System

Abstract

To provide a system according to an embodiment that early predicts the occurrence of a natural disaster and issues a warning.SOLUTION: A system comprises a collection unit, an analysis unit, a prediction unit, and a warning unit. The collection unit collects weather information or data on a sensor. The analysis unit analyzes the data collected by the collection unit. The prediction unit predicts the occurrence of a natural disaster on the basis of an analysis result obtained by the analysis unit. The warning unit issues a warning on the basis of a prediction result obtained by the prediction unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has not been sufficient in predicting and alerting for the occurrence of natural disasters at an early stage, and there is room for improvement.

[0005] The system according to the embodiment aims to predict the occurrence of natural disasters at an early stage and to provide warning. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, and a warning unit. The collection unit collects meteorological information or sensor data. The analysis unit analyzes the data collected by the collection unit. The prediction unit predicts the occurrence of a natural disaster based on the analysis result obtained by the analysis unit. The warning unit exercises warning based on the prediction result obtained by the prediction unit. Effect of the Invention

[0007] The system according to the embodiment can predict and warn of the occurrence of natural disasters at an early stage. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The natural disaster early warning system according to the embodiment of the present invention is a system that collects meteorological information and sensor data, analyzes it with AI, predicts the occurrence of natural disasters, and performs early warning. This system performs early warning by collecting meteorological information and sensor data, analyzing it with AI, and predicting the occurrence of natural disasters. For example, as meteorological information, data such as temperature, humidity, wind speed, and precipitation are collected. As sensor data, data such as seismometers and water level gauges are collected. For example, data provided by the Japan Meteorological Agency and observation stations in various locations can be collected. Next, the AI ​​analyzes the collected data. The AI ​​predicts the occurrence of natural disasters based on the collected data. For example, it can predict the occurrence of heavy rain from changes in temperature and humidity, and predict the occurrence of earthquakes from seismometer data. The AI ​​can make highly accurate predictions by learning past data and recognizing patterns. Based on the prediction results, an early warning is performed. For example, when heavy rain is predicted, an evacuation advisory can be issued to residents. Also, when an earthquake is predicted, earthquake resistance measures for buildings can be strengthened. In this way, damage from natural disasters can be minimized. This system can provide early warning of the occurrence of natural disasters and ensure the safety of residents. For example, if heavy rain is predicted, early evacuation can prevent damage from floods and landslides. Similarly, if an earthquake is predicted, strengthening earthquake resistance measures for buildings can reduce damage from the earthquake. In this way, natural disaster early warning systems can provide early warning of natural disasters and ensure the safety of residents.

[0029] The natural disaster early warning system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, and a warning unit. The collection unit collects meteorological information and sensor data. The meteorological information includes, for example, temperature, humidity, wind speed, and precipitation. The sensor data includes, for example, data from a seismometer and a water level gauge. The collection unit can collect data provided by, for example, the Japan Meteorological Agency and observation stations in various locations. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, machine learning or deep learning. Machine learning and deep learning algorithms include neural networks and support vector machines. The prediction unit predicts the occurrence of a natural disaster based on the analysis results obtained by the analysis unit. The prediction unit can predict the occurrence of heavy rain from changes in temperature and humidity, and predict the occurrence of an earthquake from seismometer data, for example. The prediction unit can perform highly accurate predictions by learning past data and recognizing patterns. The warning unit performs early warning based on the prediction results obtained by the prediction unit. For example, when the occurrence of heavy rain is predicted, the warning unit can issue an evacuation advisory to residents. Also, when the occurrence of an earthquake is predicted, the warning unit can strengthen earthquake resistance measures of buildings. In this way, the natural disaster early warning system according to the embodiment can provide early warning of the occurrence of a natural disaster and ensure the safety of residents.

[0030] The collection unit collects meteorological information and sensor data. Meteorological information includes, for example, temperature, humidity, wind speed, and precipitation. These meteorological information can be collected in real time from the data provided by the Japan Meteorological Agency and observation stations in various regions. Specifically, an API is used to access the database of the Japan Meteorological Agency and obtain the latest meteorological information. In addition, data is obtained from observation stations in various regions using a dedicated communication protocol. The sensor data includes, for example, data from seismometers and water level gauges. Seismometers detect earthquake vibrations and collect their amplitude and frequency as data. Water level gauges measure the water level of rivers and lakes and monitor their fluctuations in real time. These sensors are installed in various regions and transmit data to a central server via wireless communication or the Internet. The collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server so that the analysis unit and prediction unit can access it. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively and improve the performance of the entire system. Furthermore, the collection unit can implement algorithms for detecting outliers and complementing data to ensure data quality. For example, to complement data missing due to sensor failure or communication failure, data from surrounding sensors is used for interpolation. In addition, to detect outliers, the collection unit automatically identifies abnormal values ​​by comparing with past data and performs appropriate processing. This allows the collection unit to provide highly reliable data and improve the accuracy of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, machine learning or deep learning. Machine learning and deep learning algorithms include neural networks and support vector machines. Specifically, the neural network is used to learn weather data patterns and detect abnormal weather conditions. The support vector machine is used to analyze seismometer data and detect earthquake precursors. The analysis unit can use these algorithms to analyze collected data in real time and quickly detect abnormal patterns and precursors. Furthermore, the analysis unit can also use past data and statistical information to perform long-term risk assessment and trend analysis. For example, based on past weather data, the analysis unit predicts risk fluctuations in a specific area or time period and plans future countermeasures. Furthermore, the analysis unit can use an anomaly detection algorithm to detect unusual patterns and abnormal data and issue an early warning. As a result, the analysis unit can not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system. Furthermore, the analysis unit can improve the accuracy of the analysis by performing data preprocessing and feature extraction. For example, the accuracy of analysis can be improved by removing noise from meteorological data and extracting important features. Also, seismometer data can be filtered and minute vibrations emphasized to more accurately detect earthquake precursors. This allows the analysis unit to analyze collected data efficiently and effectively, improving the performance of the entire system.

[0032] The prediction unit predicts the occurrence of a natural disaster based on the analysis results obtained by the analysis unit. The prediction unit can predict the occurrence of heavy rain from changes in temperature and humidity, and predict the occurrence of an earthquake from seismometer data, for example. Specifically, the prediction unit detects a sudden change in temperature and humidity, and compares it with past data to calculate the probability of heavy rain. In addition, the prediction unit analyzes seismometer data and detects a pattern of minute vibrations to predict a precursor to an earthquake. The prediction unit can perform highly accurate predictions by learning past data and recognizing patterns. For example, based on past heavy rain data, the prediction unit calculates the probability of heavy rain occurring under specific weather conditions and provides a prediction result. In addition, based on past earthquake data, the prediction unit determines whether a specific vibration pattern is a precursor to an earthquake and provides a prediction result. In addition, based on data updated in real time, the prediction unit can continuously revise the prediction result to respond to the latest situation. For example, if the temperature or humidity changes suddenly, the prediction unit immediately takes in new data and updates the prediction result. In addition, if the seismometer data changes suddenly, the prediction unit immediately takes in new data and updates the prediction result. This allows the prediction unit to always provide highly accurate predictions based on the latest information and support rapid and appropriate responses. Furthermore, the prediction unit can identify the most likely risk by simulating multiple scenarios. For example, multiple weather scenarios are simulated based on meteorological data to identify the most likely scenario of heavy rain. Also, multiple earthquake scenarios are simulated based on earthquake data to identify the most likely scenario of earthquake occurrence. This allows the prediction unit to predict disaster risks with high accuracy and provide information for taking appropriate measures.

[0033] The warning unit performs early warning based on the prediction results obtained by the prediction unit. For example, when the occurrence of heavy rain is predicted, the warning unit can issue an evacuation advisory to residents. Specifically, the warning unit issues an evacuation advisory to residents in an area where the occurrence of heavy rain is predicted through smartphone notifications, emails, voice calls, etc. Furthermore, when the occurrence of an earthquake is predicted, earthquake resistance measures of buildings can be strengthened. Specifically, the building owners in the area where the occurrence of an earthquake is predicted are notified of the need for earthquake resistance reinforcement and specific measures are proposed. This allows the warning unit to warn of the occurrence of natural disasters early and ensure the safety of residents. Furthermore, the warning unit can formulate an emergency response plan based on the prediction results and respond quickly in cooperation with related organizations. For example, when the occurrence of heavy rain is predicted, the warning unit works with fire departments, police departments, medical institutions, etc. to set up evacuation shelters and prepare for rescue activities. Furthermore, when the occurrence of an earthquake is predicted, the warning unit works with construction companies and infrastructure managers to prepare for earthquake resistance reinforcement and emergency repairs. This allows the warning unit to respond quickly and appropriately to the occurrence of natural disasters and minimize damage. Furthermore, the Warning Department can collect feedback from residents and related organizations to continuously improve the accuracy and effectiveness of the warning system. For example, based on feedback from residents who have received evacuation advisories, the department can review evacuation routes and improve the method of providing warning information. Also, based on feedback from related organizations, the department can review emergency response plans and strengthen collaboration systems. This allows the Warning Department to always provide a highly accurate warning system based on the latest information and feedback, ensuring the safety of residents.

[0034] The collection unit can collect weather information or sensor data provided by the Japan Meteorological Agency or observation stations in various locations. The collection unit collects, for example, weather information provided by the Japan Meteorological Agency. The weather information includes temperature, humidity, wind speed, precipitation, and the like. The collection unit can also collect sensor data provided by observation stations in various locations. The sensor data includes data from seismometers and water level gauges. For example, the collection unit can access the Japan Meteorological Agency's database to obtain the latest weather information. The collection unit can also collect data in real time from observation stations in various locations. This allows the collection unit to collect highly reliable data.

[0035] The analysis unit can analyze the collected data using machine learning or deep learning. The analysis unit, for example, analyzes the collected data using machine learning. Machine learning algorithms include neural networks and support vector machines. The analysis unit can also analyze data using deep learning. Deep learning algorithms include convolutional neural networks (CNNs) and recurrent neural networks (RNNs). For example, the analysis unit inputs collected weather data into a neural network to analyze changes in temperature and humidity. The analysis unit can also input data from a seismometer into a support vector machine to analyze the occurrence of earthquakes. This allows the analysis unit to perform highly accurate data analysis.

[0036] The prediction unit can predict the occurrence of heavy rain based on the analysis result. The prediction unit predicts the occurrence of heavy rain based on, for example, data on changes in temperature and humidity obtained by the analysis unit. The prediction unit can predict the occurrence of heavy rain by learning past heavy rain data and recognizing patterns of changes in temperature and humidity. For example, the prediction unit predicts the occurrence of heavy rain when the temperature rises sharply and the humidity becomes high. The prediction unit can also predict the occurrence of heavy rain by taking into account data on wind speed and precipitation. This allows the prediction unit to predict the occurrence of heavy rain with high accuracy.

[0037] The prediction unit can predict the occurrence of an earthquake based on the analysis results. The prediction unit predicts the occurrence of an earthquake based on, for example, seismometer data obtained by the analysis unit. The prediction unit can predict the occurrence of an earthquake by learning past earthquake data and recognizing data patterns of the seismometer. For example, the prediction unit predicts the occurrence of an earthquake when vibration data from the seismometer shows a specific pattern. The prediction unit can also predict the occurrence of an earthquake by taking into account data on the location of the earthquake and its seismic intensity. This allows the prediction unit to predict the occurrence of an earthquake with high accuracy.

[0038] The alert department can issue an evacuation advisory to residents based on the prediction results. For example, when the prediction department predicts the occurrence of heavy rain, the alert department issues an evacuation advisory to residents. The alert department can set conditions for issuing evacuation advisories based on the prediction results and quickly issue evacuation advisories to residents. For example, when the occurrence of heavy rain is predicted, the alert department issues an evacuation advisory to a specific area. In addition, the alert department can use smartphone apps, email, television, radio, etc. as a means of notifying the evacuation advisory. This allows the alert department to quickly issue evacuation advisories to residents.

[0039] The alert unit can strengthen earthquake resistance measures of the building based on the prediction result. For example, when the prediction unit predicts the occurrence of an earthquake, the alert unit strengthens the earthquake resistance measures of the building. The alert unit can set conditions for strengthening the earthquake resistance measures of the building based on the prediction result, and can quickly strengthen the earthquake resistance measures of the building. For example, when the occurrence of an earthquake is predicted, the alert unit can carry out earthquake reinforcement work on a specific building. Furthermore, the alert unit can install earthquake reinforcement materials or reinforce the structure of the building as a means of strengthening the earthquake resistance measures of the building. This allows the alert unit to quickly strengthen the earthquake resistance measures of the building.

[0040] The collection unit can analyze past disaster data and select an optimal data collection method. The collection unit can analyze, for example, past heavy rain data and optimize a data collection method under specific weather conditions. The collection unit can use AI to analyze past disaster data. The AI ​​learns past data and recognizes patterns for selecting an optimal data collection method. For example, the collection unit inputs past heavy rain data into the AI ​​and optimizes a data collection method under specific weather conditions. The collection unit can also analyze past earthquake data and optimize sensor placement at the time of an earthquake occurrence. For example, the collection unit inputs past earthquake data into the AI ​​and optimizes sensor placement at the time of an earthquake occurrence. Furthermore, the collection unit can analyze past flood data and optimize the installation location of a water level gauge. For example, the collection unit inputs past flood data into the AI ​​and optimizes the installation location of a water level gauge. This allows the collection unit to select an optimal data collection method based on past disaster data. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0041] The collection unit can prioritize collection of specific weather conditions and abnormal values ​​of sensors when collecting data. For example, the collection unit prioritizes collection of data from a wind speed sensor when a typhoon approaches. The collection unit can use AI to prioritize collection of specific weather conditions and abnormal values ​​of sensors. The AI ​​recognizes a pattern for detecting specific weather conditions and abnormal values ​​of sensors and collecting data preferentially. For example, the collection unit inputs data from a wind speed sensor to the AI ​​when a typhoon approaches and prioritizes collection of the data from the wind speed sensor. The collection unit can also prioritize collection of abnormal values ​​of a seismometer when an earthquake occurs. For example, the collection unit inputs data from a seismometer to the AI ​​when an earthquake occurs and prioritizes collection of abnormal values ​​from the seismometer. Furthermore, the collection unit can also prioritize collection of data from a water level gauge when heavy rain falls. For example, the collection unit inputs data from a water level gauge to the AI ​​when heavy rain falls and prioritizes collection of the data from the water level gauge. This allows the collection unit to prioritize collection of specific weather conditions and abnormal values ​​of sensors. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or may be performed without using AI.

[0042] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting data. For example, when the user is in a coastal area, the collection unit preferentially collects tsunami warning data. The collection unit can use AI to collect highly relevant data in consideration of the geographical location information of the user. The AI ​​recognizes a pattern for preferentially collecting highly relevant data based on the geographical location information of the user. For example, when the user is in a coastal area, the collection unit inputs tsunami warning data to the AI ​​and preferentially collects the tsunami warning data. In addition, the collection unit can also preferentially collect landslide data when the user is in a mountainous area. For example, when the user is in a mountainous area, the collection unit inputs landslide data to the AI ​​and preferentially collects the landslide data. In addition, the collection unit can also preferentially collect flood warning data when the user is in an urban area. For example, when the user is in an urban area, the collection unit inputs flood warning data to the AI ​​and preferentially collects the flood warning data. This allows the collection unit to preferentially collect highly relevant data based on the geographical location information of the user. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or may be performed without using AI.

[0043] The collection unit can analyze the social media activity of the user at the time of data collection and collect related data. For example, when the user posts about heavy rain, the collection unit prioritizes collecting data about heavy rain. The collection unit can use AI to analyze the social media activity of the user. The AI ​​recognizes a pattern for collecting related data based on the social media activity of the user. For example, when the user posts about heavy rain, the collection unit inputs data about heavy rain into the AI ​​and prioritizes collecting data about heavy rain. In addition, when the user posts about earthquakes, the collection unit can also prioritize collecting data about earthquakes. For example, when the user posts about earthquakes, the collection unit inputs data about earthquakes into the AI ​​and prioritizes collecting data about earthquakes. In addition, the collection unit can also prioritize collecting data about floods when the user posts about floods. For example, when the user posts about floods, the collection unit inputs data about floods into the AI ​​and prioritizes collecting data about floods. This allows the collection unit to collect related data based on the social media activity of the user. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or may be performed without using AI.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can use AI to evaluate the importance of the data. The AI ​​evaluates the importance of the data based on the influence and relevance of the data. For example, the analysis unit inputs data with high importance to the AI ​​and performs a detailed analysis. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit inputs data with low importance to the AI ​​and performs a simplified analysis. Furthermore, the analysis unit can also perform an analysis with a moderate level of detail on data with medium importance. For example, the analysis unit inputs data with medium importance to the AI ​​and performs an analysis with a moderate level of detail. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI.

[0045] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a weather forecasting algorithm to weather data. The analysis unit can use AI to apply the optimal analysis algorithm depending on the category of data. The AI ​​selects the optimal analysis algorithm based on the category of data. For example, the analysis unit inputs weather data to AI and applies a weather forecasting algorithm. The analysis unit can also apply an earthquake forecasting algorithm to earthquake data. For example, the analysis unit inputs earthquake data to AI and applies an earthquake forecasting algorithm. The analysis unit can also apply a flood forecasting algorithm to flood data. For example, the analysis unit inputs flood data to AI and applies a flood forecasting algorithm. This allows the analysis unit to apply the optimal analysis algorithm depending on the category of data. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

[0046] The analysis unit can determine the priority of the analysis based on the collection time of the data at the time of analysis. The analysis unit, for example, prioritizes the analysis of the latest data. The analysis unit can use AI to determine the priority of the analysis taking into account the collection time of the data. The AI ​​recognizes a pattern for determining the priority of the analysis based on the collection time of the data. For example, the analysis unit inputs the latest data into the AI ​​and analyzes it with priority. The analysis unit can also analyze with emphasis on the latest data while referring to past data. For example, the analysis unit inputs past data and the latest data into the AI ​​and analyzes with emphasis on the latest data. Furthermore, the analysis unit can also analyze with priority on data collected at a specific time. For example, the analysis unit inputs data collected at a specific time into the AI ​​and analyzes it with priority. This allows the analysis unit to determine the priority of the analysis based on the collection time of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0047] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, preferentially analyzes highly relevant data. The analysis unit can use AI to evaluate the relevance of data. The AI ​​evaluates the relevance of data based on the correlation coefficient or relevance score of data. For example, the analysis unit inputs highly relevant data to the AI ​​and analyzes it preferentially. The analysis unit can also postpone data with low relevance. For example, the analysis unit inputs data with low relevance to the AI ​​and postpones it. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of data. For example, the analysis unit inputs the relevance of data to the AI ​​and dynamically adjusts the order of analysis. This allows the analysis unit to adjust the order of analysis based on the relevance of data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0048] The prediction unit can improve the accuracy of the prediction by considering the interrelationship of data at the time of prediction. The prediction unit makes a prediction by considering, for example, the interrelationship between weather data and earthquake data. The prediction unit can use AI to evaluate the interrelationship of data. The AI ​​evaluates the interrelationship of data based on correlation analysis of data and analysis of causal relationship. For example, the prediction unit inputs the interrelationship between weather data and earthquake data into the AI ​​and makes a prediction. The prediction unit can also make a prediction by considering the interrelationship between weather data and flood data. For example, the prediction unit inputs the interrelationship between weather data and flood data into the AI ​​and makes a prediction. Furthermore, the prediction unit can also make a prediction by considering the interrelationship between earthquake data and tsunami data. For example, the prediction unit inputs the interrelationship between earthquake data and tsunami data into the AI ​​and makes a prediction. Thereby, the prediction unit can improve the accuracy of the prediction by considering the interrelationship of data. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI.

[0049] The prediction unit can make predictions taking into account the attribute information of the data provider at the time of prediction. The prediction unit, for example, emphasizes data provided by the Japan Meteorological Agency to make predictions. The prediction unit can use AI to make predictions taking into account the attribute information of the data provider. The AI ​​recognizes patterns for improving the accuracy of predictions based on attribute information such as the occupation and region of the data provider. For example, the prediction unit inputs data provided by the Japan Meteorological Agency into the AI ​​and emphasizes it to make predictions. The prediction unit can also make predictions taking into account data provided by a local observation station. For example, the prediction unit inputs data provided by a local observation station into the AI ​​and emphasizes it to make predictions. Furthermore, the prediction unit can also make predictions taking into account data provided by an international organization. For example, the prediction unit inputs data provided by an international organization into the AI ​​and emphasizes it to make predictions. As a result, the prediction unit can improve the accuracy of predictions taking into account the attribute information of the data provider. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI.

[0050] The prediction unit can make predictions taking into account the geographical distribution of data at the time of prediction. The prediction unit, for example, makes predictions by placing emphasis on geographically close data. The prediction unit can use AI to evaluate the geographical distribution of data. The AI ​​evaluates the geographical distribution of data using techniques such as geographical cluster analysis and map display. For example, the prediction unit inputs geographically close data into the AI ​​and places emphasis on the data to make predictions. The prediction unit can also place emphasis on close data while referring to geographically distant data. For example, the prediction unit inputs geographically distant data and close data into the AI ​​and places emphasis on the close data to make predictions. Furthermore, the prediction unit can adjust the accuracy of predictions according to the geographical distribution. For example, the prediction unit inputs the geographical distribution into the AI ​​and adjusts the accuracy of predictions. As a result, the prediction unit can improve the accuracy of predictions by taking into account the geographical distribution of data. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI.

[0051] The prediction unit can improve the accuracy of the prediction by referring to the literature related to the data at the time of prediction. The prediction unit, for example, makes a prediction by referring to the latest research paper. The prediction unit can use AI to improve the accuracy of the prediction by referring to the literature related to the data. The AI ​​refers to the related literature using a literature database, citation criteria, etc. For example, the prediction unit inputs the latest research paper into the AI ​​and makes a prediction by referring to it. The prediction unit can also make a prediction by referring to past research results. For example, the prediction unit inputs past research results into the AI ​​and makes a prediction by referring to it. Furthermore, the prediction unit can also improve the accuracy of the prediction by referring to related academic literature. For example, the prediction unit inputs related academic literature into the AI ​​and improves the accuracy of the prediction by referring to it. Thereby, the prediction unit can improve the accuracy of the prediction by referring to the literature related to the data. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI.

[0052] The alert unit can optimize the current alert by referring to past alert data during alert. The alert unit optimizes the current alert by referring to, for example, past heavy rain alert data. The alert unit can use AI to optimize the current alert by referring to the past alert data. The AI ​​recognizes a pattern for optimizing the current alert based on the past alert history and the alert database. For example, the alert unit inputs past heavy rain alert data into the AI ​​and optimizes the current alert by referring to it. The alert unit can also optimize the current alert by referring to past earthquake alert data. For example, the alert unit inputs past earthquake alert data into the AI ​​and optimizes the current alert by referring to it. Furthermore, the alert unit can also optimize the current alert by referring to past flood alert data. For example, the alert unit inputs past flood alert data into the AI ​​and optimizes the current alert by referring to it. This allows the alert unit to optimize the current alert by referring to the past alert data. A part or all of the above-mentioned processing in the alert unit may be performed, for example, using AI, or may be performed without using AI.

[0053] The alert unit can apply different alert methods for each data category during alert. For example, during heavy rain alert, the alert unit places emphasis on precipitation data. The alert unit can use AI to apply the optimal alert method for each data category. The AI ​​selects the optimal alert method based on categories such as weather data and earthquake data. For example, during heavy rain alert, the alert unit inputs precipitation data into the AI ​​and places emphasis on the data for alert. Furthermore, during earthquake alert, the alert unit can also place emphasis on seismometer data for alert. For example, during earthquake alert, the alert unit inputs seismometer data into the AI ​​and places emphasis on the data for alert. Furthermore, during flood alert, the alert unit can also place emphasis on water level gauge data for alert. For example, during flood alert, the alert unit inputs water level gauge data into the AI ​​and places emphasis on the data for alert. This allows the alert unit to apply the optimal alert method for each data category. A part or all of the above-mentioned processing in the alert unit may be performed using, for example, AI, or may be performed without using AI.

[0054] The alert unit can analyze changes in alertness based on the data collection time during alert. The alert unit, for example, analyzes changes in alertness based on the latest data. The alert unit can use AI to analyze changes in alertness taking into account the data collection time. The AI ​​recognizes a pattern for analyzing changes in alertness based on the data collection time. For example, the alert unit inputs the latest data into the AI ​​and analyzes changes in alertness based on the latest data. The alert unit can also analyze changes in alertness with emphasis on the latest data while referring to past data. For example, the alert unit inputs past data and the latest data into the AI ​​and analyzes changes in alertness with emphasis on the latest data. Furthermore, the alert unit can also analyze changes in alertness based on data collected at a specific time. For example, the alert unit inputs data collected at a specific time into the AI ​​and analyzes changes in alertness based on the latest data. This allows the alert unit to analyze changes in alertness based on the data collection time. A part or all of the above-mentioned processing in the alert unit may be performed using, for example, an AI, or may be performed without using an AI.

[0055] The alerting unit can analyze alerting by referring to the relevant market data of the data at the time of alerting. The alerting unit, for example, analyzes changes in alerting based on the relevant market data. The alerting unit can use AI to analyze alerting by referring to the relevant market data of the data. The AI ​​refers to the relevant market data using a market database, economic indicators, etc. For example, the alerting unit inputs the relevant market data into the AI ​​and analyzes changes in alerting based on the data. The alerting unit can also analyze alerting by considering the interrelationship between the market data and the weather data. For example, the alerting unit inputs the interrelationship between the market data and the weather data into the AI ​​and analyzes alerting based on the data. Furthermore, the alerting unit can analyze alerting by considering the interrelationship between the market data and the earthquake data. For example, the alerting unit inputs the interrelationship between the market data and the earthquake data into the AI ​​and analyzes alerting based on the data. This allows the alerting unit to improve the accuracy of alerting by referring to the relevant market data of the data. A part or all of the above-mentioned processing in the alerting unit may be performed, for example, using AI, or may be performed without using AI.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The analysis unit can evaluate the reliability of the data source and prioritize analysis of highly reliable data. For example, data provided by the Japan Meteorological Agency or international organizations is evaluated as being highly reliable and is prioritized for analysis. Data provided by local observation stations may also be evaluated as being highly reliable. Furthermore, the analysis unit can take into account the data provider's past performance and the consistency of the data in order to evaluate the reliability of the data. This allows the analysis unit to perform highly accurate analysis based on highly reliable data.

[0058] The collection unit can prioritize collection of highly relevant data in consideration of the geographical location information of the user. For example, if the user is in a coastal area, data on tsunami warnings can be prioritized. If the user is in a mountainous area, data on landslides can be prioritized. Furthermore, if the user is in an urban area, data on flood warnings can be prioritized. This allows the collection unit to prioritize collection of highly relevant data based on the geographical location information of the user.

[0059] The prediction unit can improve the accuracy of the prediction by taking into account the interrelationships of data. For example, a prediction is made by taking into account the interrelationships between meteorological data and earthquake data. A prediction can also be made by taking into account the interrelationships between meteorological data and flood data. A prediction can also be made by taking into account the interrelationships between earthquake data and tsunami data. In this way, the prediction unit can improve the accuracy of the prediction by taking into account the interrelationships of data.

[0060] The alert section can optimize the current alert by referring to past alert data. For example, the current alert can be optimized by referring to past heavy rain alert data. The current alert can also be optimized by referring to past earthquake alert data. Furthermore, the current alert can also be optimized by referring to past flood alert data. In this way, the alert section can optimize the current alert by referring to past alert data.

[0061] The warning unit can apply different warning methods to each data category. For example, during a heavy rain warning, the warning can be given priority to precipitation data. During an earthquake warning, the warning can be given priority to seismometer data. Furthermore, during a flood warning, the warning can be given priority to water level gauge data. This allows the warning unit to apply the optimal warning method for each data category.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The collection unit collects meteorological information and sensor data. Meteorological information includes, for example, temperature, humidity, wind speed, and precipitation. Sensor data includes, for example, data from a seismometer and a water level gauge. The collection unit can collect data provided by, for example, the Japan Meteorological Agency and observation stations in various locations. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, machine learning or deep learning. Machine learning and deep learning algorithms include neural networks and support vector machines. Step 3: The prediction unit predicts the occurrence of natural disasters based on the analysis results obtained by the analysis unit. For example, the prediction unit can predict the occurrence of heavy rain from changes in temperature and humidity, or the occurrence of an earthquake from seismometer data. The prediction unit can make highly accurate predictions by learning from past data and recognizing patterns. Step 4: The warning unit performs early warning based on the prediction results obtained by the prediction unit. For example, if the occurrence of heavy rain is predicted, the warning unit can issue an evacuation advisory to residents. Also, if the occurrence of an earthquake is predicted, the warning unit can strengthen earthquake resistance measures for buildings.

[0064] (Example 2) The natural disaster early warning system according to the embodiment of the present invention is a system that collects meteorological information and sensor data, analyzes it with AI, predicts the occurrence of natural disasters, and performs early warning. This system performs early warning by collecting meteorological information and sensor data, analyzing it with AI, and predicting the occurrence of natural disasters. For example, as meteorological information, data such as temperature, humidity, wind speed, and precipitation are collected. As sensor data, data such as seismometers and water level gauges are collected. For example, data provided by the Japan Meteorological Agency and observation stations in various locations can be collected. Next, the AI ​​analyzes the collected data. The AI ​​predicts the occurrence of natural disasters based on the collected data. For example, it can predict the occurrence of heavy rain from changes in temperature and humidity, and predict the occurrence of earthquakes from seismometer data. The AI ​​can make highly accurate predictions by learning past data and recognizing patterns. Based on the prediction results, an early warning is performed. For example, when heavy rain is predicted, an evacuation advisory can be issued to residents. Also, when an earthquake is predicted, earthquake resistance measures for buildings can be strengthened. In this way, damage from natural disasters can be minimized. This system can provide early warning of the occurrence of natural disasters and ensure the safety of residents. For example, if heavy rain is predicted, early evacuation can prevent damage from floods and landslides. Similarly, if an earthquake is predicted, strengthening earthquake resistance measures for buildings can reduce damage from the earthquake. In this way, natural disaster early warning systems can provide early warning of natural disasters and ensure the safety of residents.

[0065] The natural disaster early warning system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, and a warning unit. The collection unit collects meteorological information and sensor data. The meteorological information includes, for example, temperature, humidity, wind speed, and precipitation. The sensor data includes, for example, data from a seismometer and a water level gauge. The collection unit can collect data provided by, for example, the Japan Meteorological Agency and observation stations in various locations. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, machine learning or deep learning. Machine learning and deep learning algorithms include neural networks and support vector machines. The prediction unit predicts the occurrence of a natural disaster based on the analysis results obtained by the analysis unit. The prediction unit can predict the occurrence of heavy rain from changes in temperature and humidity, and predict the occurrence of an earthquake from seismometer data, for example. The prediction unit can perform highly accurate predictions by learning past data and recognizing patterns. The warning unit performs early warning based on the prediction results obtained by the prediction unit. For example, when the occurrence of heavy rain is predicted, the warning unit can issue an evacuation advisory to residents. Also, when the occurrence of an earthquake is predicted, the warning unit can strengthen earthquake resistance measures of buildings. In this way, the natural disaster early warning system according to the embodiment can provide early warning of the occurrence of a natural disaster and ensure the safety of residents.

[0066] The collection unit collects meteorological information and sensor data. Meteorological information includes, for example, temperature, humidity, wind speed, and precipitation. These meteorological information can be collected in real time from the data provided by the Japan Meteorological Agency and observation stations in various regions. Specifically, an API is used to access the database of the Japan Meteorological Agency and obtain the latest meteorological information. In addition, data is obtained from observation stations in various regions using a dedicated communication protocol. The sensor data includes, for example, data from seismometers and water level gauges. Seismometers detect earthquake vibrations and collect their amplitude and frequency as data. Water level gauges measure the water level of rivers and lakes and monitor their fluctuations in real time. These sensors are installed in various regions and transmit data to a central server via wireless communication or the Internet. The collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server so that the analysis unit and prediction unit can access it. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively and improve the performance of the entire system. Furthermore, the collection unit can implement algorithms for detecting outliers and complementing data to ensure data quality. For example, to complement data missing due to sensor failure or communication failure, data from surrounding sensors is used for interpolation. In addition, to detect outliers, the collection unit automatically identifies abnormal values ​​by comparing with past data and performs appropriate processing. This allows the collection unit to provide highly reliable data and improve the accuracy of the entire system.

[0067] The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, machine learning or deep learning. Machine learning and deep learning algorithms include neural networks and support vector machines. Specifically, the neural network is used to learn weather data patterns and detect abnormal weather conditions. The support vector machine is used to analyze seismometer data and detect earthquake precursors. The analysis unit can use these algorithms to analyze collected data in real time and quickly detect abnormal patterns and precursors. Furthermore, the analysis unit can also use past data and statistical information to perform long-term risk assessment and trend analysis. For example, based on past weather data, the analysis unit predicts risk fluctuations in a specific area or time period and plans future countermeasures. Furthermore, the analysis unit can use an anomaly detection algorithm to detect unusual patterns and abnormal data and issue an early warning. As a result, the analysis unit can not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system. Furthermore, the analysis unit can improve the accuracy of the analysis by performing data preprocessing and feature extraction. For example, the accuracy of analysis can be improved by removing noise from meteorological data and extracting important features. Also, seismometer data can be filtered and minute vibrations emphasized to more accurately detect earthquake precursors. This allows the analysis unit to analyze collected data efficiently and effectively, improving the performance of the entire system.

[0068] The prediction unit predicts the occurrence of a natural disaster based on the analysis results obtained by the analysis unit. The prediction unit can predict the occurrence of heavy rain from changes in temperature and humidity, and predict the occurrence of an earthquake from seismometer data, for example. Specifically, the prediction unit detects a sudden change in temperature and humidity, and compares it with past data to calculate the probability of heavy rain. In addition, the prediction unit analyzes seismometer data and detects a pattern of minute vibrations to predict a precursor to an earthquake. The prediction unit can perform highly accurate predictions by learning past data and recognizing patterns. For example, based on past heavy rain data, the prediction unit calculates the probability of heavy rain occurring under specific weather conditions and provides a prediction result. In addition, based on past earthquake data, the prediction unit determines whether a specific vibration pattern is a precursor to an earthquake and provides a prediction result. In addition, based on data updated in real time, the prediction unit can continuously revise the prediction result to respond to the latest situation. For example, if the temperature or humidity changes suddenly, the prediction unit immediately takes in new data and updates the prediction result. In addition, if the seismometer data changes suddenly, the prediction unit immediately takes in new data and updates the prediction result. This allows the prediction unit to always provide highly accurate predictions based on the latest information and support rapid and appropriate responses. Furthermore, the prediction unit can identify the most likely risk by simulating multiple scenarios. For example, multiple weather scenarios are simulated based on meteorological data to identify the most likely scenario of heavy rain. Also, multiple earthquake scenarios are simulated based on earthquake data to identify the most likely scenario of earthquake occurrence. This allows the prediction unit to predict disaster risks with high accuracy and provide information for taking appropriate measures.

[0069] The warning unit performs early warning based on the prediction results obtained by the prediction unit. For example, when the occurrence of heavy rain is predicted, the warning unit can issue an evacuation advisory to residents. Specifically, the warning unit issues an evacuation advisory to residents in an area where the occurrence of heavy rain is predicted through smartphone notifications, emails, voice calls, etc. Furthermore, when the occurrence of an earthquake is predicted, earthquake resistance measures of buildings can be strengthened. Specifically, the building owners in the area where the occurrence of an earthquake is predicted are notified of the need for earthquake resistance reinforcement and specific measures are proposed. This allows the warning unit to warn of the occurrence of natural disasters early and ensure the safety of residents. Furthermore, the warning unit can formulate an emergency response plan based on the prediction results and respond quickly in cooperation with related organizations. For example, when the occurrence of heavy rain is predicted, the warning unit works with fire departments, police departments, medical institutions, etc. to set up evacuation shelters and prepare for rescue activities. Furthermore, when the occurrence of an earthquake is predicted, the warning unit works with construction companies and infrastructure managers to prepare for earthquake resistance reinforcement and emergency repairs. This allows the warning unit to respond quickly and appropriately to the occurrence of natural disasters and minimize damage. Furthermore, the Warning Department can collect feedback from residents and related organizations to continuously improve the accuracy and effectiveness of the warning system. For example, based on feedback from residents who have received evacuation advisories, the department can review evacuation routes and improve the method of providing warning information. Also, based on feedback from related organizations, the department can review emergency response plans and strengthen collaboration systems. This allows the Warning Department to always provide a highly accurate warning system based on the latest information and feedback, ensuring the safety of residents.

[0070] The collection unit can collect weather information or sensor data provided by the Japan Meteorological Agency or observation stations in various locations. The collection unit collects, for example, weather information provided by the Japan Meteorological Agency. The weather information includes temperature, humidity, wind speed, precipitation, and the like. The collection unit can also collect sensor data provided by observation stations in various locations. The sensor data includes data from seismometers and water level gauges. For example, the collection unit can access the Japan Meteorological Agency's database to obtain the latest weather information. The collection unit can also collect data in real time from observation stations in various locations. This allows the collection unit to collect highly reliable data.

[0071] The analysis unit can analyze the collected data using machine learning or deep learning. The analysis unit, for example, analyzes the collected data using machine learning. Machine learning algorithms include neural networks and support vector machines. The analysis unit can also analyze data using deep learning. Deep learning algorithms include convolutional neural networks (CNNs) and recurrent neural networks (RNNs). For example, the analysis unit inputs collected weather data into a neural network to analyze changes in temperature and humidity. The analysis unit can also input data from a seismometer into a support vector machine to analyze the occurrence of earthquakes. This allows the analysis unit to perform highly accurate data analysis.

[0072] The prediction unit can predict the occurrence of heavy rain based on the analysis result. The prediction unit predicts the occurrence of heavy rain based on, for example, data on changes in temperature and humidity obtained by the analysis unit. The prediction unit can predict the occurrence of heavy rain by learning past heavy rain data and recognizing patterns of changes in temperature and humidity. For example, the prediction unit predicts the occurrence of heavy rain when the temperature rises sharply and the humidity becomes high. The prediction unit can also predict the occurrence of heavy rain by taking into account data on wind speed and precipitation. This allows the prediction unit to predict the occurrence of heavy rain with high accuracy.

[0073] The prediction unit can predict the occurrence of an earthquake based on the analysis results. The prediction unit predicts the occurrence of an earthquake based on, for example, seismometer data obtained by the analysis unit. The prediction unit can predict the occurrence of an earthquake by learning past earthquake data and recognizing data patterns of the seismometer. For example, the prediction unit predicts the occurrence of an earthquake when vibration data from the seismometer shows a specific pattern. The prediction unit can also predict the occurrence of an earthquake by taking into account data on the location of the earthquake and its seismic intensity. This allows the prediction unit to predict the occurrence of an earthquake with high accuracy.

[0074] The alert department can issue an evacuation advisory to residents based on the prediction results. For example, when the prediction department predicts the occurrence of heavy rain, the alert department issues an evacuation advisory to residents. The alert department can set conditions for issuing evacuation advisories based on the prediction results and quickly issue evacuation advisories to residents. For example, when the occurrence of heavy rain is predicted, the alert department issues an evacuation advisory to a specific area. In addition, the alert department can use smartphone apps, email, television, radio, etc. as a means of notifying the evacuation advisory. This allows the alert department to quickly issue evacuation advisories to residents.

[0075] The alert unit can strengthen earthquake resistance measures of the building based on the prediction result. For example, when the prediction unit predicts the occurrence of an earthquake, the alert unit strengthens the earthquake resistance measures of the building. The alert unit can set conditions for strengthening the earthquake resistance measures of the building based on the prediction result, and can quickly strengthen the earthquake resistance measures of the building. For example, when the occurrence of an earthquake is predicted, the alert unit can carry out earthquake reinforcement work on a specific building. Furthermore, the alert unit can install earthquake reinforcement materials or reinforce the structure of the building as a means of strengthening the earthquake resistance measures of the building. This allows the alert unit to quickly strengthen the earthquake resistance measures of the building.

[0076] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. For example, when the user feels anxious, the collection unit increases the frequency of data collection to provide information in real time. The collection unit uses an emotion recognition algorithm to estimate the user's emotions. The emotion recognition algorithm estimates the user's emotions using techniques such as facial expression recognition, voice analysis, and text analysis. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The collection unit can also record the user's voice and estimate the emotion using a voice analysis algorithm. Furthermore, the collection unit can analyze the user's text message and estimate the emotion using a text analysis algorithm. This allows the collection unit to adjust the timing of data collection according to the user's emotions. For example, when the user is relaxed, the frequency of data collection is kept normal. Also, when the user is facing an emergency, data collection is started immediately and information is provided quickly. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0077] The collection unit can analyze past disaster data and select an optimal data collection method. The collection unit can analyze, for example, past heavy rain data and optimize a data collection method under specific weather conditions. The collection unit can use AI to analyze past disaster data. The AI ​​learns past data and recognizes patterns for selecting an optimal data collection method. For example, the collection unit inputs past heavy rain data into the AI ​​and optimizes a data collection method under specific weather conditions. The collection unit can also analyze past earthquake data and optimize sensor placement at the time of an earthquake occurrence. For example, the collection unit inputs past earthquake data into the AI ​​and optimizes sensor placement at the time of an earthquake occurrence. Furthermore, the collection unit can analyze past flood data and optimize the installation location of a water level gauge. For example, the collection unit inputs past flood data into the AI ​​and optimizes the installation location of a water level gauge. This allows the collection unit to select an optimal data collection method based on past disaster data. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0078] The collection unit can prioritize collection of specific weather conditions and abnormal values ​​of sensors when collecting data. For example, the collection unit prioritizes collection of data from a wind speed sensor when a typhoon approaches. The collection unit can use AI to prioritize collection of specific weather conditions and abnormal values ​​of sensors. The AI ​​recognizes a pattern for detecting specific weather conditions and abnormal values ​​of sensors and collecting data preferentially. For example, the collection unit inputs data from a wind speed sensor to the AI ​​when a typhoon approaches and prioritizes collection of the data from the wind speed sensor. The collection unit can also prioritize collection of abnormal values ​​of a seismometer when an earthquake occurs. For example, the collection unit inputs data from a seismometer to the AI ​​when an earthquake occurs and prioritizes collection of abnormal values ​​from the seismometer. Furthermore, the collection unit can also prioritize collection of data from a water level gauge when heavy rain falls. For example, the collection unit inputs data from a water level gauge to the AI ​​when heavy rain falls and prioritizes collection of the data from the water level gauge. This allows the collection unit to prioritize collection of specific weather conditions and abnormal values ​​of sensors. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or may be performed without using AI.

[0079] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, when the user feels anxious, the collection unit preferentially collects weather information. The collection unit uses an emotion recognition algorithm to estimate the user's emotion. The emotion recognition algorithm estimates the user's emotion using techniques such as facial expression recognition, voice analysis, and text analysis. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The collection unit can also record the user's voice and estimate the emotion using a voice analysis algorithm. Furthermore, the collection unit can analyze the user's text message and estimate the emotion using a text analysis algorithm. This allows the collection unit to determine the priority of data to be collected according to the user's emotion. For example, when the user is relaxed, normal data collection is performed. Also, when the user is facing an emergency, abnormal values ​​of the sensor are preferentially collected. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0080] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting data. For example, when the user is in a coastal area, the collection unit preferentially collects tsunami warning data. The collection unit can use AI to collect highly relevant data in consideration of the geographical location information of the user. The AI ​​recognizes a pattern for preferentially collecting highly relevant data based on the geographical location information of the user. For example, when the user is in a coastal area, the collection unit inputs tsunami warning data to the AI ​​and preferentially collects the tsunami warning data. In addition, the collection unit can also preferentially collect landslide data when the user is in a mountainous area. For example, when the user is in a mountainous area, the collection unit inputs landslide data to the AI ​​and preferentially collects the landslide data. In addition, the collection unit can also preferentially collect flood warning data when the user is in an urban area. For example, when the user is in an urban area, the collection unit inputs flood warning data to the AI ​​and preferentially collects the flood warning data. This allows the collection unit to preferentially collect highly relevant data based on the geographical location information of the user. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or may be performed without using AI.

[0081] The collection unit can analyze the social media activity of the user at the time of data collection and collect related data. For example, when the user posts about heavy rain, the collection unit prioritizes collecting data about heavy rain. The collection unit can use AI to analyze the social media activity of the user. The AI ​​recognizes a pattern for collecting related data based on the social media activity of the user. For example, when the user posts about heavy rain, the collection unit inputs data about heavy rain into the AI ​​and prioritizes collecting data about heavy rain. In addition, when the user posts about earthquakes, the collection unit can also prioritize collecting data about earthquakes. For example, when the user posts about earthquakes, the collection unit inputs data about earthquakes into the AI ​​and prioritizes collecting data about earthquakes. In addition, the collection unit can also prioritize collecting data about floods when the user posts about floods. For example, when the user posts about floods, the collection unit inputs data about floods into the AI ​​and prioritizes collecting data about floods. This allows the collection unit to collect related data based on the social media activity of the user. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or may be performed without using AI.

[0082] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is feeling anxious, the analysis unit provides a simple and highly visible analysis result. The analysis unit uses an emotion recognition algorithm to estimate the user's emotion. The emotion recognition algorithm estimates the user's emotion using techniques such as facial expression recognition, voice analysis, and text analysis. For example, the analysis unit takes a picture of the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis algorithm. Furthermore, the analysis unit can analyze the user's text message and estimate the emotion using a text analysis algorithm. This allows the analysis unit to adjust the expression method of the analysis according to the user's emotion. For example, when the user is relaxed, a detailed analysis result is provided. Also, when the user is facing an emergency, a quickly understandable analysis result is provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can use AI to evaluate the importance of the data. The AI ​​evaluates the importance of the data based on the influence and relevance of the data. For example, the analysis unit inputs data with high importance to the AI ​​and performs a detailed analysis. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit inputs data with low importance to the AI ​​and performs a simplified analysis. Furthermore, the analysis unit can also perform an analysis with a moderate level of detail on data with medium importance. For example, the analysis unit inputs data with medium importance to the AI ​​and performs an analysis with a moderate level of detail. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI.

[0084] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a weather forecasting algorithm to weather data. The analysis unit can use AI to apply the optimal analysis algorithm depending on the category of data. The AI ​​selects the optimal analysis algorithm based on the category of data. For example, the analysis unit inputs weather data to AI and applies a weather forecasting algorithm. The analysis unit can also apply an earthquake forecasting algorithm to earthquake data. For example, the analysis unit inputs earthquake data to AI and applies an earthquake forecasting algorithm. The analysis unit can also apply a flood forecasting algorithm to flood data. For example, the analysis unit inputs flood data to AI and applies a flood forecasting algorithm. This allows the analysis unit to apply the optimal analysis algorithm depending on the category of data. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

[0085] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is feeling anxious, the analysis unit provides a short and to-the-point analysis result. The analysis unit uses an emotion recognition algorithm to estimate the user's emotion. The emotion recognition algorithm estimates the user's emotion using techniques such as facial expression recognition, voice analysis, and text analysis. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis algorithm. Furthermore, the analysis unit can analyze the user's text message and estimate the emotion using a text analysis algorithm. This allows the analysis unit to adjust the length of the analysis according to the user's emotion. For example, when the user is relaxed, a detailed analysis result is provided. Also, when the user is facing an emergency, a quickly understandable analysis result is provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0086] The analysis unit can determine the priority of the analysis based on the collection time of the data at the time of analysis. The analysis unit, for example, prioritizes the analysis of the latest data. The analysis unit can use AI to determine the priority of the analysis taking into account the collection time of the data. The AI ​​recognizes a pattern for determining the priority of the analysis based on the collection time of the data. For example, the analysis unit inputs the latest data into the AI ​​and analyzes it with priority. The analysis unit can also analyze with emphasis on the latest data while referring to past data. For example, the analysis unit inputs past data and the latest data into the AI ​​and analyzes with emphasis on the latest data. Furthermore, the analysis unit can also analyze with priority on data collected at a specific time. For example, the analysis unit inputs data collected at a specific time into the AI ​​and analyzes it with priority. This allows the analysis unit to determine the priority of the analysis based on the collection time of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0087] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, preferentially analyzes highly relevant data. The analysis unit can use AI to evaluate the relevance of data. The AI ​​evaluates the relevance of data based on the correlation coefficient or relevance score of data. For example, the analysis unit inputs highly relevant data to the AI ​​and analyzes it preferentially. The analysis unit can also postpone data with low relevance. For example, the analysis unit inputs data with low relevance to the AI ​​and postpones it. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of data. For example, the analysis unit inputs the relevance of data to the AI ​​and dynamically adjusts the order of analysis. This allows the analysis unit to adjust the order of analysis based on the relevance of data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0088] The prediction unit can estimate the user's emotion and adjust the prediction criterion based on the estimated user's emotion. For example, when the user feels anxious, the prediction unit adopts a conservative prediction criterion. The prediction unit uses an emotion recognition algorithm to estimate the user's emotion. The emotion recognition algorithm estimates the user's emotion using techniques such as facial expression recognition, voice analysis, and text analysis. For example, the prediction unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The prediction unit can also record the user's voice and estimate the emotion using a voice analysis algorithm. Furthermore, the prediction unit can analyze the user's text message and estimate the emotion using a text analysis algorithm. This allows the prediction unit to adjust the prediction criterion according to the user's emotion. For example, when the user is relaxed, a normal prediction criterion is adopted. Also, when the user is facing an emergency, a prediction criterion that can respond quickly is adopted. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0089] The prediction unit can improve the accuracy of the prediction by considering the interrelationship of data at the time of prediction. The prediction unit makes a prediction by considering, for example, the interrelationship between weather data and earthquake data. The prediction unit can use AI to evaluate the interrelationship of data. The AI ​​evaluates the interrelationship of data based on correlation analysis of data and analysis of causal relationship. For example, the prediction unit inputs the interrelationship between weather data and earthquake data into the AI ​​and makes a prediction. The prediction unit can also make a prediction by considering the interrelationship between weather data and flood data. For example, the prediction unit inputs the interrelationship between weather data and flood data into the AI ​​and makes a prediction. Furthermore, the prediction unit can also make a prediction by considering the interrelationship between earthquake data and tsunami data. For example, the prediction unit inputs the interrelationship between earthquake data and tsunami data into the AI ​​and makes a prediction. Thereby, the prediction unit can improve the accuracy of the prediction by considering the interrelationship of data. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI.

[0090] The prediction unit can make predictions taking into account the attribute information of the data provider at the time of prediction. The prediction unit, for example, emphasizes data provided by the Japan Meteorological Agency to make predictions. The prediction unit can use AI to make predictions taking into account the attribute information of the data provider. The AI ​​recognizes patterns for improving the accuracy of predictions based on attribute information such as the occupation and region of the data provider. For example, the prediction unit inputs data provided by the Japan Meteorological Agency into the AI ​​and emphasizes it to make predictions. The prediction unit can also make predictions taking into account data provided by a local observation station. For example, the prediction unit inputs data provided by a local observation station into the AI ​​and emphasizes it to make predictions. Furthermore, the prediction unit can also make predictions taking into account data provided by an international organization. For example, the prediction unit inputs data provided by an international organization into the AI ​​and emphasizes it to make predictions. As a result, the prediction unit can improve the accuracy of predictions taking into account the attribute information of the data provider. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI.

[0091] The prediction unit can estimate the user's emotion and adjust the order of displaying the prediction results based on the estimated user's emotion. For example, when the user is feeling anxious, the prediction unit displays important prediction results first. The prediction unit uses an emotion recognition algorithm to estimate the user's emotion. The emotion recognition algorithm estimates the user's emotion using techniques such as facial expression recognition, voice analysis, and text analysis. For example, the prediction unit takes a picture of the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The prediction unit can also record the user's voice and estimate the emotion using a voice analysis algorithm. Furthermore, the prediction unit can analyze the user's text message and estimate the emotion using a text analysis algorithm. This allows the prediction unit to adjust the order of displaying the prediction results according to the user's emotion. For example, when the user is relaxed, the prediction results are displayed in a normal order. Also, when the user is facing an emergency, the prediction results that can be quickly responded to are displayed first. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0092] The prediction unit can make predictions taking into account the geographical distribution of data at the time of prediction. The prediction unit, for example, makes predictions by placing emphasis on geographically close data. The prediction unit can use AI to evaluate the geographical distribution of data. The AI ​​evaluates the geographical distribution of data using techniques such as geographical cluster analysis and map display. For example, the prediction unit inputs geographically close data into the AI ​​and places emphasis on the data to make predictions. The prediction unit can also place emphasis on close data while referring to geographically distant data. For example, the prediction unit inputs geographically distant data and close data into the AI ​​and places emphasis on the close data to make predictions. Furthermore, the prediction unit can adjust the accuracy of predictions according to the geographical distribution. For example, the prediction unit inputs the geographical distribution into the AI ​​and adjusts the accuracy of predictions. As a result, the prediction unit can improve the accuracy of predictions by taking into account the geographical distribution of data. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI.

[0093] The prediction unit can improve the accuracy of the prediction by referring to the literature related to the data at the time of prediction. The prediction unit, for example, makes a prediction by referring to the latest research paper. The prediction unit can use AI to improve the accuracy of the prediction by referring to the literature related to the data. The AI ​​refers to the related literature using a literature database, citation criteria, etc. For example, the prediction unit inputs the latest research paper into the AI ​​and makes a prediction by referring to it. The prediction unit can also make a prediction by referring to past research results. For example, the prediction unit inputs past research results into the AI ​​and makes a prediction by referring to it. Furthermore, the prediction unit can also improve the accuracy of the prediction by referring to related academic literature. For example, the prediction unit inputs related academic literature into the AI ​​and improves the accuracy of the prediction by referring to it. Thereby, the prediction unit can improve the accuracy of the prediction by referring to the literature related to the data. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI.

[0094] The alert unit can estimate the user's emotion and adjust the alert display method based on the estimated user's emotion. For example, when the user is feeling anxious, the alert unit provides a simple and highly visible alert display. The alert unit uses an emotion recognition algorithm to estimate the user's emotion. The emotion recognition algorithm estimates the user's emotion using techniques such as facial expression recognition, voice analysis, and text analysis. For example, the alert unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The alert unit can also record the user's voice and estimate the emotion using a voice analysis algorithm. Furthermore, the alert unit can analyze the user's text message and estimate the emotion using a text analysis algorithm. This allows the alert unit to adjust the alert display method according to the user's emotion. For example, when the user is relaxed, a detailed alert display is provided. Also, when the user is facing an emergency, a quickly understandable alert display is provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0095] The alert unit can optimize the current alert by referring to past alert data during alert. The alert unit optimizes the current alert by referring to, for example, past heavy rain alert data. The alert unit can use AI to optimize the current alert by referring to the past alert data. The AI ​​recognizes a pattern for optimizing the current alert based on the past alert history and the alert database. For example, the alert unit inputs past heavy rain alert data into the AI ​​and optimizes the current alert by referring to it. The alert unit can also optimize the current alert by referring to past earthquake alert data. For example, the alert unit inputs past earthquake alert data into the AI ​​and optimizes the current alert by referring to it. Furthermore, the alert unit can also optimize the current alert by referring to past flood alert data. For example, the alert unit inputs past flood alert data into the AI ​​and optimizes the current alert by referring to it. This allows the alert unit to optimize the current alert by referring to the past alert data. A part or all of the above-mentioned processing in the alert unit may be performed, for example, using AI, or may be performed without using AI.

[0096] The alert unit can apply different alert methods for each data category during alert. For example, during heavy rain alert, the alert unit places emphasis on precipitation data. The alert unit can use AI to apply the optimal alert method for each data category. The AI ​​selects the optimal alert method based on categories such as weather data and earthquake data. For example, during heavy rain alert, the alert unit inputs precipitation data into the AI ​​and places emphasis on the data for alert. Furthermore, during earthquake alert, the alert unit can also place emphasis on seismometer data for alert. For example, during earthquake alert, the alert unit inputs seismometer data into the AI ​​and places emphasis on the data for alert. Furthermore, during flood alert, the alert unit can also place emphasis on water level gauge data for alert. For example, during flood alert, the alert unit inputs water level gauge data into the AI ​​and places emphasis on the data for alert. This allows the alert unit to apply the optimal alert method for each data category. A part or all of the above-mentioned processing in the alert unit may be performed using, for example, AI, or may be performed without using AI.

[0097] The alert unit can estimate the user's emotion and adjust the importance of alert based on the estimated user's emotion. For example, when the user feels anxious, the alert unit increases the importance of alert and displays it. The alert unit uses an emotion recognition algorithm to estimate the user's emotion. The emotion recognition algorithm estimates the user's emotion using techniques such as facial expression recognition, voice analysis, and text analysis. For example, the alert unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The alert unit can also record the user's voice and estimate the emotion using a voice analysis algorithm. Furthermore, the alert unit can analyze the user's text message and estimate the emotion using a text analysis algorithm. This allows the alert unit to adjust the importance of alert according to the user's emotion. For example, when the user is relaxed, the alert unit displays the alert with a normal alert importance. Also, when the user is facing an emergency, the alert unit displays the alert with the highest importance. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0098] The alert unit can analyze changes in alertness based on the data collection time during alert. The alert unit, for example, analyzes changes in alertness based on the latest data. The alert unit can use AI to analyze changes in alertness taking into account the data collection time. The AI ​​recognizes a pattern for analyzing changes in alertness based on the data collection time. For example, the alert unit inputs the latest data into the AI ​​and analyzes changes in alertness based on the latest data. The alert unit can also analyze changes in alertness with emphasis on the latest data while referring to past data. For example, the alert unit inputs past data and the latest data into the AI ​​and analyzes changes in alertness with emphasis on the latest data. Furthermore, the alert unit can also analyze changes in alertness based on data collected at a specific time. For example, the alert unit inputs data collected at a specific time into the AI ​​and analyzes changes in alertness based on the latest data. This allows the alert unit to analyze changes in alertness based on the data collection time. A part or all of the above-mentioned processing in the alert unit may be performed using, for example, an AI, or may be performed without using an AI.

[0099] The alerting unit can analyze alerting by referring to the relevant market data of the data at the time of alerting. The alerting unit, for example, analyzes changes in alerting based on the relevant market data. The alerting unit can use AI to analyze alerting by referring to the relevant market data of the data. The AI ​​refers to the relevant market data using a market database, economic indicators, etc. For example, the alerting unit inputs the relevant market data into the AI ​​and analyzes changes in alerting based on the data. The alerting unit can also analyze alerting by considering the interrelationship between the market data and the weather data. For example, the alerting unit inputs the interrelationship between the market data and the weather data into the AI ​​and analyzes alerting based on the data. Furthermore, the alerting unit can analyze alerting by considering the interrelationship between the market data and the earthquake data. For example, the alerting unit inputs the interrelationship between the market data and the earthquake data into the AI ​​and analyzes alerting based on the data. This allows the alerting unit to improve the accuracy of alerting by referring to the relevant market data of the data. A part or all of the above-mentioned processing in the alerting unit may be performed, for example, using AI, or may be performed without using AI.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The analysis unit can evaluate the reliability of the data source and prioritize analysis of highly reliable data. For example, data provided by the Japan Meteorological Agency or international organizations is evaluated as being highly reliable and is prioritized for analysis. Data provided by local observation stations may also be evaluated as being highly reliable. Furthermore, the analysis unit can take into account the data provider's past performance and the consistency of the data in order to evaluate the reliability of the data. This allows the analysis unit to perform highly accurate analysis based on highly reliable data.

[0102] The collection unit can prioritize collection of highly relevant data in consideration of the geographical location information of the user. For example, if the user is in a coastal area, data on tsunami warnings can be prioritized. If the user is in a mountainous area, data on landslides can be prioritized. Furthermore, if the user is in an urban area, data on flood warnings can be prioritized. This allows the collection unit to prioritize collection of highly relevant data based on the geographical location information of the user.

[0103] The prediction unit can improve the accuracy of the prediction by taking into account the interrelationships of data. For example, a prediction is made by taking into account the interrelationships between meteorological data and earthquake data. A prediction can also be made by taking into account the interrelationships between meteorological data and flood data. A prediction can also be made by taking into account the interrelationships between earthquake data and tsunami data. In this way, the prediction unit can improve the accuracy of the prediction by taking into account the interrelationships of data.

[0104] The alert section can optimize the current alert by referring to past alert data. For example, the current alert can be optimized by referring to past heavy rain alert data. The current alert can also be optimized by referring to past earthquake alert data. Furthermore, the current alert can also be optimized by referring to past flood alert data. In this way, the alert section can optimize the current alert by referring to past alert data.

[0105] The warning unit can apply different warning methods to each data category. For example, during a heavy rain warning, the warning can be given priority to precipitation data. During an earthquake warning, the warning can be given priority to seismometer data. Furthermore, during a flood warning, the warning can be given priority to water level gauge data. This allows the warning unit to apply the optimal warning method for each data category.

[0106] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. For example, if the user feels anxious, the frequency of data collection can be increased to provide information in real time. If the user feels relaxed, the frequency of data collection can be kept normal. Furthermore, if the user is facing an emergency, data collection can be started immediately to provide information quickly. This allows the collection unit to adjust the timing of data collection according to the user's emotions.

[0107] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, when the user feels anxious, a simple and highly visible analysis result can be provided. When the user feels relaxed, a detailed analysis result can be provided. Furthermore, when the user is facing an emergency, a quickly understandable analysis result can be provided. This allows the analysis unit to adjust the way the analysis is presented according to the user's emotions.

[0108] The prediction unit can estimate the user's emotion and adjust the prediction criterion based on the estimated user's emotion. For example, when the user feels anxious, a conservative prediction criterion can be adopted. When the user feels relaxed, a normal prediction criterion can be adopted. Furthermore, when the user is facing an emergency, a prediction criterion that allows for a quick response can be adopted. This allows the prediction unit to adjust the prediction criterion according to the user's emotion.

[0109] The alert unit can estimate the user's emotion and adjust the alert display method based on the estimated user's emotion. For example, when the user feels anxious, a simple and highly visible alert display can be provided. When the user feels relaxed, a detailed alert display can be provided. Furthermore, when the user is facing an emergency, a quickly understandable alert display can be provided. This allows the alert unit to adjust the alert display method according to the user's emotion.

[0110] The alert unit can estimate the user's emotions and adjust the importance of alerting based on the estimated user's emotions. For example, when the user feels anxious, the alerting importance can be increased and displayed. When the user feels relaxed, the alerting importance can be displayed at a normal level. Furthermore, when the user is facing an emergency, the alerting importance can be maximized and displayed. This allows the alert unit to adjust the importance of alerting according to the user's emotions.

[0111] The process flow of the second embodiment will be briefly described below.

[0112] Step 1: The collection unit collects meteorological information and sensor data. Meteorological information includes, for example, temperature, humidity, wind speed, and precipitation. Sensor data includes, for example, data from a seismometer and a water level gauge. The collection unit can collect data provided by, for example, the Japan Meteorological Agency and observation stations in various locations. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, machine learning or deep learning. Machine learning and deep learning algorithms include neural networks and support vector machines. Step 3: The prediction unit predicts the occurrence of natural disasters based on the analysis results obtained by the analysis unit. For example, the prediction unit can predict the occurrence of heavy rain from changes in temperature and humidity, or the occurrence of an earthquake from seismometer data. The prediction unit can make highly accurate predictions by learning from past data and recognizing patterns. Step 4: The warning unit performs early warning based on the prediction results obtained by the prediction unit. For example, if the occurrence of heavy rain is predicted, the warning unit can issue an evacuation advisory to residents. Also, if the occurrence of an earthquake is predicted, the warning unit can strengthen earthquake resistance measures for buildings.

[0113] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0114] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0115] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements including the above-mentioned collection unit, analysis unit, prediction unit, and alert unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects meteorological information and sensor data using the camera 42 and sensors of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the occurrence of a natural disaster based on the analysis result. The alert unit is realized, for example, by the control unit 46A of the smart device 14, and performs early warning based on the prediction result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The 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.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0124] Fig. 4 shows an example of 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.

[0125] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0129] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0131] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements including the collection unit, analysis unit, prediction unit, and alert 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 collection unit collects meteorological information and sensor data using the camera 42 and sensor of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the occurrence of a natural disaster based on the analysis result. The alert unit is realized, for example, by the control unit 46A of the smart glasses 214, and performs early warning based on the prediction result. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0136] 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.

[0137] 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 the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0139] 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 performed in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] 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 voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements including the above-mentioned collection unit, analysis unit, prediction unit, and alert unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects meteorological information and sensor data using the camera 42 and sensors of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the occurrence of a natural disaster based on the analysis result. The alert unit is realized, for example, by the control unit 46A of the headset type terminal 314, and performs early warning based on the prediction result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0152] 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.

[0153] 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 the voice according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0155] 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 performed in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0157] 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.

[0158] 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.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0160] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0161] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0162] 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 a voice indicating a user input for 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.

[0163] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0164] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0165] Each of the multiple elements including the above-mentioned collection unit, analysis unit, prediction unit, and alert unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects meteorological information and sensor data using the camera 42 and sensors of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the occurrence of a natural disaster based on the analysis result. The alert unit is realized, for example, by the control unit 46A of the robot 414, and performs early warning based on the prediction result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0167] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0169] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0171] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0173] 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.

[0174] 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 Universal Serial Bus (USB) 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.

[0175] In addition, 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 upon request from the data processing device 12.

[0176] 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.

[0177] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0178] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0179] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0180] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0182] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0184] (Appendix 1) A collection unit for collecting meteorological information or sensor data; an analysis unit that analyzes the data collected by the collection unit; a prediction unit that predicts the occurrence of a natural disaster based on the analysis result obtained by the analysis unit; a warning unit that performs warning based on the prediction result obtained by the prediction unit; Equipped A system characterized by: (Appendix 2) The collecting unit includes: Collect meteorological information or sensor data provided by the Japan Meteorological Agency or local observation stations 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze the collected data using machine learning or deep learning 2. The system of claim 1, (Appendix 4) The prediction unit is Predicting heavy rainfall based on analysis results 2. The system of claim 1, (Appendix 5) The prediction unit is Predicting earthquake occurrence based on analysis results 2. The system of claim 1, (Appendix 6) The alert unit includes: Issue evacuation advice to residents based on the prediction results. 2. The system of claim 1, (Appendix 7) The alert unit includes: Strengthening earthquake resistance measures for buildings based on prediction results 2. The system of claim 1, (Appendix 8) The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1, (Appendix 9) The collecting unit includes: Analyze past disaster data and select the most appropriate data collection method 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting data, prioritize collection of specific weather conditions or abnormal sensor values. 2. The system of claim 1, (Appendix 11) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting data, prioritize collecting data that is more relevant by taking into account the user's geographic location. 2. The system of claim 1, (Appendix 13) The collecting unit includes: When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, adjust the level of detail based on the importance of the data 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1, (Appendix 17) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, prioritize analysis based on when the data was collected 2. The system of claim 1, (Appendix 19) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 20) The prediction unit is Infer user sentiment and adjust prediction criteria based on the inferred user sentiment 2. The system of claim 1, (Appendix 21) The prediction unit is When forecasting, consider data correlations to improve the accuracy of the forecast 2. The system of claim 1, (Appendix 22) The prediction unit is When making predictions, the attribute information of the data provider is taken into account. 2. The system of claim 1, (Appendix 23) The prediction unit is Infer user sentiment and adjust the order in which prediction results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 24) The prediction unit is When forecasting, the geographical distribution of data is taken into account 2. The system of claim 1, (Appendix 25) The prediction unit is When making predictions, refer to the relevant literature for the data to improve the accuracy of the predictions. 2. The system of claim 1, (Appendix 26) The alert unit includes: To estimate a user's emotion and adjust a method of displaying alertness based on the estimated user's emotion. 2. The system of claim 1, (Appendix 27) The alert unit includes: When alerting, refer to past alert data to optimize current alerts 2. The system of claim 1, (Appendix 28) The alert unit includes: During alerting, apply different alerting techniques to different categories of data 2. The system of claim 1, (Appendix 29) The alert unit includes: To estimate a user's emotion and adjust the importance of alerting based on the estimated user's emotion. 2. The system of claim 1, (Appendix 30) The alert unit includes: During alerts, analyze alert changes based on when the data was collected 2. The system of claim 1, (Appendix 31) The alert unit includes: When an alert occurs, refer to the relevant market data to analyze the alert. 2. The system of claim 1, [Explanation of symbols]

[0185] 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 collection unit for collecting meteorological information or sensor data; an analysis unit that analyzes the data collected by the collection unit; a prediction unit that predicts the occurrence of a natural disaster based on the analysis result obtained by the analysis unit; a warning unit that performs warning based on the prediction result obtained by the prediction unit; Equipped A system characterized by:

2. The collecting unit includes: Collect meteorological information or sensor data provided by the Japan Meteorological Agency or local observation stations 2. The system of claim 1 .

3. The analysis unit is Analyze the collected data using machine learning or deep learning 2. The system of claim 1 .

4. The prediction unit is Predicting heavy rainfall based on analysis results 2. The system of claim 1 .

5. The prediction unit is Predicting earthquake occurrence based on analysis results 2. The system of claim 1 .

6. The alert unit includes: Issue evacuation advice to residents based on the prediction results.

2. The system of claim 1 .

7. The alert unit includes: Strengthening earthquake resistance measures for buildings based on prediction results 2. The system of claim 1 .

8. The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1 .

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