system

Abstract

The system according to this embodiment aims to integrate meteorological data, topographic data, and hydrological data to perform risk assessment and issue alerts quickly. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a transmission unit. The collection unit collects meteorological data, topographic data, and hydrological data. The analysis unit analyzes the data collected by the collection unit. The evaluation unit performs a risk assessment based on the data analyzed by the analysis unit. The transmission unit transmits an alert based on the risk assessed by the evaluation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, risk assessment by integrating meteorological data, terrain data, and hydrological data and quickly sending alerts have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to perform risk assessment by integrating meteorological data, terrain data, and hydrological data and quickly send alerts.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an evaluation unit, and a transmission unit. The data collection unit collects meteorological data, topographic data, and hydrological data. The analysis unit analyzes the data collected by the data collection unit. The evaluation unit performs a risk assessment based on the data analyzed by the analysis unit. The transmission unit sends out alerts based on the risks assessed by the evaluation unit. [Effects of the Invention]

[0007] The system according to this embodiment can integrate meteorological data, topographic data, and hydrological data to perform risk assessment and issue alerts quickly. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The risk assessment system according to an embodiment of the present invention is a system that integrates meteorological data, topographic data, and hydrological data, performs risk assessment using an AI algorithm, and rapidly issues alerts to high-risk areas. This risk assessment system collects meteorological data, topographic data, and hydrological data, analyzes them with an AI algorithm, and assesses flood risk. For example, meteorological data, topographic data, and hydrological data are acquired in real time from sensors and databases. Next, the collected data is analyzed with an AI algorithm to assess flood risk. The AI ​​identifies high-risk areas based on past data and current conditions. For example, it analyzes past rainfall and river water level data to predict future flood risk. Once the risk assessment is complete, the AI ​​rapidly issues alerts to high-risk areas. The alerts are notified to residents and relevant organizations via smartphone apps, email, social media, etc. This allows residents to prepare for evacuation early and relevant organizations to take rapid countermeasures. This system has advanced data analysis capabilities that integrate multiple data sources and performs real-time risk assessment and alert generation. In addition, it provides information through a user-friendly interface, allowing users to easily operate the system. The introduction of this AI agent is expected to reduce damage from floods, ensure sufficient evacuation time through advance warnings, and expedite disaster response. It will be particularly beneficial for residents and businesses in high-flood-risk areas, as well as for government agencies that need to implement disaster countermeasures quickly. This will allow risk assessment systems to take appropriate measures before flood risks increase.

[0029] The risk assessment system according to this embodiment comprises a data collection unit, an analysis unit, an evaluation unit, and a transmission unit. The data collection unit collects meteorological data, topographic data, and hydrological data. The data collection unit obtains data from, for example, the databases of the Japan Meteorological Agency, the Geospatial Information Authority of Japan, and river administrators. The data collection unit can obtain data in real time from sensors and databases. The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses, for example, an algorithm to predict risk based on rainfall and river water level data. The analysis unit uses an AI algorithm to identify high-risk areas based on past data and current conditions. The evaluation unit performs a risk assessment based on the data analyzed by the analysis unit. The evaluation unit analyzes, for example, past rainfall and river water level data to predict future flood risk. The evaluation unit uses an AI algorithm to perform a risk assessment. The transmission unit sends alerts based on the risk assessed by the evaluation unit. The transmission unit sends alerts via, for example, a smartphone app, email, or social media. The transmission unit provides information through a user-friendly interface. This enables the risk assessment system to integrate meteorological, topographic, and hydrological data, perform risk assessments using AI algorithms, and quickly issue alerts to high-risk areas.

[0030] The data collection unit collects meteorological, topographic, and hydrological data. Specifically, meteorological data includes information such as rainfall, temperature, wind speed, and humidity. This data is obtained in real time from the Japan Meteorological Agency's database. Topographic data includes topographic maps, elevation data, and land use data, and is obtained from the Geospatial Information Authority of Japan's database. Hydrological data includes river water levels, flow rates, and reservoir water levels, and is obtained from river management databases. The data collection unit can obtain this data in real time from sensors and databases. For example, it can obtain water level data from water level sensors installed in rivers and real-time rainfall data from the Japan Meteorological Agency. Topographic data may also be updated using satellite imagery and aerial data from drones. This allows the data collection unit to integrate the latest meteorological, topographic, and hydrological data and provide it to the analysis unit. Furthermore, the data collection unit performs data consistency checks and filters outliers to ensure data quality. This increases the reliability of the collected data and supports accurate analysis by the analysis unit.

[0031] The analysis unit analyzes the data collected by the collection unit. Specifically, it uses algorithms to predict risk based on rainfall and river water level data. For example, it analyzes rainfall data over time to predict future rainfall patterns. It also analyzes river water level data to assess the risk of river flooding. The analysis unit uses AI algorithms to identify high-risk areas based on past data and current conditions. The AI ​​algorithms use machine learning and deep learning to learn patterns from past disaster data and predict risk by comparing them with current data. For example, it predicts the risk of river flooding in the event of a specific rainfall pattern based on past rainfall and river water level data. It also analyzes topographic data to identify areas prone to water flow based on topographic characteristics. This allows the analysis unit to quickly and accurately analyze collected data and identify high-risk areas. Furthermore, the analysis unit provides the analysis results to the evaluation unit to support the evaluation unit's risk assessment.

[0032] The evaluation unit conducts risk assessments based on data analyzed by the analysis unit. Specifically, it analyzes past rainfall and river water level data to predict future flood risk. The evaluation unit uses AI algorithms to perform risk assessments. The AI ​​algorithms analyze current data based on patterns learned from past data and identify high-risk areas. For example, based on past rainfall data and river water level data, it assesses the risk of river flooding in the event of a specific rainfall pattern. It also analyzes topographic data to identify areas prone to water flow based on topographic characteristics. This allows the evaluation unit to identify high-risk areas and conduct risk assessments based on the collected data. Furthermore, the evaluation unit provides the risk assessment results to the dissemination unit to support the dissemination of alerts. Based on the risk assessment results, the evaluation unit provides information to enable appropriate countermeasures to be taken in high-risk areas.

[0033] The alerting unit issues alerts based on the risks assessed by the evaluation unit. Specifically, alerts are sent via smartphone apps, email, and social media. The alerting unit provides information through a user-friendly interface. For example, the smartphone app displays the current risk situation and evacuation orders in real time, prompting users to take appropriate action. It also quickly communicates risk situations and evacuation orders via email and social media. The alerting unit displays the content of alerts in an easy-to-understand manner, enabling users to respond quickly. Furthermore, the alerting unit manages the alerting history and can review past alerting status. This allows the alerting unit to issue alerts quickly and accurately based on the risks assessed by the evaluation unit, prompting users to take appropriate action. The alerting unit can collect user feedback and improve the content and method of issuing alerts. For example, it can improve the interface to display alert content more clearly based on user feedback. In addition, the alerting unit can reliably transmit information using multiple communication methods. This allows the alerting unit to issue alerts quickly and reliably to users, maximizing the effectiveness of the risk assessment system.

[0034] The data collection unit can obtain data from databases of the Japan Meteorological Agency, the Geospatial Information Authority of Japan, and river administrators. For example, the data collection unit can obtain meteorological data from the Japan Meteorological Agency's database. The data collection unit can also obtain topographic data from the Geospatial Information Authority of Japan's database. The data collection unit can also obtain hydrological data from river administrators' databases. This allows the data collection unit to collect highly reliable data. Databases include, but are not limited to, the Japan Meteorological Agency's database and the Geospatial Information Authority of Japan's database.

[0035] The analysis unit can use algorithms to predict risk based on rainfall and river water level data. For example, the analysis unit can use an algorithm to predict risk based on rainfall data. The analysis unit can also use an algorithm to predict risk based on river water level data. The analysis unit uses AI algorithms to identify high-risk areas based on past data and current conditions. This enables the analysis unit to perform a more accurate risk assessment. Examples of risk prediction algorithms include, but are not limited to, machine learning algorithms and statistical models.

[0036] The sending unit can send alerts via smartphone apps, email, and social media. For example, the sending unit can send alerts via smartphone apps. The sending unit can also send alerts via email. The sending unit can also send alerts via social media. This allows the sending unit to quickly disseminate information. Smartphone apps include, but are not limited to, notification functions and location information functions. Emails include, but are not limited to, emergency emails and regular emails.

[0037] The transmitting unit can provide information through a user-friendly interface. For example, the transmitting unit can provide an intuitively operable interface. The transmitting unit can also provide an interface that employs a visual design. The transmitting unit can also provide an interface designed to allow users to easily operate the system. This allows the transmitting unit to enable users to easily operate the system. User-friendly interfaces include, but are not limited to, intuitive operation and visual design.

[0038] The data collection unit can analyze historical weather and topographic data variations and select the optimal data collection method. For example, the data collection unit can analyze historical weather data and select a data collection method appropriate to a specific season or weather condition. The data collection unit can also analyze topographic data variations and select a data collection method appropriate to changes in topography. The data collection unit can also analyze historical hydrological data and select a data collection method appropriate to fluctuations in river water levels. This allows the data collection unit to select the optimal data collection method. Optimal data collection methods include, but are not limited to, sensor networks and remote sensing. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI.

[0039] The data collection unit can filter data based on the weather conditions and topographic characteristics of a specific region during data collection. For example, the data collection unit can collect only the necessary data based on the weather conditions of a specific region. The data collection unit can also limit the scope of data collection based on topographic characteristics. The data collection unit can also select the optimal data collection method by considering both weather conditions and topographic characteristics. This allows the data collection unit to collect only the necessary data. Filtering includes, but is not limited to, weather condition thresholds and types of topographic characteristics. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant weather data based on the user's current location. The data collection unit can also prioritize the collection of topographic data by considering the user's geographical location information. The data collection unit can also prioritize the collection of hydrological data based on the user's location information. This allows the data collection unit to prioritize the collection of highly relevant data. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or without using AI.

[0041] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze users' social media posts and collect relevant weather data. The data collection unit can also collect topographic data based on users' social media activity. The data collection unit can also analyze users' social media activity and collect hydrological data. This allows the data collection unit to collect relevant data. Social media activity includes, but is not limited to, posts and follower counts. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI.

[0042] 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 important data. The analysis unit can also perform a simplified analysis on less important data. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. This enables the analysis unit to perform efficient analysis. The importance of the data includes, but is not limited to, the scope of impact and frequency of occurrence. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a weather forecasting algorithm to meteorological data. The analysis unit can also apply a topographic analysis algorithm to topographic data. The analysis unit can also apply a hydrological analysis algorithm to hydrological data. This allows the analysis unit to perform more accurate analysis. Data categories include, but are not limited to, meteorological data, topographic data, and hydrological data. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without using AI.

[0044] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also analyze current data while referring to past data. The analysis unit can also dynamically adjust the priority of analysis based on the data collection period. This enables the analysis unit to perform efficient analysis. The data collection period includes, but is not limited to, seasons and time of day. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes the analysis of highly relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. The analysis unit can also postpone the analysis of less relevant data. This enables the analysis unit to perform efficient analysis. The relevance of the data includes, but is not limited to, correlation and causation. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI.

[0046] The assessment unit can predict current risk by referring to past risk data during risk assessment. For example, the assessment unit predicts current risk based on past risk data. The assessment unit can also predict risk fluctuations by comparing past risk data with current data. The assessment unit can also improve the accuracy of risk assessment by referring to past risk data. This allows the assessment unit to predict current risk more accurately. Past risk data includes, but is not limited to, past disaster records and risk assessment results. Some or all of the above processing in the assessment unit may be performed using, for example, AI, or not using AI.

[0047] The evaluation unit can apply different evaluation methods to each data category during risk assessment. For example, the evaluation unit can apply a meteorological risk assessment method to meteorological data. The evaluation unit can also apply a topographic risk assessment method to topographic data. The evaluation unit can also apply a hydrological risk assessment method to hydrological data. This allows the evaluation unit to perform appropriate risk assessments according to the data category. Evaluation methods include, but are not limited to, quantitative and qualitative assessments. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI.

[0048] The assessment unit can analyze changes in risk based on the timing of data collection during risk assessment. For example, the assessment unit can analyze changes in risk based on the latest data. The assessment unit can also analyze current risk by referring to past data. The assessment unit can also dynamically analyze changes in risk based on the timing of data collection. This enables the assessment unit to perform more accurate risk assessments. Changes in risk include, but are not limited to, changes over time and environmental changes. Some or all of the above processing in the assessment unit may be performed using, for example, AI, or not using AI.

[0049] The evaluation unit can analyze risk by referring to relevant market data during risk assessment. For example, the evaluation unit analyzes risk based on relevant market data. The evaluation unit can also compare market data and risk data to analyze fluctuations in risk. The evaluation unit can also improve the accuracy of risk assessment by referring to relevant market data. This improves the accuracy of risk assessment. Relevant market data includes, but is not limited to, economic indicators and trading data. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI.

[0050] The sending unit can select the optimal sending method by referring to the user's past alert history when sending an alert. For example, the sending unit selects the optimal sending method based on the user's past alert history. The sending unit can also select the optimal sending method by comparing the past alert history with the current situation. The sending unit can also adjust the frequency of alert sending by referring to the user's alert history. This allows the sending unit to select the optimal alert sending method. Past alert history includes, but is not limited to, past notification content and sending timing. Some or all of the above processing in the sending unit may be performed using, for example, AI, or not using AI.

[0051] The sending unit can customize the content of an alert based on the characteristics of a specific region when issuing an alert. For example, the sending unit can customize the content of an alert based on the weather conditions of a specific region. The sending unit can also adjust the content of an alert based on the topographical characteristics of a region. The sending unit can also customize the content of an alert based on the characteristics of the residents of a region. This allows the sending unit to issue more appropriate alerts. The characteristics of a specific region include, but are not limited to, topographical characteristics and weather conditions. Some or all of the above processing in the sending unit may be performed using, for example, AI, or not using AI.

[0052] The sending unit can select the optimal sending method when issuing an alert, taking into account the user's geographical location information. For example, the sending unit can select the optimal alert sending method based on the user's current location. The sending unit can also adjust the content of the alert, taking into account the user's geographical location information. The sending unit can also adjust the timing of the alert based on the user's location information. This allows the sending unit to select the optimal alert sending method. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the above processing in the sending unit may be performed using, for example, AI, or without using AI.

[0053] The alerting unit can analyze the user's social media activity and adjust the content of the alert when issuing it. For example, the alerting unit can analyze the user's social media posts and issue relevant alerts. The alerting unit can also adjust the content of the alert based on the user's social media activity. The alerting unit can also analyze the user's social media activity and adjust the timing of alert issuance. This allows the alerting unit to issue relevant alerts. Social media activity includes, but is not limited to, posts and follower count. Some or all of the above processing in the alerting unit may be performed using, for example, AI, or not using AI.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The evaluation unit can improve the accuracy of risk assessments by referring to the user's past behavioral history. For example, it can analyze how users have acted in response to specific risks in the past and reflect this in the current risk assessment. It can also perform more accurate risk assessments based on past evacuation actions and countermeasures. This allows the evaluation unit to perform risk assessments based on the user's behavioral patterns.

[0056] The sending unit can select the optimal sending method depending on the type of device the user is using when sending an alert. For example, it can send a push notification to users using smartphones and an email to users using PCs. It can also send in-app notifications to users using tablets. This allows the sending unit to send alerts optimized for the user's device.

[0057] The data collection unit can adjust its data acquisition method based on the user's internet connection status during data collection. For example, if the user is connected to a high-speed internet connection, it can quickly collect a large amount of data. If the user is connected to a low-speed internet connection, it can collect only the minimum necessary data. This allows the data collection unit to perform efficient data collection according to the user's internet connection status.

[0058] The analysis unit can prioritize analysis based on data reliability during the analysis process. For example, it can prioritize the analysis of highly reliable data and postpone the analysis of less reliable data. Highly reliable data includes data provided by official institutions and data with historical track records. This allows the analysis unit to perform accurate analysis based on reliable data.

[0059] The alerting unit can adjust the content of an alert by referring to the user's past alert response history when issuing an alert. For example, it can send a detailed alert to a user who has responded quickly to a particular alert in the past. Conversely, it can send a concise alert containing only essential information to a user who has responded slowly to an alert in the past. This allows the alerting unit to send the most appropriate alert based on the user's past response history.

[0060] The data collection unit can adjust its data acquisition method during data collection, taking into account the battery level of the user's device. For example, if the battery level is low, the collection unit will collect only the minimum necessary data. If the battery level is sufficient, the collection unit can also collect detailed data. This allows the collection unit to efficiently collect data according to the battery status of the user's device.

[0061] The assessment unit can adjust the risk assessment results during the risk assessment process, taking into account the user's current activity status. For example, if the user is on the move, the assessment unit can perform a rapid risk assessment and provide a concise result. If the user is at home, the assessment unit can also provide a detailed risk assessment result. If the user is facing an emergency, the assessment unit can perform an immediate risk assessment and provide the results quickly. This allows the assessment unit to provide appropriate risk assessment results that are relevant to the user's current activity status.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects meteorological data, topographic data, and hydrological data. The data collection unit obtains data from databases such as those of the Japan Meteorological Agency, the Geospatial Information Authority of Japan, and river management authorities. The data collection unit can acquire data in real time from sensors and databases. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses algorithms to predict risk based on data such as rainfall and river water levels. The analysis unit uses AI algorithms to identify high-risk areas based on historical data and current conditions. Step 3: The evaluation unit performs a risk assessment based on the data analyzed by the analysis unit. For example, the evaluation unit analyzes past rainfall and river water level data to predict future flood risks. The evaluation unit uses AI algorithms to perform the risk assessment. Step 4: The outgoing unit issues alerts based on the risks assessed by the evaluation unit. The outgoing unit issues alerts via, for example, a smartphone app, email, or social media. The outgoing unit provides information through a user-friendly interface.

[0064] (Example of form 2) The risk assessment system according to an embodiment of the present invention is a system that integrates meteorological data, topographic data, and hydrological data, performs risk assessment using an AI algorithm, and rapidly issues alerts to high-risk areas. This risk assessment system collects meteorological data, topographic data, and hydrological data, analyzes them with an AI algorithm, and assesses flood risk. For example, meteorological data, topographic data, and hydrological data are acquired in real time from sensors and databases. Next, the collected data is analyzed with an AI algorithm to assess flood risk. The AI ​​identifies high-risk areas based on past data and current conditions. For example, it analyzes past rainfall and river water level data to predict future flood risk. Once the risk assessment is complete, the AI ​​rapidly issues alerts to high-risk areas. The alerts are notified to residents and relevant organizations via smartphone apps, email, social media, etc. This allows residents to prepare for evacuation early and relevant organizations to take rapid countermeasures. This system has advanced data analysis capabilities that integrate multiple data sources and performs real-time risk assessment and alert generation. In addition, it provides information through a user-friendly interface, allowing users to easily operate the system. The introduction of this AI agent is expected to reduce damage from floods, ensure sufficient evacuation time through advance warnings, and expedite disaster response. It will be particularly beneficial for residents and businesses in high-flood-risk areas, as well as for government agencies that need to implement disaster countermeasures quickly. This will allow risk assessment systems to take appropriate measures before flood risks increase.

[0065] The risk assessment system according to this embodiment comprises a data collection unit, an analysis unit, an evaluation unit, and a transmission unit. The data collection unit collects meteorological data, topographic data, and hydrological data. The data collection unit obtains data from, for example, the databases of the Japan Meteorological Agency, the Geospatial Information Authority of Japan, and river administrators. The data collection unit can obtain data in real time from sensors and databases. The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses, for example, an algorithm to predict risk based on rainfall and river water level data. The analysis unit uses an AI algorithm to identify high-risk areas based on past data and current conditions. The evaluation unit performs a risk assessment based on the data analyzed by the analysis unit. The evaluation unit analyzes, for example, past rainfall and river water level data to predict future flood risk. The evaluation unit uses an AI algorithm to perform a risk assessment. The transmission unit sends alerts based on the risk assessed by the evaluation unit. The transmission unit sends alerts via, for example, a smartphone app, email, or social media. The transmission unit provides information through a user-friendly interface. This enables the risk assessment system to integrate meteorological, topographic, and hydrological data, perform risk assessments using AI algorithms, and quickly issue alerts to high-risk areas.

[0066] The data collection unit collects meteorological, topographic, and hydrological data. Specifically, meteorological data includes information such as rainfall, temperature, wind speed, and humidity. This data is obtained in real time from the Japan Meteorological Agency's database. Topographic data includes topographic maps, elevation data, and land use data, and is obtained from the Geospatial Information Authority of Japan's database. Hydrological data includes river water levels, flow rates, and reservoir water levels, and is obtained from river management databases. The data collection unit can obtain this data in real time from sensors and databases. For example, it can obtain water level data from water level sensors installed in rivers and real-time rainfall data from the Japan Meteorological Agency. Topographic data may also be updated using satellite imagery and aerial data from drones. This allows the data collection unit to integrate the latest meteorological, topographic, and hydrological data and provide it to the analysis unit. Furthermore, the data collection unit performs data consistency checks and filters outliers to ensure data quality. This increases the reliability of the collected data and supports accurate analysis by the analysis unit.

[0067] The analysis unit analyzes the data collected by the collection unit. Specifically, it uses algorithms to predict risk based on rainfall and river water level data. For example, it analyzes rainfall data over time to predict future rainfall patterns. It also analyzes river water level data to assess the risk of river flooding. The analysis unit uses AI algorithms to identify high-risk areas based on past data and current conditions. The AI ​​algorithms use machine learning and deep learning to learn patterns from past disaster data and predict risk by comparing them with current data. For example, it predicts the risk of river flooding in the event of a specific rainfall pattern based on past rainfall and river water level data. It also analyzes topographic data to identify areas prone to water flow based on topographic characteristics. This allows the analysis unit to quickly and accurately analyze collected data and identify high-risk areas. Furthermore, the analysis unit provides the analysis results to the evaluation unit to support the evaluation unit's risk assessment.

[0068] The evaluation unit conducts risk assessments based on data analyzed by the analysis unit. Specifically, it analyzes past rainfall and river water level data to predict future flood risk. The evaluation unit uses AI algorithms to perform risk assessments. The AI ​​algorithms analyze current data based on patterns learned from past data and identify high-risk areas. For example, based on past rainfall data and river water level data, it assesses the risk of river flooding in the event of a specific rainfall pattern. It also analyzes topographic data to identify areas prone to water flow based on topographic characteristics. This allows the evaluation unit to identify high-risk areas and conduct risk assessments based on the collected data. Furthermore, the evaluation unit provides the risk assessment results to the dissemination unit to support the dissemination of alerts. Based on the risk assessment results, the evaluation unit provides information to enable appropriate countermeasures to be taken in high-risk areas.

[0069] The alerting unit issues alerts based on the risks assessed by the evaluation unit. Specifically, alerts are sent via smartphone apps, email, and social media. The alerting unit provides information through a user-friendly interface. For example, the smartphone app displays the current risk situation and evacuation orders in real time, prompting users to take appropriate action. It also quickly communicates risk situations and evacuation orders via email and social media. The alerting unit displays the content of alerts in an easy-to-understand manner, enabling users to respond quickly. Furthermore, the alerting unit manages the alerting history and can review past alerting status. This allows the alerting unit to issue alerts quickly and accurately based on the risks assessed by the evaluation unit, prompting users to take appropriate action. The alerting unit can collect user feedback and improve the content and method of issuing alerts. For example, it can improve the interface to display alert content more clearly based on user feedback. In addition, the alerting unit can reliably transmit information using multiple communication methods. This allows the alerting unit to issue alerts quickly and reliably to users, maximizing the effectiveness of the risk assessment system.

[0070] The data collection unit can obtain data from databases of the Japan Meteorological Agency, the Geospatial Information Authority of Japan, and river administrators. For example, the data collection unit can obtain meteorological data from the Japan Meteorological Agency's database. The data collection unit can also obtain topographic data from the Geospatial Information Authority of Japan's database. The data collection unit can also obtain hydrological data from river administrators' databases. This allows the data collection unit to collect highly reliable data. Databases include, but are not limited to, the Japan Meteorological Agency's database and the Geospatial Information Authority of Japan's database.

[0071] The analysis unit can use algorithms to predict risk based on rainfall and river water level data. For example, the analysis unit can use an algorithm to predict risk based on rainfall data. The analysis unit can also use an algorithm to predict risk based on river water level data. The analysis unit uses AI algorithms to identify high-risk areas based on past data and current conditions. This enables the analysis unit to perform a more accurate risk assessment. Examples of risk prediction algorithms include, but are not limited to, machine learning algorithms and statistical models.

[0072] The sending unit can send alerts via smartphone apps, email, and social media. For example, the sending unit can send alerts via smartphone apps. The sending unit can also send alerts via email. The sending unit can also send alerts via social media. This allows the sending unit to quickly disseminate information. Smartphone apps include, but are not limited to, notification functions and location information functions. Emails include, but are not limited to, emergency emails and regular emails.

[0073] The transmitting unit can provide information through a user-friendly interface. For example, the transmitting unit can provide an intuitively operable interface. The transmitting unit can also provide an interface that employs a visual design. The transmitting unit can also provide an interface designed to allow users to easily operate the system. This allows the transmitting unit to enable users to easily operate the system. User-friendly interfaces include, but are not limited to, intuitive operation and visual design.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can increase the frequency of data collection and provide information in real time. If the user is relaxed, the data collection unit can also decrease the frequency of data collection and provide only the necessary information. If the user is facing an emergency, the data collection unit can immediately start data collection and provide information quickly. This allows the data collection unit to collect data at a more appropriate time. User emotions are estimated using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI.

[0075] The data collection unit can analyze historical weather and topographic data variations and select the optimal data collection method. For example, the data collection unit can analyze historical weather data and select a data collection method appropriate to a specific season or weather condition. The data collection unit can also analyze topographic data variations and select a data collection method appropriate to changes in topography. The data collection unit can also analyze historical hydrological data and select a data collection method appropriate to fluctuations in river water levels. This allows the data collection unit to select the optimal data collection method. Optimal data collection methods include, but are not limited to, sensor networks and remote sensing. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI.

[0076] The data collection unit can filter data based on the weather conditions and topographic characteristics of a specific region during data collection. For example, the data collection unit can collect only the necessary data based on the weather conditions of a specific region. The data collection unit can also limit the scope of data collection based on topographic characteristics. The data collection unit can also select the optimal data collection method by considering both weather conditions and topographic characteristics. This allows the data collection unit to collect only the necessary data. Filtering includes, but is not limited to, weather condition thresholds and types of topographic characteristics. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI.

[0077] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting important data. If the user is relaxed, the data collection unit may collect only the necessary data. If the user is facing an emergency, the data collection unit may also prioritize collecting the most important data. This allows the data collection unit to prioritize the collection of important data. Data prioritization includes, but is not limited to, urgency and importance. User emotions are estimated using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant weather data based on the user's current location. The data collection unit can also prioritize the collection of topographic data by considering the user's geographical location information. The data collection unit can also prioritize the collection of hydrological data based on the user's location information. This allows the data collection unit to prioritize the collection of highly relevant data. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or without using AI.

[0079] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze users' social media posts and collect relevant weather data. The data collection unit can also collect topographic data based on users' social media activity. The data collection unit can also analyze users' social media activity and collect hydrological data. This allows the data collection unit to collect relevant data. Social media activity includes, but is not limited to, posts and follower counts. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit provides simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. If the user is facing an emergency, the analysis unit can also provide analysis results quickly. This allows the analysis unit to provide more appropriate analysis results. Presentation methods of the analysis include, but are not limited to, graph displays and text displays. Estimation of the user's emotions is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI.

[0081] 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 important data. The analysis unit can also perform a simplified analysis on less important data. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. This enables the analysis unit to perform efficient analysis. The importance of the data includes, but is not limited to, the scope of impact and frequency of occurrence. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI.

[0082] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a weather forecasting algorithm to meteorological data. The analysis unit can also apply a topographic analysis algorithm to topographic data. The analysis unit can also apply a hydrological analysis algorithm to hydrological data. This allows the analysis unit to perform more accurate analysis. Data categories include, but are not limited to, meteorological data, topographic data, and hydrological data. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without using AI.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. If the user is facing an emergency, the analysis unit can provide a rapid analysis. This allows the analysis unit to provide more appropriate analysis results. The length of the analysis includes, but is not limited to, the user's level of interest and the complexity of the data. The estimation of the user's emotions is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI.

[0084] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also analyze current data while referring to past data. The analysis unit can also dynamically adjust the priority of analysis based on the data collection period. This enables the analysis unit to perform efficient analysis. The data collection period includes, but is not limited to, seasons and time of day. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes the analysis of highly relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. The analysis unit can also postpone the analysis of less relevant data. This enables the analysis unit to perform efficient analysis. The relevance of the data includes, but is not limited to, correlation and causation. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI.

[0086] The evaluation unit can estimate the user's emotions and adjust the risk assessment criteria based on the estimated user emotions. For example, if the user is feeling anxious, the evaluation unit may tighten the risk assessment criteria. If the user is relaxed, the evaluation unit may also loosen the risk assessment criteria. The evaluation unit can also perform a rapid risk assessment if the user is facing an emergency. This allows the evaluation unit to perform a more appropriate risk assessment. Risk assessment criteria include, but are not limited to, the type of risk and the scale of the assessment. User emotions are estimated using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI.

[0087] The assessment unit can predict current risk by referring to past risk data during risk assessment. For example, the assessment unit predicts current risk based on past risk data. The assessment unit can also predict risk fluctuations by comparing past risk data with current data. The assessment unit can also improve the accuracy of risk assessment by referring to past risk data. This allows the assessment unit to predict current risk more accurately. Past risk data includes, but is not limited to, past disaster records and risk assessment results. Some or all of the above processing in the assessment unit may be performed using, for example, AI, or not using AI.

[0088] The evaluation unit can apply different evaluation methods to each data category during risk assessment. For example, the evaluation unit can apply a meteorological risk assessment method to meteorological data. The evaluation unit can also apply a topographic risk assessment method to topographic data. The evaluation unit can also apply a hydrological risk assessment method to hydrological data. This allows the evaluation unit to perform appropriate risk assessments according to the data category. Evaluation methods include, but are not limited to, quantitative and qualitative assessments. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI.

[0089] The evaluation unit can estimate the user's emotions and adjust the importance of the risk assessment based on the estimated user emotions. For example, if the user is feeling anxious, the evaluation unit can increase the importance of the risk assessment. If the user is relaxed, the evaluation unit can also decrease the importance of the risk assessment. The evaluation unit can also perform a rapid risk assessment if the user is facing an emergency. This allows the evaluation unit to perform a more appropriate risk assessment. The importance of the risk assessment includes, but is not limited to, the scope of impact and frequency of occurrence. The estimation of the user's emotions is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI.

[0090] The assessment unit can analyze changes in risk based on the timing of data collection during risk assessment. For example, the assessment unit can analyze changes in risk based on the latest data. The assessment unit can also analyze current risk by referring to past data. The assessment unit can also dynamically analyze changes in risk based on the timing of data collection. This enables the assessment unit to perform more accurate risk assessments. Changes in risk include, but are not limited to, changes over time and environmental changes. Some or all of the above processing in the assessment unit may be performed using, for example, AI, or not using AI.

[0091] The evaluation unit can analyze risk by referring to relevant market data during risk assessment. For example, the evaluation unit analyzes risk based on relevant market data. The evaluation unit can also compare market data and risk data to analyze fluctuations in risk. The evaluation unit can also improve the accuracy of risk assessment by referring to relevant market data. This improves the accuracy of risk assessment. Relevant market data includes, but is not limited to, economic indicators and trading data. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI.

[0092] The alerting unit can estimate the user's emotions and adjust the alert delivery method based on the estimated emotions. For example, if the user is feeling anxious, the alerting unit may send a detailed alert. If the user is relaxed, the alerting unit may send a concise alert. If the user is facing an emergency, the alerting unit may send a rapid alert. This allows the alerting unit to send more appropriate alerts. The method of sending alerts includes, but is not limited to, notification methods and timing. The estimation of the user's emotions is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the alerting unit may be performed using, for example, AI or not using AI.

[0093] The sending unit can select the optimal sending method by referring to the user's past alert history when sending an alert. For example, the sending unit selects the optimal sending method based on the user's past alert history. The sending unit can also select the optimal sending method by comparing the past alert history with the current situation. The sending unit can also adjust the frequency of alert sending by referring to the user's alert history. This allows the sending unit to select the optimal alert sending method. Past alert history includes, but is not limited to, past notification content and sending timing. Some or all of the above processing in the sending unit may be performed using, for example, AI, or not using AI.

[0094] The sending unit can customize the content of an alert based on the characteristics of a specific region when issuing an alert. For example, the sending unit can customize the content of an alert based on the weather conditions of a specific region. The sending unit can also adjust the content of an alert based on the topographical characteristics of a region. The sending unit can also customize the content of an alert based on the characteristics of the residents of a region. This allows the sending unit to issue more appropriate alerts. The characteristics of a specific region include, but are not limited to, topographical characteristics and weather conditions. Some or all of the above processing in the sending unit may be performed using, for example, AI, or not using AI.

[0095] The sending unit can estimate the user's emotions and determine the priority of alerts based on the estimated emotions. For example, if the user is feeling anxious, the sending unit will prioritize important alerts. If the user is relaxed, the sending unit can also send only necessary alerts. If the user is facing an emergency, the sending unit can immediately send the most important alerts. This allows the sending unit to prioritize important alerts. Alert prioritization includes, but is not limited to, urgency and importance. User emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sending unit may be performed using AI or not using AI.

[0096] The sending unit can select the optimal sending method when issuing an alert, taking into account the user's geographical location information. For example, the sending unit can select the optimal alert sending method based on the user's current location. The sending unit can also adjust the content of the alert, taking into account the user's geographical location information. The sending unit can also adjust the timing of the alert based on the user's location information. This allows the sending unit to select the optimal alert sending method. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the above processing in the sending unit may be performed using, for example, AI, or without using AI.

[0097] The alerting unit can analyze the user's social media activity and adjust the content of the alert when issuing it. For example, the alerting unit can analyze the user's social media posts and issue relevant alerts. The alerting unit can also adjust the content of the alert based on the user's social media activity. The alerting unit can also analyze the user's social media activity and adjust the timing of alert issuance. This allows the alerting unit to issue relevant alerts. Social media activity includes, but is not limited to, posts and follower count. Some or all of the above processing in the alerting unit may be performed using, for example, AI, or not using AI.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will perform a more accurate analysis and provide more detailed information. If the user is relaxed, the analysis unit can perform a minimal analysis and provide concise information. If the user is facing an emergency, the analysis unit can perform a rapid analysis and provide results immediately. In this way, the analysis unit can provide appropriate analysis results that are tailored to the user's emotions.

[0100] The evaluation unit can improve the accuracy of risk assessments by referring to the user's past behavioral history. For example, it can analyze how users have acted in response to specific risks in the past and reflect this in the current risk assessment. It can also perform more accurate risk assessments based on past evacuation actions and countermeasures. This allows the evaluation unit to perform risk assessments based on the user's behavioral patterns.

[0101] The sending unit can select the optimal sending method depending on the type of device the user is using when sending an alert. For example, it can send a push notification to users using smartphones and an email to users using PCs. It can also send in-app notifications to users using tablets. This allows the sending unit to send alerts optimized for the user's device.

[0102] The data collection unit can adjust its data acquisition method based on the user's internet connection status during data collection. For example, if the user is connected to a high-speed internet connection, it can quickly collect a large amount of data. If the user is connected to a low-speed internet connection, it can collect only the minimum necessary data. This allows the data collection unit to perform efficient data collection according to the user's internet connection status.

[0103] The analysis unit can prioritize analysis based on data reliability during the analysis process. For example, it can prioritize the analysis of highly reliable data and postpone the analysis of less reliable data. Highly reliable data includes data provided by official institutions and data with historical track records. This allows the analysis unit to perform accurate analysis based on reliable data.

[0104] The assessment unit can estimate the user's emotions and customize the risk assessment results based on those emotions. For example, if the user is feeling anxious, the assessment unit can provide a detailed risk assessment result to reassure them. If the user is relaxed, the assessment unit can also provide a concise risk assessment result. If the user is facing an emergency, the assessment unit can quickly perform a risk assessment and provide the results immediately. This allows the assessment unit to provide appropriate risk assessment results that are tailored to the user's emotions.

[0105] The alerting unit can adjust the content of an alert by referring to the user's past alert response history when issuing an alert. For example, it can send a detailed alert to a user who has responded quickly to a particular alert in the past. Conversely, it can send a concise alert containing only essential information to a user who has responded slowly to an alert in the past. This allows the alerting unit to send the most appropriate alert based on the user's past response history.

[0106] The data collection unit can adjust its data acquisition method during data collection, taking into account the battery level of the user's device. For example, if the battery level is low, the collection unit will collect only the minimum necessary data. If the battery level is sufficient, the collection unit can also collect detailed data. This allows the collection unit to efficiently collect data according to the battery status of the user's device.

[0107] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on those estimated emotions. For example, if the user is feeling anxious, the analysis unit can display the results using visually easy-to-understand graphs and charts. If the user is relaxed, the results can also be displayed in detailed text format. If the user is facing an emergency, the analysis unit can also highlight only the essential points in a concise manner. This allows the analysis unit to display appropriate analysis results that match the user's emotions.

[0108] The assessment unit can adjust the risk assessment results during the risk assessment process, taking into account the user's current activity status. For example, if the user is on the move, the assessment unit can perform a rapid risk assessment and provide a concise result. If the user is at home, the assessment unit can also provide a detailed risk assessment result. If the user is facing an emergency, the assessment unit can perform an immediate risk assessment and provide the results quickly. This allows the assessment unit to provide appropriate risk assessment results that are relevant to the user's current activity status.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects meteorological data, topographic data, and hydrological data. The data collection unit obtains data from databases such as those of the Japan Meteorological Agency, the Geospatial Information Authority of Japan, and river management authorities. The data collection unit can acquire data in real time from sensors and databases. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses algorithms to predict risk based on data such as rainfall and river water levels. The analysis unit uses AI algorithms to identify high-risk areas based on historical data and current conditions. Step 3: The evaluation unit performs a risk assessment based on the data analyzed by the analysis unit. For example, the evaluation unit analyzes past rainfall and river water level data to predict future flood risks. The evaluation unit uses AI algorithms to perform the risk assessment. Step 4: The outgoing unit issues alerts based on the risks assessed by the evaluation unit. The outgoing unit issues alerts via, for example, a smartphone app, email, or social media. The outgoing unit provides information through a user-friendly interface.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and transmission unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit acquires weather data, topographic data, and hydrological data in real time from the sensors and database of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using an AI algorithm. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs a risk assessment based on the analyzed data. The transmission unit is implemented in the control unit 46A of the smart device 14 and sends an alert based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and transmission unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit acquires weather data, topographic data, and hydrological data in real time from the sensors and database of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using an AI algorithm. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs a risk assessment based on the analyzed data. The transmission unit is implemented in the control unit 46A of the smart glasses 214 and sends an alert based on the evaluation result. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and transmission unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit acquires weather data, topographic data, and hydrological data in real time from the sensors and database of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using an AI algorithm. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs a risk assessment based on the analyzed data. The transmission unit is implemented in the control unit 46A of the headset terminal 314 and sends an alert based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and transmission unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit acquires weather data, topographic data, and hydrological data in real time from the robot 414's sensors and database. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using an AI algorithm. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs a risk assessment based on the analyzed data. The transmission unit is implemented in the control unit 46A of the robot 414 and sends an alert based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A data collection unit that collects meteorological data, topographic data, and hydrological data, An analysis unit analyzes the data collected by the aforementioned collection unit, An evaluation unit that performs a risk assessment based on the data analyzed by the analysis unit, The system includes a transmitting unit that issues alerts based on the risks assessed by the aforementioned evaluation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Data is obtained from databases of the Japan Meteorological Agency, the Geospatial Information Authority of Japan, and river administrators. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The algorithm predicts risk based on rainfall and river water level data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The transmitting unit is Alerts are sent via smartphone apps, email, and social media. The system described in Appendix 1, characterized by the features described herein. (Note 5) The transmitting unit is Provides information through a user-friendly interface. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze past weather and topographic data variations to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, filtering is performed based on the weather conditions and topographical characteristics of a specific region. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit, We estimate user sentiment and adjust risk assessment criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, When assessing risk, historical risk data is used to predict current risk. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, When assessing risk, different assessment methods are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, The system estimates user sentiment and adjusts the importance of risk assessment based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, When assessing risk, analyze how risk changes based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, When assessing risk, analyze the risk by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The transmitting unit is It estimates the user's emotions and adjusts how alerts are sent based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The transmitting unit is When an alert is issued, the system selects the optimal method of notification by referring to the user's past alert history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The transmitting unit is When an alert is issued, customize the content of the alert based on the characteristics of a specific region. The system described in Appendix 1, characterized by the features described herein. (Note 27) The transmitting unit is It estimates the user's emotions and determines the priority of alerts based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The transmitting unit is When issuing an alert, the system selects the optimal method of notification by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The transmitting unit is When an alert is issued, the system analyzes the user's social media activity and adjusts the content of the alert accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects meteorological data, topographic data, and hydrological data, An analysis unit analyzes the data collected by the aforementioned collection unit, An evaluation unit that performs a risk assessment based on the data analyzed by the analysis unit, The system includes a transmitting unit that issues alerts based on the risks assessed by the aforementioned evaluation unit. A system characterized by the following features.

2. The aforementioned collection unit is Data is obtained from databases of the Japan Meteorological Agency, the Geospatial Information Authority of Japan, and river administrators. The system according to feature 1.

3. The aforementioned analysis unit, The algorithm predicts risk based on rainfall and river water level data. The system according to feature 1.

4. The transmitting unit is Alerts are sent via smartphone apps, email, and social media. The system according to feature 1.

5. The transmitting unit is Provides information through a user-friendly interface. The system according to feature 1.

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

7. The aforementioned collection unit is Analyze past weather and topographic data variations to select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is During data collection, filtering is performed based on the weather conditions and topographical characteristics of a specific region. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system according to feature 1.

Images