creation algorithm through self-learning and improve the accuracy of the route.

[0124] The route creation unit can estimate the user's emotions and determine the priority of flight routes based on the estimated user's emotions. The route creation unit can, for example, estimate the user's emotions and determine the priority of flight routes based on the estimated user's emotions. The user's emotions are estimated using facial expression recognition, voice analysis, or the like. The route creation unit can determine the priority of flight routes based on this and provide important routes with priority. This allows the route creation unit to determine the priority of flight routes according to the user's emotions and provide important routes with priority.

[0125] The route creation unit can create a route taking geographical information into consideration when detecting an abnormality. For example, when detecting an abnormality, the route creation unit creates a route taking geographical information into consideration. Geographical information includes map data and GPS data. Based on this, the route creation unit can provide routes in areas where damage is expected with priority. This allows the route creation unit to adjust the route based on the geographical information and provide routes in areas where damage is expected with priority.

[0126] When an abnormality is detected, the route creation unit can use information from social media to create a route. For example, when an abnormality is detected, the route creation unit uses information from social media to create a route. Social media information is collected using hashtag analysis, filtering of posted content, etc. Based on this, the route creation unit can identify the location of the abnormality and improve the accuracy of route creation. This allows the route creation unit to identify the location of the abnormality based on social media information and improve the accuracy of route creation.

[0127] The route creation unit can create a route in cooperation with the disaster prevention system of the local government when an abnormality is detected. For example, when an abnormality is detected, the route creation unit creates a route in cooperation with the disaster prevention system of the local government. The disaster prevention system of the local government includes an evacuation instruction system and a disaster information sharing system. The route creation unit can issue more effective evacuation instructions based on this. In this way, the route creation unit can issue more effective evacuation instructions by coordinating with the disaster prevention system of the local government.

[0128] The control unit can estimate the user's emotions and adjust the drone's flight speed and altitude based on the estimated user's emotions. The control unit, for example, estimates the user's emotions and adjusts the drone's flight speed and altitude based on the estimated user's emotions. The user's emotions are estimated using facial expression recognition, voice analysis, etc. The control unit can adjust the drone's flight speed and altitude based on this and provide rapid and detailed information. This allows the control unit to adjust the drone's flight speed and altitude in accordance with the user's emotions and provide rapid and detailed information.

[0129] When the control unit detects an abnormality, it can optimize the flight path in cooperation with other drones. When the control unit detects an abnormality, it can optimize the flight path in cooperation with other drones. The other drones include communication protocols and cooperation algorithms. The control unit can share data based on this, optimize the flight path, and identify the cause of the abnormality. This allows the control unit to optimize the flight path and identify the cause of the abnormality by cooperating with other drones.

[0130] When an abnormality is detected, the control unit can improve flight accuracy by referring to past flight data. For example, when an abnormality is detected, the control unit can improve flight accuracy by referring to past flight data. Past flight data includes flight logs and flight path history. The control unit can improve the accuracy of the flight path based on this. This allows the control unit to improve the accuracy of the flight path based on the past flight data.

[0131] The control unit can be added with a function to periodically perform self-diagnosis and detect failures or abnormalities. The control unit, for example, periodically performs self-diagnosis to detect failures or abnormalities. The self-diagnosis includes the frequency of diagnosis and the diagnosis items. Based on this, the control unit can detect failures or abnormalities early and notify the user of the need for maintenance. This allows the control unit to detect failures or abnormalities early and notify the user of the need for maintenance.

[0132] The control unit can estimate the user's emotions and optimize the drone's flight path based on the estimated user's emotions. The control unit, for example, estimates the user's emotions and optimizes the drone's flight path based on the estimated user's emotions. The user's emotions are estimated using facial expression recognition, voice analysis, or the like. The control unit can optimize the flight path based on this and provide important routes with priority. This allows the control unit to optimize the flight path according to the user's emotions and provide important routes with priority.

[0133] When an abnormality is detected, the control unit can adjust the flight route taking into account geographical information. For example, when an abnormality is detected, the control unit adjusts the flight route taking into account geographical information. Geographical information includes map data and GPS data. Based on this, the control unit can provide flight routes in areas where damage is expected with priority. This allows the control unit to adjust the flight route based on geographical information and provide flight routes in areas where damage is expected with priority.

[0134] When an abnormality is detected, the control unit can reflect information from social media in the flight route. For example, when an abnormality is detected, the control unit reflects information from social media in the flight route. Social media information is collected using hashtag analysis, filtering of posted content, etc. Based on this, the control unit can identify the location of the abnormality and improve the accuracy of the flight route. This allows the control unit to identify the location of the abnormality based on social media information and improve the accuracy of the flight route.

[0135] When an abnormality is detected, the control unit can adjust the flight path in cooperation with the disaster prevention system of the local government. For example, when an abnormality is detected, the control unit adjusts the flight path in cooperation with the disaster prevention system of the local government. The disaster prevention system of the local government includes an evacuation instruction system and a disaster information sharing system. The control unit can issue more effective evacuation instructions based on this. In this way, the control unit can issue more effective evacuation instructions by coordinating with the disaster prevention system of the local government.

[0136] The audio broadcasting unit can estimate the user's emotion and adjust the content and volume of the audio message based on the estimated user's emotion. The audio broadcasting unit, for example, estimates the user's emotion and adjusts the content and volume of the audio message based on the estimated user's emotion. The user's emotion is estimated using facial expression recognition, audio analysis, or the like. The audio broadcasting unit can adjust the content and volume of the audio message based on this and provide appropriate evacuation guidance. This allows the audio broadcasting unit to adjust the content and volume of the audio message according to the user's emotion and provide appropriate evacuation guidance.

[0137] The voice broadcasting unit can be added with a function to broadcast a voice message in multiple languages ​​when an abnormality is detected. For example, the voice broadcasting unit broadcasts a voice message in multiple languages ​​when an abnormality is detected. The multiple languages ​​include Japanese, English, Spanish, French, etc. Based on this, the voice broadcasting unit can notify the abnormality from multiple perspectives. This allows the voice broadcasting unit to broadcast a voice message in multiple languages ​​and notify the abnormality from multiple perspectives.

[0138] When an abnormality is detected, the audio broadcasting unit can improve the accuracy of the message by referring to past broadcast data. When an abnormality is detected, for example, the audio broadcasting unit can improve the accuracy of the message by referring to past broadcast data. Past broadcast data includes broadcast logs and broadcast content history. The audio broadcasting unit can optimize the content of the message based on this and provide appropriate evacuation guidance. This allows the audio broadcasting unit to optimize the content of the message based on past broadcast data and provide appropriate evacuation guidance.

[0139] The audio broadcasting unit can be added with a function to periodically perform self-diagnosis and detect failures or abnormalities. The audio broadcasting unit, for example, periodically performs self-diagnosis to detect failures or abnormalities. The self-diagnosis includes the frequency of diagnosis and the items to be diagnosed. Based on this, the audio broadcasting unit can detect failures or abnormalities early and notify the user of the need for maintenance. This allows the audio broadcasting unit to detect failures or abnormalities early and notify the user of the need for maintenance.

[0140] The audio broadcasting unit can estimate the user's emotion and determine the priority of audio messages based on the estimated user's emotion. For example, the audio broadcasting unit can estimate the user's emotion and determine the priority of audio messages based on the estimated user's emotion. The user's emotion is estimated using facial expression recognition, audio analysis, or the like. The audio broadcasting unit can determine the priority of audio messages based on this and broadcast important messages with priority. This allows the audio broadcasting unit to determine the priority of audio messages according to the user's emotion and broadcast important messages with priority.

[0141] When an abnormality is detected, the audio broadcasting unit can broadcast an audio message taking into consideration geographical information. For example, when an abnormality is detected, the audio broadcasting unit broadcasts an audio message taking into consideration geographical information. Geographical information includes map data and GPS data. Based on this, the audio broadcasting unit can broadcast the audio message limited to areas where damage is expected. In this way, the audio broadcasting unit can adjust the range of the audio message based on the geographical information and broadcast the audio message limited to areas where damage is expected.

[0142] When an abnormality is detected, the voice broadcasting unit can reflect information from social media in the voice message. When an abnormality is detected, for example, the voice broadcasting unit reflects information from social media in the voice message. Social media information is collected using hashtag analysis, filtering of posted content, etc. The voice broadcasting unit can identify the location of the abnormality based on this information and improve the accuracy of the voice message. This allows the voice broadcasting unit to identify the location of the abnormality based on social media information and improve the accuracy of the voice message.

[0143] When an abnormality is detected, the audio broadcasting unit can broadcast an audio message in cooperation with a local government's disaster prevention system. For example, when an abnormality is detected, the audio broadcasting unit broadcasts an audio message in cooperation with a local government's disaster prevention system. The local government's disaster prevention system includes an evacuation instruction system and a disaster information sharing system. The audio broadcasting unit can issue more effective evacuation instructions based on this. In this way, the audio broadcasting unit can issue more effective evacuation instructions by coordinating with the local government's disaster prevention system.

[0144] The image transfer unit can estimate the user's emotion and adjust the image transfer method based on the estimated user's emotion. The image transfer unit, for example, estimates the user's emotion and adjusts the image transfer method based on the estimated user's emotion. The user's emotion is estimated using facial expression recognition, voice analysis, or the like. The image transfer unit can adjust the image transfer speed and resolution based on this, and provide rapid and detailed information. This allows the image transfer unit to adjust the image transfer method according to the user's emotion and provide rapid and detailed information.

[0145] The image transfer unit can be added with a function of using multiple communication means when an abnormality is detected. For example, the image transfer unit uses multiple communication means (Wi-Fi, satellite communication, etc.) when an abnormality is detected. The multiple communication means include Wi-Fi, satellite communication, 4G, 5G, etc. The image transfer unit can improve the accuracy of image transfer based on this. As a result, the image transfer unit can improve the accuracy of image transfer by using multiple communication means.

[0146] When an abnormality is detected, the image transfer unit can improve the accuracy of transfer by referring to past transfer data. For example, when an abnormality is detected, the image transfer unit can improve the accuracy of transfer by referring to past transfer data. Past transfer data includes a transfer log and a transfer content history. The image transfer unit can improve the accuracy of transfer based on this. This allows the image transfer unit to improve the accuracy of transfer based on the past transfer data.

[0147] The image transfer unit can be added with a function to periodically perform self-diagnosis and detect failures or abnormalities. The image transfer unit, for example, periodically performs self-diagnosis to detect failures or abnormalities. The self-diagnosis includes the frequency of diagnosis and the items to be diagnosed. Based on this, the image transfer unit can detect failures or abnormalities early and notify the user of the need for maintenance. This allows the image transfer unit to detect failures or abnormalities early and notify the user of the need for maintenance.

[0148] The image transfer unit can estimate the user's emotion and determine the image transfer priority based on the estimated user's emotion. The image transfer unit, for example, estimates the user's emotion and determines the image transfer priority based on the estimated user's emotion. The user's emotion is estimated using facial expression recognition, voice analysis, or the like. The image transfer unit can determine the image transfer priority based on this and transfer important images preferentially. This allows the image transfer unit to determine the image transfer priority according to the user's emotion and transfer important images preferentially.

[0149] The image transfer unit can transfer images taking into consideration geographical information when an abnormality is detected. For example, the image transfer unit transfers images taking into consideration geographical information when an abnormality is detected. Geographical information includes map data and GPS data. Based on this, the image transfer unit can preferentially transfer images of areas where damage is expected. This allows the image transfer unit to adjust the image transfer range based on the geographical information and preferentially transfer images of areas where damage is expected.

[0150] When an abnormality is detected, the image transfer unit can reflect information from social media in the image transfer. For example, when an abnormality is detected, the image transfer unit reflects information from social media in the image transfer. Social media information is collected using hashtag analysis, filtering of posted content, etc. The image transfer unit can identify the location of the abnormality based on this information and improve the accuracy of image transfer. This allows the image transfer unit to identify the location of the abnormality based on social media information and improve the accuracy of image transfer.

[0151] The image transfer unit can transfer images in cooperation with a local government's disaster prevention system when it detects an abnormality. For example, the image transfer unit transfers images in cooperation with a local government's disaster prevention system when it detects an abnormality. The local government's disaster prevention system includes an evacuation instruction system and a disaster information sharing system. The image transfer unit can issue more effective evacuation instructions based on this. In this way, the image transfer unit can issue more effective evacuation instructions by coordinating with the local government's disaster prevention system. === Hard Collateral 1-1 === Each of the above-described elements, including the sensor unit, alarm unit, analysis unit, route creation unit, control unit, audio broadcast unit, and image transfer unit, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the sensor unit can monitor river water levels using the camera 42 and microphone 38B of the smart device 14 and collect measurement data in real time using the specific processing unit 290 of the data processing device 12. For example, the alarm unit can issue audio alerts using the output device 40 of the smart device 14 and send text messages using the specific processing unit 290 of the data processing device 12. For example, the analysis unit can analyze weather maps using the specific processing unit 290 of the data processing device 12 and predict potential damage areas. For example, the route creation unit can create a drone flight path using GPS data and topographical data using the specific processing unit 290 of the data processing device 12. For example, the control unit can automatically fly the drone using the control unit 46A of the smart device 14. For example, the audio broadcast unit can broadcast evacuation guidance messages using the speaker 40B of the smart device 14. For example, the image transfer unit can use the communication I / F 44 of the smart device 14 to transmit images taken from the sky to local government officials in real time. === Hard Collateral 1-2 === Each of the multiple elements, including the sensor unit, alarm unit, analysis unit, route creation unit, control unit, audio broadcast unit, and image transfer unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the sensor unit can monitor river water levels using the camera 42 and microphone 238 of the smart glasses 214 and collect measurement data in real time using the specific processing unit 290 of the data processing device 12. For example, the alarm unit can issue audio alerts using the speaker 240 of the smart glasses 214 and send text messages using the specific processing unit 290 of the data processing device 12. For example, the analysis unit can analyze weather maps using the specific processing unit 290 of the data processing device 12 and predict potential damage areas. For example, the route creation unit can create a drone flight path using GPS data and topographical data using the specific processing unit 290 of the data processing device 12. For example, the control unit can automatically fly the drone using the control unit 46A of the smart glasses 214. For example, the audio broadcast unit can broadcast evacuation guidance messages using the speaker 240 of the smart glasses 214. For example, the image transfer unit can use the communication I / F 44 of the smart glasses 214 to transmit images taken from the sky to local government officials in real time. === Hard Collateral 1-3 === Each of the multiple elements, including the sensor unit, alarm unit, analysis unit, route creation unit, control unit, audio broadcast unit, and image transfer unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the sensor unit can monitor the water level of a river using the camera 42 and microphone 238 of the headset terminal 314 and collect measurement data in real time using the specific processing unit 290 of the data processing device 12. For example, the alarm unit can issue an audio alert using the speaker 240 of the headset terminal 314 and send a text message using the specific processing unit 290 of the data processing device 12. For example, the analysis unit can analyze a weather map using the specific processing unit 290 of the data processing device 12 and predict potential damage areas. For example, the route creation unit can create a drone flight path using GPS data and topographical data using the specific processing unit 290 of the data processing device 12. For example, the control unit can automatically fly the drone using the control unit 46A of the headset terminal 314. For example, the audio broadcasting unit can broadcast an evacuation guidance message using the speaker 240 of the headset terminal 314. For example, the image transfer unit can transmit images taken from the sky to local government officials in real time using the communication I / F 44 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the sensor unit, alarm unit, analysis unit, route creation unit, control unit, audio broadcast unit, and image transfer unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the sensor unit can monitor river water levels using the camera 42 and microphone 238 of the robot 414 and collect measurement data in real time using the specific processing unit 290 of the data processing device 12. For example, the alarm unit can issue audio alerts using the speaker 240 of the robot 414 and send text messages using the specific processing unit 290 of the data processing device 12. For example, the analysis unit can analyze weather maps using the specific processing unit 290 of the data processing device 12 and predict potential damage areas. For example, the route creation unit can create a drone flight path using GPS data and topographical data using the specific processing unit 290 of the data processing device 12. For example, the control unit can automatically fly the drone using the control unit 46A of the robot 414. For example, the audio broadcast unit can broadcast evacuation guidance messages using the speaker 240 of the robot 414. For example, the image transfer unit can use the communication I / F 44 of the robot 414 to transmit images taken from the sky to local government officials in real time.

[0152] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0153] The sensor unit can monitor not only the river's water level, but also its flow rate and water quality. For example, flow rate is measured using a current meter, and water quality is monitored by measuring pH, turbidity, and chemical concentrations. This allows the sensor unit to integrate data on the river's water level, flow rate, and water quality to perform comprehensive risk assessments. Furthermore, when an abnormality is detected, the sensor unit can cross-check the data in conjunction with other sensors. The cross-check uses data consistency and anomaly detection algorithms, which confirms the accuracy of the abnormality and reduces false alarms. Furthermore, the sensor unit periodically performs self-diagnosis to detect malfunctions and abnormalities early and notify the need for maintenance.

[0154] The analysis unit can use not only meteorological data, but also topographical data and data on past disasters for its analysis. For example, topographical data includes elevation data and topographical maps, and past disaster data includes past flood data and disaster history. This allows the analysis unit to integrate meteorological data, topographical data, and past disaster data to perform comprehensive risk assessments. Furthermore, when the analysis unit detects an anomaly, it can collaborate with other AI systems to improve the accuracy of the analysis. Other AI systems include anomaly detection systems and predictive models, which allow data sharing and improved analysis accuracy. Furthermore, the analysis unit can periodically perform self-learning to optimize its analysis algorithm. Self-learning includes the type of learning data and the learning frequency, which allows it to update the analysis algorithm and improve analysis accuracy.

[0155] The route creation unit can create routes using not only weather data but also terrain data and past flight data. For example, terrain data includes elevation data and topographical maps, and past flight data includes flight logs and flight route history. This allows the route creation unit to integrate weather data, terrain data, and past flight data to create an optimal flight route. Furthermore, when an anomaly is detected, the route creation unit can work with other AI systems to improve route accuracy. Other AI systems include anomaly detection systems and predictive models, which can share data and improve route accuracy. Furthermore, the route creation unit can periodically perform self-learning to optimize its route creation algorithm. Self-learning includes the type of learning data and the learning frequency, which allows the route creation algorithm to be updated and the route accuracy to be improved.

[0156] When the control unit detects an abnormality, it can optimize the flight path in cooperation with other drones. For example, the other drones may include communication protocols and coordination algorithms, which allow them to share data, optimize the flight path, and identify the cause of the abnormality. The control unit can also improve flight accuracy by referencing past flight data. Past flight data includes flight logs and flight path history, which can improve flight path accuracy. Furthermore, the control unit can periodically perform self-diagnosis to detect malfunctions or abnormalities early and notify the user of the need for maintenance. The self-diagnosis includes diagnostic frequency and diagnostic items, which allows for early detection of malfunctions or abnormalities and notify the user of the need for maintenance.

[0157] The voice broadcasting unit can be equipped with a function that broadcasts voice messages in multiple languages ​​when an abnormality is detected. For example, the multiple languages ​​can include Japanese, English, Spanish, and French, allowing for multifaceted notification of abnormalities. The voice broadcasting unit can also improve the accuracy of messages by referencing past broadcast data. Past broadcast data includes broadcast logs and broadcast content history, which can be used to optimize the content of messages and provide appropriate evacuation guidance. Furthermore, the voice broadcasting unit can periodically perform self-diagnosis to detect malfunctions or abnormalities early and notify the need for maintenance. The self-diagnosis includes diagnostic frequency and diagnostic items, allowing for early detection of malfunctions or abnormalities and notification of the need for maintenance.

[0158] The sensor unit can estimate the user's emotions and adjust the sensitivity of the sensor based on the estimated user's emotions. For example, the user's emotions can be estimated using facial expression recognition or voice analysis, and the sensor sensitivity can be adjusted accordingly to reduce false alarms and issue an alarm at an appropriate time. The sensor unit can also optimize the installation location of the sensor based on the user's emotions. This allows the sensor installation location to be optimized according to the user's emotions, providing a sense of security. Furthermore, by adjusting the sensitivity of the sensor based on the user's emotions, the sensor unit can reduce false alarms and issue an alarm at an appropriate time.

[0159] The alarm unit can estimate the user's emotions and adjust the volume and content of the alarm based on the estimated user's emotions. For example, the user's emotions can be estimated using facial expression recognition or voice analysis, and the volume and content of the alarm can be adjusted based on this to issue an appropriate alarm. The alarm unit can also determine the priority of the alarm based on the user's emotions. This allows the priority of the alarm to be determined according to the user's emotions, and important alarms to be notified preferentially. Furthermore, the alarm unit can adjust the volume and content of the alarm based on the user's emotions to issue an appropriate alarm.

[0160] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, the user's emotions can be estimated using facial expression recognition or voice analysis, and the display method of the analysis results can be adjusted based on this to improve visibility. The analysis unit can also determine the priority of the analysis results based on the user's emotions. This allows the priority of the analysis results to be determined according to the user's emotions, and important analysis results can be displayed preferentially. Furthermore, the analysis unit can improve visibility by adjusting the display method of the analysis results based on the user's emotions.

[0161] The route creation unit can estimate the user's emotions and adjust the flight route creation method based on the estimated user's emotions. For example, the user's emotions can be estimated using facial expression recognition, voice analysis, etc., and the flight route creation method can be adjusted based on the estimated user's emotions, thereby providing an optimal route. The route creation unit can also determine the priority of flight routes based on the user's emotions. This allows the flight route priorities to be determined according to the user's emotions, and important routes to be provided preferentially. Furthermore, the route creation unit can provide an optimal route by adjusting the flight route creation method based on the user's emotions.

[0162] The control unit can estimate the user's emotions and adjust the drone's flight speed and altitude based on the estimated user's emotions. For example, the user's emotions can be estimated using facial expression recognition or voice analysis, and the drone's flight speed and altitude can be adjusted accordingly, allowing for rapid and detailed information to be provided. The control unit can also optimize the drone's flight path based on the user's emotions. This allows for the flight path to be optimized according to the user's emotions, and important paths to be provided with priority. Furthermore, the control unit can adjust the drone's flight speed and altitude based on the user's emotions, allowing for rapid and detailed information to be provided.

[0163] The processing flow of the second embodiment will be briefly explained below.

[0164] Step 1: The sensor unit monitors the water level of the river. For example, the sensor unit can measure the water level of the river in real time using an ultrasonic sensor or a laser sensor and collect data. The sensor unit can also issue an alarm when a certain water level is reached. Step 2: The alarm unit issues an alarm based on the data collected by the sensor unit. For example, the alarm unit can issue a voice alert or a text message. Step 3: The analysis unit analyzes weather maps and predicts areas where damage is likely to occur. For example, the analysis unit can analyze meteorological data to identify areas where heavy rain is expected or areas where river flooding is expected. Step 4: The route creation unit creates a flight path for the drone based on the estimated damage area predicted by the analysis unit. For example, the route creation unit can create an optimal flight path using GPS data and topographical data. Step 5: The control unit automatically flies the drone based on the flight path created by the path creation unit. For example, the control unit can control the drone in real time using an autopilot system. Step 6: The voice broadcasting unit, controlled by the control unit, allows the drone to fly and provide evacuation guidance to residents in the affected area via voice. For example, the voice broadcasting unit can use a speaker to broadcast a message such as, "This area is dangerous. Please evacuate immediately." Step 7: The image transmission unit transmits the images taken by the drone, which is flown by the control unit, to local government officials. For example, the image transmission unit can use 4G or 5G mobile communications to transmit images taken from the sky to local government officials in real time.

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0169] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0170] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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 AI 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.

[0183] 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.

[0184] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0185] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0193] 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.

[0194] 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.

[0195] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0196] 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.

[0197] 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.

[0198] 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 AI 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.

[0199] 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.

[0200] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0201] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0202] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0213] 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.

[0214] 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.

[0215] 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 AI 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.

[0216] 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.

[0217] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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."

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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, in order to avoid confusion and to 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.

[0235] 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.

[0236] [Explanation of symbols]

[0237] 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 monitors the water level of the river; an alarm unit that issues an alarm based on the data collected by the sensor unit; An analysis section that analyzes weather charts and predicts areas of potential damage; a route creation unit that creates a flight route for the drone based on the estimated damage area predicted by the analysis unit; a control unit that automatically flies the drone based on the flight path created by the path creation unit; An audio broadcasting unit that causes the drone to fly under the control of the control unit to provide evacuation guidance to residents in the predicted damage area by voice; and an image transfer unit that transfers images taken by the drone flown by the control unit to local government officials. A system characterized by:

2. The sensor unit Monitoring the water level of a river using at least one of an ultrasonic sensor and a laser sensor 2. The system of claim 1.

3. The analysis unit Analyzing weather data and predicting areas likely to be affected 2. The system of claim 1.

4. The path creation unit Create a drone flight path based on the estimated damage area 2. The system of claim 1.

5. The control unit Use GPS to fly drones automatically 2. The system of claim 1.

6. The audio broadcasting unit Using a speaker to guide residents to evacuate 2. The system of claim 1.

7. The image transfer unit Use at least one of 4G or 5G mobile communications to transmit aerial images to local government officials.

2. The system of claim 1.

8. The control unit Analyzing images taken by drones and automatically classifying the damage situation 2. The system of claim 1.

9. The sensor unit The user's emotion is estimated, and the sensitivity of the sensor is adjusted based on the estimated user's emotion.

2. The system of claim 1.

10. The sensor unit Monitor not only water level but also flow rate and water quality 2. The system of claim 1.

Images