System

Abstract

An object of a system according to an embodiment is to provide an evacuation route by quickly and appropriately raising a breakwater at the time of an earthquake or a tsunami.SOLUTION: A system includes a collection part, an analysis part, an elevation part, and a provision part. The collection unit collects data of an earthquake or a tsunami. The analysis unit analyzes the data collected by the collection unit. The raising unit raises the breakwater based on the analysis result obtained by the analysis unit. The providing unit determines an appropriate evacuation route from the latest disaster information and an evacuation route database, and notifies the residents of the evacuation route.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to quickly and appropriately raise breakwaters and provide evacuation routes in the event of an earthquake or tsunami.

[0005] The system according to the embodiment aims to quickly and appropriately raise breakwaters and provide evacuation routes in the event of an earthquake or tsunami. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an elevation unit, and a provision unit. The collection unit collects earthquake and tsunami data. The analysis unit analyzes the data collected by the collection unit. The elevation unit raises the breakwater based on the analysis results obtained by the analysis unit. The provision unit determines the appropriate evacuation route based on the latest disaster information and an evacuation route database, and notifies residents. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and appropriately raise breakwaters and provide evacuation routes in the event of an earthquake or tsunami. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A disaster prevention system according to an embodiment of the present invention collects and analyzes data on earthquakes and tsunamis, raises breakwaters, and provides optimal evacuation routes. The disaster prevention system collects and analyzes data on earthquakes and tsunamis, raises breakwaters, and provides optimal evacuation routes, thereby enabling rapid and accurate disaster prevention measures. For example, the disaster prevention system collects data on earthquakes and tsunamis. For example, the disaster prevention system collects data such as the seismic intensity of earthquakes, the height of tsunamis, and the time of occurrence. The disaster prevention system then analyzes the collected data. For example, the disaster prevention system performs analysis using statistical analysis of the data or machine learning algorithms. The disaster prevention system then raises breakwaters based on the analysis results. For example, the disaster prevention system adjusts the height of the breakwaters depending on the scale of the earthquake or tsunami. The disaster prevention system then determines an appropriate evacuation route based on the latest disaster information and an evacuation route database, and notifies residents. For example, the disaster prevention system collects weather data, earthquake data, traffic information, and the like, and uses an algorithm to determine the optimal evacuation route. This allows the disaster prevention system to protect people's lives and property from natural disasters such as earthquakes and tsunamis. This enables the disaster prevention system to respond quickly and accurately, minimizing damage during disasters. For example, the disaster prevention system can analyze earthquake and tsunami data in real time and automatically raise breakwaters to the required height. The disaster prevention system can also collect weather data, earthquake data, traffic information, and other data, and use algorithms to determine the optimal evacuation route, providing residents with a quick and safe evacuation route.

[0029] The disaster prevention system according to the embodiment includes a collection unit, an analysis unit, an elevation unit, and a provision unit. The collection unit collects earthquake and tsunami data. The earthquake and tsunami data includes, for example, earthquake seismic intensity, tsunami height, and occurrence time, but is not limited to these examples. For example, the collection unit measures the earthquake seismic intensity using a sensor and collects the data. The collection unit can also use a tsunami meter to measure the tsunami height. The collection unit can also assign a timestamp to record the occurrence time. For example, the collection unit measures the earthquake seismic intensity in real time and collects the data. The collection unit can also measure the tsunami height in real time and collect the data. The collection unit can also record the occurrence time in real time and collect the data. The analysis unit analyzes the data collected by the collection unit. For example, the analysis is performed by applying statistical analysis of the data or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit performs statistical analysis of the data to analyze the occurrence pattern of earthquakes and tsunamis. The analysis unit can also apply a machine learning algorithm to predict earthquakes and tsunamis. The analysis unit can also cluster data to extract characteristics of earthquakes and tsunamis. For example, the analysis unit performs statistical analysis of data to analyze patterns of earthquake and tsunami occurrence. The analysis unit can also apply a machine learning algorithm to predict earthquakes and tsunamis. The analysis unit can also cluster data to extract characteristics of earthquakes and tsunamis. The raising unit raises the breakwater based on the analysis results obtained by the analysis unit. The breakwater is raised based on, for example, a raising speed, a raising height, and a control method, but is not limited to these examples. For example, the raising unit adjusts the raising height of the breakwater depending on the scale of the earthquake or tsunami. The raising unit can also adjust the raising speed. The raising unit can also optimize the control method of the breakwater. For example, the raising unit adjusts the raising height of the breakwater depending on the scale of the earthquake or tsunami. The raising unit can also adjust the raising speed. The raising unit can also optimize the control method of the breakwater. The information provider will determine the appropriate evacuation route based on the latest disaster information and evacuation route database and notify residents.The determination of an evacuation route is based on, for example, the safety and distance of the evacuation route, the capacity of the evacuation shelter, etc., but is not limited to such examples. For example, the providing unit collects weather data, earthquake data, traffic information, etc., and determines an optimal evacuation route. The providing unit can also evaluate the safety of the evacuation route. The providing unit can also determine an evacuation route taking into account the capacity of the evacuation shelter. For example, the providing unit collects weather data, earthquake data, traffic information, etc., and determines an optimal evacuation route. The providing unit can also evaluate the safety of the evacuation route. The providing unit can also determine an evacuation route taking into account the capacity of the evacuation shelter. As a result, the disaster prevention system according to the embodiment collects and analyzes earthquake and tsunami data, raises breakwaters, and provides optimal evacuation routes, thereby enabling rapid and accurate disaster prevention measures.

[0030] When collecting earthquake and tsunami data, the collection unit can improve the collection method by referring to past disaster data. For example, the collection unit refers to past earthquake data to identify the epicenter and predict the seismic intensity. The collection unit can also refer to past tsunami data to predict the arrival time and height of the tsunami. The collection unit can also determine optimal sensor placement and data collection points based on past disaster data. By referring to past disaster data, the data collection method can be optimized, enabling highly accurate data collection. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input past disaster data into the generation AI and have the generation AI optimize the data collection method.

[0031] The collection unit can change the collection range in real time based on the earthquake epicenter or tsunami source during data collection. For example, if the earthquake epicenter is identified, the collection unit can strengthen data collection in the surrounding area. Furthermore, if the tsunami source is identified, the collection unit can also collect data along its direction of travel. The collection unit can also expand or narrow the collection range depending on the scale of the earthquake or tsunami. This enables more effective data collection by dynamically changing the collection range based on the earthquake epicenter or tsunami source. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input earthquake epicenter data to the generation AI and cause the generation AI to change the collection range.

[0032] The collection unit can integrate data from multiple sensors during data collection to improve collection accuracy. The collection unit integrates data from, for example, seismometers, tsunami meters, and weather sensors, and performs comprehensive analysis. The collection unit can also collect data from multiple sensors in real time to provide highly accurate information. The collection unit can also compare and complement data between sensors and eliminate outliers and errors. By integrating data from multiple sensors, collection accuracy can be improved and more accurate information can be obtained. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input data from multiple sensors into a generation AI and have the generation AI integrate the data.

[0033] When collecting data, the collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration. For example, the collection unit prioritizes collecting data around the epicenter of an earthquake. The collection unit can also prioritize collecting data in areas along the direction of tsunami travel. The collection unit can also prioritize collecting data in areas where heavy rain is predicted. In this way, highly relevant data can be collected preferentially by taking geographical location information into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0034] The collection unit can analyze information from social media and collect related data when collecting data. For example, the collection unit analyzes posts on social media to understand the occurrence of earthquakes and tsunamis. The collection unit can also identify the damage situation based on location information on social media. The collection unit can also collect real-time information on social media and take prompt action. This allows related data to be collected in real time by analyzing information from social media. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media data into a generation AI and have the generation AI collect related data.

[0035] The collection unit can customize the collection method by reflecting past feedback when collecting data. The collection unit can improve the data collection method based on feedback from past disasters, for example. The collection unit can also adjust the collection range and frequency by reflecting feedback from users. The collection unit can also analyze past data collection results and introduce the optimal collection method. In this way, by reflecting past feedback, the collection method can be customized, enabling more effective data collection. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input past feedback data into the generation AI and have the generation AI customize the collection method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the scale of the earthquake or tsunami. For example, in the case of a large earthquake, the analysis unit performs a detailed analysis and provides a damage forecast. In addition, in the case of a small earthquake, the analysis unit can perform a simple analysis and provide only the necessary information. The analysis unit can also use detailed analysis or simple analysis depending on the scale of the tsunami. This allows for more effective analysis by adjusting the level of detail of the analysis based on the scale of the earthquake or tsunami. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input earthquake and tsunami scale data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the disaster category. For example, in the case of an earthquake, the analysis unit applies a seismic wave analysis algorithm. In addition, in the case of a tsunami, the analysis unit can also apply a tsunami progress prediction algorithm. In addition, in the case of heavy rain, the analysis unit can also apply a rainfall and river water level prediction algorithm. In this way, applying different analysis algorithms depending on the disaster category enables more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input disaster category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. The analysis unit can, for example, improve the accuracy of the current earthquake analysis based on past earthquake analysis results. The analysis unit can also improve the accuracy of the current tsunami analysis based on past tsunami analysis results. The analysis unit can also improve the accuracy of the current heavy rain analysis based on past heavy rain analysis results. In this way, by referring to past analysis results, the accuracy of the analysis is improved and more accurate information can be obtained. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the time of disaster occurrence. For example, the analysis unit prioritizes analysis of the most important information immediately after an earthquake occurs. The analysis unit can also prioritize analysis of tsunami progress predictions immediately after a tsunami occurs. The analysis unit can also prioritize analysis of rainfall and river water level predictions when heavy rain is predicted. This enables rapid and effective analysis by determining the priority of analysis based on the time of disaster occurrence. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of disaster occurrence into the generation AI and have the generation AI determine the priority of analysis.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of disasters. For example, if an earthquake and a tsunami are related, the analysis unit can perform an earthquake analysis followed by a tsunami analysis. Furthermore, if heavy rain and river flooding are related, the analysis unit can also perform a heavy rain analysis followed by a river water level analysis. Furthermore, if multiple disasters occur simultaneously, the analysis unit can prioritize the analysis of disasters that are more closely related. This allows for more effective analysis by adjusting the order of analysis based on the relevance of disasters. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input disaster relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results in simple language to a user with little technical expertise. The analysis unit can also provide analysis results using detailed technical terminology to a user with technical expertise. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0042] The raising unit can dynamically change the raising height of the breakwater based on the scale of the earthquake or tsunami when raising the breakwater. For example, in the case of a large earthquake, the raising unit can maximize the raising height of the breakwater. In addition, in the case of a small earthquake, the raising unit can also minimize the raising height of the breakwater. The raising unit can also adjust the raising height of the breakwater according to the scale of the tsunami. This allows for more effective raising of the breakwater by dynamically changing the raising height based on the scale of the earthquake or tsunami. Some or all of the above-mentioned processing in the raising unit may be performed using, or without, AI, for example. For example, the raising unit can input earthquake or tsunami scale data into the generation AI and have the generation AI adjust the raising height.

[0043] When raising the breakwater, the ascent unit can optimize the ascent method taking into account surrounding buildings and terrain. For example, the ascent unit adjusts the ascent height of the breakwater to match the height of the surrounding buildings. The ascent unit can also optimize the ascent method of the breakwater according to the slope of the terrain. The ascent unit can also select an optimal ascent pattern taking into account surrounding buildings and terrain. This allows for more effective ascent of the breakwater by taking into account the surrounding buildings and terrain. Some or all of the above-described processing in the ascent unit may be performed using, or without, AI, for example. For example, the ascent unit can input data on surrounding buildings and terrain into the generation AI and have the generation AI execute optimization of the ascent method.

[0044] The ascent unit can improve the accuracy of ascent by integrating data from multiple sensors when raising the breakwater. The ascent unit can integrate data from, for example, seismometers, tsunami sensors, and weather sensors to improve the accuracy of ascent. The ascent unit can also collect data from multiple sensors in real time to improve the accuracy of ascent. The ascent unit can also compare and complement data between sensors to eliminate outliers and errors. By integrating data from multiple sensors, the accuracy of ascent is improved, enabling more accurate breakwater raising. Some or all of the above-described processing in the ascent unit may be performed using, or without, AI. For example, the ascent unit can input data from multiple sensors into a generation AI and have the generation AI integrate the data.

[0045] The raising unit can dynamically change the raising range by taking into account geographical location information when raising the breakwater. For example, the raising unit can prioritize raising breakwaters in areas close to the earthquake epicenter. The raising unit can also prioritize raising breakwaters in areas along the direction of tsunami travel. The raising unit can also determine the optimal raising range based on geographical location information. This allows for more effective breakwater raising by taking geographical location information into consideration. Some or all of the above-mentioned processing in the raising unit can be performed using, for example, AI, or can be performed without using AI. For example, the raising unit can input geographical location information data into the generation AI and have the generation AI change the raising range.

[0046] The raising unit can analyze information from social media and reflect relevant data when raising the breakwater. For example, the raising unit can analyze posts on social media to grasp the occurrence status of earthquakes and tsunamis. The raising unit can also identify the damage status based on location information on social media. The raising unit can also collect real-time information on social media and take prompt action. This allows for analyzing information from social media to reflect relevant data in real time, enabling prompt action. Some or all of the above-mentioned processing in the raising unit may be performed using, for example, AI, or may be performed without using AI. For example, the raising unit can input social media data into the generation AI and have the generation AI reflect the relevant data.

[0047] The ascent unit can customize the ascent method by reflecting past feedback when raising the breakwater. The ascent unit can improve the ascent method of the breakwater, for example, based on feedback from past disasters. The ascent unit can also adjust the ascent range and speed by reflecting feedback from the user. The ascent unit can also analyze past data collection results and introduce the optimal ascent method. This allows the ascent method to be customized by reflecting past feedback, enabling more effective ascent of the breakwater. Some or all of the above-mentioned processing in the ascent unit may be performed, for example, using AI, or may be performed without using AI. For example, the ascent unit can input past feedback data into the generation AI and have the generation AI customize the ascent method.

[0048] The providing unit can adjust the level of detail of the evacuation notification based on the scale of the disaster. For example, in the case of a large-scale earthquake, the providing unit can provide a detailed evacuation notification. Furthermore, in the case of a small-scale earthquake, the providing unit can also provide a simple evacuation notification. Furthermore, the providing unit can selectively use a detailed evacuation notification or a simple evacuation notification depending on the scale of the tsunami. In this way, by adjusting the level of detail of the notification based on the scale of the disaster, more effective evacuation notifications can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input disaster scale data into the generating AI and cause the generating AI to adjust the level of detail of the notification.

[0049] The providing unit can apply different notification algorithms depending on the disaster category when issuing an evacuation notification. For example, in the case of an earthquake, the providing unit can apply an evacuation notification algorithm specifically for earthquakes. Furthermore, in the case of a tsunami, the providing unit can also apply an evacuation notification algorithm specifically for tsunamis. Furthermore, in the case of heavy rain, the providing unit can also apply an evacuation notification algorithm specifically for heavy rain. In this way, by applying different notification algorithms depending on the disaster category, more accurate evacuation notifications are possible. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input disaster category data into the generating AI and cause the generating AI to apply the notification algorithm.

[0050] The providing unit can improve the accuracy of evacuation notifications by referring to past notification results. For example, the providing unit can improve the accuracy of the current earthquake evacuation notification based on past earthquake evacuation notification results. The providing unit can also improve the accuracy of the current tsunami evacuation notification based on past tsunami evacuation notification results. The providing unit can also improve the accuracy of the current heavy rain evacuation notification based on past heavy rain evacuation notification results. In this way, by referring to past notification results, the accuracy of notifications is improved, and more accurate evacuation notifications are possible. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input past notification result data into the generating AI and cause the generating AI to improve the accuracy of notifications.

[0051] The providing unit can determine the priority of notifications based on the time of disaster occurrence when issuing evacuation notifications. For example, the providing unit prioritizes notification of the most important information immediately after an earthquake occurs. The providing unit can also prioritize notification of a tsunami progress forecast immediately after a tsunami occurs. The providing unit can also prioritize notification of rainfall and river water level forecasts when heavy rain is predicted. This enables prompt and effective evacuation notifications by determining the priority of notifications based on the time of disaster occurrence. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the time of disaster occurrence into the generating AI and have the generating AI determine the priority of notifications.

[0052] The providing unit can adjust the order of notifications based on the relevance of disasters when issuing evacuation notifications. For example, if an earthquake and a tsunami are related, the providing unit can issue a tsunami notification after an earthquake notification. Furthermore, if heavy rain and river flooding are related, the providing unit can also issue a heavy rain notification before a river water level notification. Furthermore, if multiple disasters occur simultaneously, the providing unit can prioritize notifications of disasters that are more related. This allows for more effective evacuation notifications by adjusting the order of notifications based on the relevance of disasters. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input disaster relevance data into the generating AI and cause the generating AI to adjust the order of notifications.

[0053] The providing unit can adjust the use of technical terminology in the evacuation notification according to the user's level of expertise. For example, the providing unit can provide an evacuation notification in simple language to a user with little technical knowledge. The providing unit can also provide an evacuation notification using detailed technical terminology to a user with technical knowledge. The providing unit can also adjust the way the evacuation notification is expressed according to the user's level of expertise. This allows for more appropriate evacuation notifications by adjusting the use of technical terminology in the notification according to the user's level of expertise. Some or all of the above-described processing by the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's level of expertise data into the generating AI and cause the generating AI to use technical terminology.

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

[0055] The analysis unit can analyze the behavioral patterns of residents in the event of a disaster and optimize evacuation routes. For example, the analysis unit collects data on the movements of residents during past disasters and predicts the congestion status of evacuation routes. The analysis unit can also evaluate the safety of evacuation routes based on the age group and health status of residents. Furthermore, the analysis unit can also consider the capacity of evacuation shelters and diverge evacuation routes. This makes it possible to optimize evacuation routes taking into account the behavioral patterns of residents, resulting in safer and faster evacuation.

[0056] The collection unit can collect information on infrastructure damage in the event of a disaster in real time and provide it to the analysis unit. For example, the collection unit uses sensors to detect road damage and bridge collapse and collect data. The collection unit can also monitor the supply of electricity and water and immediately notify if an abnormality occurs. Furthermore, the collection unit can monitor the status of communication infrastructure and provide alternative means in the event of a communication failure. This allows the status of infrastructure damage to be grasped in real time and enables rapid response.

[0057] The analysis unit can analyze traffic conditions in the event of a disaster and optimize evacuation routes. For example, the analysis unit collects real-time traffic data and predicts congestion. The analysis unit can also analyze traffic accident occurrences and evaluate the safety of evacuation routes. Furthermore, the analysis unit can monitor the operation status of public transportation and provide optimal evacuation methods. This makes it possible to optimize evacuation routes taking traffic conditions into account, resulting in safer and faster evacuation.

[0058] The collection unit can collect meteorological data in real time when a disaster occurs and provide it to the analysis unit. For example, the collection unit uses sensors to measure and collect data such as temperature, humidity, wind speed, and rainfall. The collection unit can also receive data from meteorological satellites to grasp weather conditions over a wide area. Furthermore, the collection unit can predict the progression of a disaster based on the meteorological data and provide the forecast to the analysis unit. This allows for real-time collection of meteorological data and enables rapid response.

[0059] The analysis unit can analyze the operating status of medical institutions in the event of a disaster and optimize evacuation routes. For example, the analysis unit can grasp the capacity and operating status of medical institutions in real time and provide optimal evacuation routes. The analysis unit can also evaluate the safety of evacuation routes based on the location information of medical institutions. Furthermore, the analysis unit can also consider the equipment status of medical institutions and diversify evacuation routes. This makes it possible to optimize evacuation routes taking into account the operating status of medical institutions, resulting in safer and faster evacuation.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects earthquake and tsunami data. Data such as earthquake intensity, tsunami height, and occurrence time are measured and collected in real time. For example, the earthquake intensity is measured by a sensor, the tsunami height is measured by a tsunami meter, and the occurrence time is recorded with a timestamp. Step 2: The analysis unit analyzes the data collected by the collection unit. It applies statistical analysis of the data and machine learning algorithms to analyze and predict the occurrence patterns of earthquakes and tsunamis. It also clusters the data to extract characteristics of earthquakes and tsunamis. Step 3: The raising section raises the breakwater based on the analysis results obtained by the analysis section. The height and speed of the breakwater's rise are adjusted according to the scale of the earthquake and tsunami, optimizing the control method. Step 4: The information provider determines the appropriate evacuation route based on the latest disaster information and evacuation route database, and notifies residents. It collects weather, earthquake, and traffic information, and determines the optimal evacuation route by taking into account the safety and distance of the evacuation route, as well as the capacity of evacuation shelters.

[0062] (Example 2) A disaster prevention system according to an embodiment of the present invention collects and analyzes data on earthquakes and tsunamis, raises breakwaters, and provides optimal evacuation routes. The disaster prevention system collects and analyzes data on earthquakes and tsunamis, raises breakwaters, and provides optimal evacuation routes, thereby enabling rapid and accurate disaster prevention measures. For example, the disaster prevention system collects data on earthquakes and tsunamis. For example, the disaster prevention system collects data such as the seismic intensity of earthquakes, the height of tsunamis, and the time of occurrence. The disaster prevention system then analyzes the collected data. For example, the disaster prevention system performs analysis using statistical analysis of the data or machine learning algorithms. The disaster prevention system then raises breakwaters based on the analysis results. For example, the disaster prevention system adjusts the height of the breakwaters depending on the scale of the earthquake or tsunami. The disaster prevention system then determines an appropriate evacuation route based on the latest disaster information and an evacuation route database, and notifies residents. For example, the disaster prevention system collects weather data, earthquake data, traffic information, and the like, and uses an algorithm to determine the optimal evacuation route. This allows the disaster prevention system to protect people's lives and property from natural disasters such as earthquakes and tsunamis. This enables the disaster prevention system to respond quickly and accurately, minimizing damage during disasters. For example, the disaster prevention system can analyze earthquake and tsunami data in real time and automatically raise breakwaters to the required height. The disaster prevention system can also collect weather data, earthquake data, traffic information, and other data, and use algorithms to determine the optimal evacuation route, providing residents with a quick and safe evacuation route.

[0063] The disaster prevention system according to the embodiment includes a collection unit, an analysis unit, an elevation unit, and a provision unit. The collection unit collects earthquake and tsunami data. The earthquake and tsunami data includes, for example, earthquake seismic intensity, tsunami height, and occurrence time, but is not limited to these examples. For example, the collection unit measures the earthquake seismic intensity using a sensor and collects the data. The collection unit can also use a tsunami meter to measure the tsunami height. The collection unit can also assign a timestamp to record the occurrence time. For example, the collection unit measures the earthquake seismic intensity in real time and collects the data. The collection unit can also measure the tsunami height in real time and collect the data. The collection unit can also record the occurrence time in real time and collect the data. The analysis unit analyzes the data collected by the collection unit. For example, the analysis is performed by applying statistical analysis of the data or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit performs statistical analysis of the data to analyze the occurrence pattern of earthquakes and tsunamis. The analysis unit can also apply a machine learning algorithm to predict earthquakes and tsunamis. The analysis unit can also cluster data to extract characteristics of earthquakes and tsunamis. For example, the analysis unit performs statistical analysis of data to analyze patterns of earthquake and tsunami occurrence. The analysis unit can also apply a machine learning algorithm to predict earthquakes and tsunamis. The analysis unit can also cluster data to extract characteristics of earthquakes and tsunamis. The raising unit raises the breakwater based on the analysis results obtained by the analysis unit. The breakwater is raised based on, for example, a raising speed, a raising height, and a control method, but is not limited to these examples. For example, the raising unit adjusts the raising height of the breakwater depending on the scale of the earthquake or tsunami. The raising unit can also adjust the raising speed. The raising unit can also optimize the control method of the breakwater. For example, the raising unit adjusts the raising height of the breakwater depending on the scale of the earthquake or tsunami. The raising unit can also adjust the raising speed. The raising unit can also optimize the control method of the breakwater. The information provider will determine the appropriate evacuation route based on the latest disaster information and evacuation route database and notify residents.The determination of an evacuation route is based on, for example, the safety and distance of the evacuation route, the capacity of the evacuation shelter, etc., but is not limited to such examples. For example, the providing unit collects weather data, earthquake data, traffic information, etc., and determines an optimal evacuation route. The providing unit can also evaluate the safety of the evacuation route. The providing unit can also determine an evacuation route taking into account the capacity of the evacuation shelter. For example, the providing unit collects weather data, earthquake data, traffic information, etc., and determines an optimal evacuation route. The providing unit can also evaluate the safety of the evacuation route. The providing unit can also determine an evacuation route taking into account the capacity of the evacuation shelter. As a result, the disaster prevention system according to the embodiment collects and analyzes earthquake and tsunami data, raises breakwaters, and provides optimal evacuation routes, thereby enabling rapid and accurate disaster prevention measures.

[0064] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit can increase the frequency of data collection and provide real-time updates. Furthermore, if the user is relaxed, the collection unit can reduce the frequency of data collection and collect only necessary information. Furthermore, if the user is facing an emergency, the collection unit can immediately start data collection and quickly provide information. This allows data collection at a more appropriate time by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0065] When collecting earthquake and tsunami data, the collection unit can improve the collection method by referring to past disaster data. For example, the collection unit refers to past earthquake data to identify the epicenter and predict the seismic intensity. The collection unit can also refer to past tsunami data to predict the arrival time and height of the tsunami. The collection unit can also determine optimal sensor placement and data collection points based on past disaster data. By referring to past disaster data, the data collection method can be optimized, enabling highly accurate data collection. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input past disaster data into the generation AI and have the generation AI optimize the data collection method.

[0066] The collection unit can change the collection range in real time based on the earthquake epicenter or tsunami source during data collection. For example, if the earthquake epicenter is identified, the collection unit can strengthen data collection in the surrounding area. Furthermore, if the tsunami source is identified, the collection unit can also collect data along its direction of travel. The collection unit can also expand or narrow the collection range depending on the scale of the earthquake or tsunami. This enables more effective data collection by dynamically changing the collection range based on the earthquake epicenter or tsunami source. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input earthquake epicenter data to the generation AI and cause the generation AI to change the collection range.

[0067] The collection unit can integrate data from multiple sensors during data collection to improve collection accuracy. The collection unit integrates data from, for example, seismometers, tsunami meters, and weather sensors, and performs comprehensive analysis. The collection unit can also collect data from multiple sensors in real time to provide highly accurate information. The collection unit can also compare and complement data between sensors and eliminate outliers and errors. By integrating data from multiple sensors, collection accuracy can be improved and more accurate information can be obtained. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input data from multiple sensors into a generation AI and have the generation AI integrate the data.

[0068] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is feeling anxious, the collection unit prioritizes collecting important data and provides it quickly. Furthermore, when the user is relaxed, the collection unit can also perform normal data collection and provide only necessary information. Furthermore, when the user is facing an emergency, the collection unit can immediately collect the most important data and provide it quickly. This allows important data to be collected quickly by determining the priority of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0069] When collecting data, the collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration. For example, the collection unit prioritizes collecting data around the epicenter of an earthquake. The collection unit can also prioritize collecting data in areas along the direction of tsunami travel. The collection unit can also prioritize collecting data in areas where heavy rain is predicted. In this way, highly relevant data can be collected preferentially by taking geographical location information into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0070] The collection unit can analyze information from social media and collect related data when collecting data. For example, the collection unit analyzes posts on social media to understand the occurrence of earthquakes and tsunamis. The collection unit can also identify the damage situation based on location information on social media. The collection unit can also collect real-time information on social media and take prompt action. This allows related data to be collected in real time by analyzing information from social media. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media data into a generation AI and have the generation AI collect related data.

[0071] The collection unit can customize the collection method by reflecting past feedback when collecting data. The collection unit can improve the data collection method based on feedback from past disasters, for example. The collection unit can also adjust the collection range and frequency by reflecting feedback from users. The collection unit can also analyze past data collection results and introduce the optimal collection method. In this way, by reflecting past feedback, the collection method can be customized, enabling more effective data collection. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input past feedback data into the generation AI and have the generation AI customize the collection method.

[0072] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide simple and easy-to-understand analysis results. Furthermore, if the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is facing an emergency, the analysis unit can quickly provide important information. This allows for more appropriate analysis results to be provided by adjusting the analysis presentation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the scale of the earthquake or tsunami. For example, in the case of a large earthquake, the analysis unit performs a detailed analysis and provides a damage forecast. In addition, in the case of a small earthquake, the analysis unit can perform a simple analysis and provide only the necessary information. The analysis unit can also use detailed analysis or simple analysis depending on the scale of the tsunami. This allows for more effective analysis by adjusting the level of detail of the analysis based on the scale of the earthquake or tsunami. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input earthquake and tsunami scale data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the disaster category. For example, in the case of an earthquake, the analysis unit applies a seismic wave analysis algorithm. In addition, in the case of a tsunami, the analysis unit can also apply a tsunami progress prediction algorithm. In addition, in the case of heavy rain, the analysis unit can also apply a rainfall and river water level prediction algorithm. In this way, applying different analysis algorithms depending on the disaster category enables more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input disaster category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. The analysis unit can, for example, improve the accuracy of the current earthquake analysis based on past earthquake analysis results. The analysis unit can also improve the accuracy of the current tsunami analysis based on past tsunami analysis results. The analysis unit can also improve the accuracy of the current heavy rain analysis based on past heavy rain analysis results. In this way, by referring to past analysis results, the accuracy of the analysis is improved and more accurate information can be obtained. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0076] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is facing an emergency, the analysis unit can quickly provide important information. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI adjust the level of detail of the analysis results.

[0077] During analysis, the analysis unit can determine the priority of analysis based on the time of disaster occurrence. For example, the analysis unit prioritizes analysis of the most important information immediately after an earthquake occurs. The analysis unit can also prioritize analysis of tsunami progress predictions immediately after a tsunami occurs. The analysis unit can also prioritize analysis of rainfall and river water level predictions when heavy rain is predicted. This enables rapid and effective analysis by determining the priority of analysis based on the time of disaster occurrence. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of disaster occurrence into the generation AI and have the generation AI determine the priority of analysis.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of disasters. For example, if an earthquake and a tsunami are related, the analysis unit can perform an earthquake analysis followed by a tsunami analysis. Furthermore, if heavy rain and river flooding are related, the analysis unit can also perform a heavy rain analysis followed by a river water level analysis. Furthermore, if multiple disasters occur simultaneously, the analysis unit can prioritize the analysis of disasters that are more closely related. This allows for more effective analysis by adjusting the order of analysis based on the relevance of disasters. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input disaster relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0079] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results in simple language to a user with little technical expertise. The analysis unit can also provide analysis results using detailed technical terminology to a user with technical expertise. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0080] The ascent unit can estimate the user's emotions and adjust the ascent speed of the breakwater based on the estimated user's emotions. For example, if the user is feeling anxious, the ascent unit can increase the ascent speed of the breakwater. Furthermore, if the user is relaxed, the ascent unit can normalize the ascent speed of the breakwater. Furthermore, if the user is facing an emergency, the ascent unit can maximize the ascent speed of the breakwater. This allows the breakwater to be raised at a more appropriate speed by adjusting the ascent speed of the breakwater according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the ascent unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the ascent unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the ascent speed of the breakwater.

[0081] The raising unit can dynamically change the raising height of the breakwater based on the scale of the earthquake or tsunami when raising the breakwater. For example, in the case of a large earthquake, the raising unit can maximize the raising height of the breakwater. In addition, in the case of a small earthquake, the raising unit can also minimize the raising height of the breakwater. The raising unit can also adjust the raising height of the breakwater according to the scale of the tsunami. This allows for more effective raising of the breakwater by dynamically changing the raising height based on the scale of the earthquake or tsunami. Some or all of the above-mentioned processing in the raising unit may be performed using, or without, AI, for example. For example, the raising unit can input earthquake or tsunami scale data into the generation AI and have the generation AI adjust the raising height.

[0082] When raising the breakwater, the ascent unit can optimize the ascent method taking into account surrounding buildings and terrain. For example, the ascent unit adjusts the ascent height of the breakwater to match the height of the surrounding buildings. The ascent unit can also optimize the ascent method of the breakwater according to the slope of the terrain. The ascent unit can also select an optimal ascent pattern taking into account surrounding buildings and terrain. This allows for more effective ascent of the breakwater by taking into account the surrounding buildings and terrain. Some or all of the above-described processing in the ascent unit may be performed using, or without, AI, for example. For example, the ascent unit can input data on surrounding buildings and terrain into the generation AI and have the generation AI execute optimization of the ascent method.

[0083] The ascent unit can improve the accuracy of ascent by integrating data from multiple sensors when raising the breakwater. The ascent unit can integrate data from, for example, seismometers, tsunami sensors, and weather sensors to improve the accuracy of ascent. The ascent unit can also collect data from multiple sensors in real time to improve the accuracy of ascent. The ascent unit can also compare and complement data between sensors to eliminate outliers and errors. By integrating data from multiple sensors, the accuracy of ascent is improved, enabling more accurate breakwater raising. Some or all of the above-described processing in the ascent unit may be performed using, or without, AI. For example, the ascent unit can input data from multiple sensors into a generation AI and have the generation AI integrate the data.

[0084] The raising unit can estimate the user's emotions and determine the timing of raising the breakwater based on the estimated user's emotions. For example, if the user is feeling anxious, the raising unit can accelerate the raising of the breakwater. Furthermore, if the user is relaxed, the raising unit can also raise the breakwater normally. Furthermore, if the user is facing an emergency, the raising unit can also raise the breakwater immediately. This allows the breakwater to be raised at a more appropriate timing by determining the timing of raising the breakwater according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the raising unit can be performed using, for example, an AI, or without an AI. For example, the raising unit can input the user's facial expression data into the generation AI and have the generation AI determine the timing of raising the breakwater.

[0085] The raising unit can dynamically change the raising range by taking into account geographical location information when raising the breakwater. For example, the raising unit can prioritize raising breakwaters in areas close to the earthquake epicenter. The raising unit can also prioritize raising breakwaters in areas along the direction of tsunami travel. The raising unit can also determine the optimal raising range based on geographical location information. This allows for more effective breakwater raising by taking geographical location information into consideration. Some or all of the above-mentioned processing in the raising unit can be performed using, for example, AI, or can be performed without using AI. For example, the raising unit can input geographical location information data into the generation AI and have the generation AI change the raising range.

[0086] The raising unit can analyze information from social media and reflect relevant data when raising the breakwater. For example, the raising unit can analyze posts on social media to grasp the occurrence status of earthquakes and tsunamis. The raising unit can also identify the damage status based on location information on social media. The raising unit can also collect real-time information on social media and take prompt action. This allows for analyzing information from social media to reflect relevant data in real time, enabling prompt action. Some or all of the above-mentioned processing in the raising unit may be performed using, for example, AI, or may be performed without using AI. For example, the raising unit can input social media data into the generation AI and have the generation AI reflect the relevant data.

[0087] The ascent unit can customize the ascent method by reflecting past feedback when raising the breakwater. The ascent unit can improve the ascent method of the breakwater, for example, based on feedback from past disasters. The ascent unit can also adjust the ascent range and speed by reflecting feedback from the user. The ascent unit can also analyze past data collection results and introduce the optimal ascent method. This allows the ascent method to be customized by reflecting past feedback, enabling more effective ascent of the breakwater. Some or all of the above-mentioned processing in the ascent unit may be performed, for example, using AI, or may be performed without using AI. For example, the ascent unit can input past feedback data into the generation AI and have the generation AI customize the ascent method.

[0088] The providing unit can estimate the user's emotions and adjust the presentation style of the evacuation notice based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit can provide a simple and easy-to-understand evacuation notice. Furthermore, if the user is relaxed, the providing unit can also provide a detailed evacuation notice. Furthermore, if the user is facing an emergency, the providing unit can quickly provide important information. This allows the evacuation notice to be provided more appropriately by adjusting the presentation style of the evacuation notice according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the presentation style of the evacuation notice.

[0089] The providing unit can adjust the level of detail of the evacuation notification based on the scale of the disaster. For example, in the case of a large-scale earthquake, the providing unit can provide a detailed evacuation notification. Furthermore, in the case of a small-scale earthquake, the providing unit can also provide a simple evacuation notification. Furthermore, the providing unit can selectively use a detailed evacuation notification or a simple evacuation notification depending on the scale of the tsunami. In this way, by adjusting the level of detail of the notification based on the scale of the disaster, more effective evacuation notifications can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input disaster scale data into the generating AI and cause the generating AI to adjust the level of detail of the notification.

[0090] The providing unit can apply different notification algorithms depending on the disaster category when issuing an evacuation notification. For example, in the case of an earthquake, the providing unit can apply an evacuation notification algorithm specifically for earthquakes. Furthermore, in the case of a tsunami, the providing unit can also apply an evacuation notification algorithm specifically for tsunamis. Furthermore, in the case of heavy rain, the providing unit can also apply an evacuation notification algorithm specifically for heavy rain. In this way, by applying different notification algorithms depending on the disaster category, more accurate evacuation notifications are possible. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input disaster category data into the generating AI and cause the generating AI to apply the notification algorithm.

[0091] The providing unit can improve the accuracy of evacuation notifications by referring to past notification results. For example, the providing unit can improve the accuracy of the current earthquake evacuation notification based on past earthquake evacuation notification results. The providing unit can also improve the accuracy of the current tsunami evacuation notification based on past tsunami evacuation notification results. The providing unit can also improve the accuracy of the current heavy rain evacuation notification based on past heavy rain evacuation notification results. In this way, by referring to past notification results, the accuracy of notifications is improved, and more accurate evacuation notifications are possible. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input past notification result data into the generating AI and cause the generating AI to improve the accuracy of notifications.

[0092] The providing unit can estimate the user's emotions and adjust the length of the evacuation notice based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit can provide a short and to-the-point evacuation notice. The providing unit can also provide a detailed evacuation notice if the user is relaxed. The providing unit can also quickly provide important information if the user is facing an emergency. This allows the evacuation notice to be adjusted in length according to the user's emotions, thereby providing a more appropriate evacuation notice. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the length of the evacuation notice.

[0093] The providing unit can determine the priority of notifications based on the time of disaster occurrence when issuing evacuation notifications. For example, the providing unit prioritizes notification of the most important information immediately after an earthquake occurs. The providing unit can also prioritize notification of a tsunami progress forecast immediately after a tsunami occurs. The providing unit can also prioritize notification of rainfall and river water level forecasts when heavy rain is predicted. This enables prompt and effective evacuation notifications by determining the priority of notifications based on the time of disaster occurrence. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the time of disaster occurrence into the generating AI and have the generating AI determine the priority of notifications.

[0094] The providing unit can adjust the order of notifications based on the relevance of disasters when issuing evacuation notifications. For example, if an earthquake and a tsunami are related, the providing unit can issue a tsunami notification after an earthquake notification. Furthermore, if heavy rain and river flooding are related, the providing unit can also issue a heavy rain notification before a river water level notification. Furthermore, if multiple disasters occur simultaneously, the providing unit can prioritize notifications of disasters that are more related. This allows for more effective evacuation notifications by adjusting the order of notifications based on the relevance of disasters. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input disaster relevance data into the generating AI and cause the generating AI to adjust the order of notifications.

[0095] The providing unit can adjust the use of technical terminology in the evacuation notification according to the user's level of expertise. For example, the providing unit can provide an evacuation notification in simple language to a user with little technical knowledge. The providing unit can also provide an evacuation notification using detailed technical terminology to a user with technical knowledge. The providing unit can also adjust the way the evacuation notification is expressed according to the user's level of expertise. This allows for more appropriate evacuation notifications by adjusting the use of technical terminology in the notification according to the user's level of expertise. Some or all of the above-described processing by the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's level of expertise data into the generating AI and cause the generating AI to use technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, raising unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect earthquake and tsunami data using the camera 42 and microphone 38B of the smart device 14. For example, the analysis unit can analyze the collected data by the specific processing unit 290 of the data processing device 12. For example, the raising unit can raise a breakwater by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the provision unit can determine the optimal evacuation route and notify residents by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, elevation unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect earthquake and tsunami data using the camera 42 and microphone 238 of the smart glasses 214. For example, the analysis unit can analyze the collected data by the specific processing unit 290 of the data processing device 12. For example, the elevation unit can raise a breakwater by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the provision unit can determine the optimal evacuation route and notify residents by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, elevation unit, and provision unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect earthquake and tsunami data using the camera 42 and microphone 238 of the headset terminal 314. For example, the analysis unit can analyze the collected data by the specific processing unit 290 of the data processing device 12. For example, the elevation unit can raise a breakwater by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the provision unit can determine the optimal evacuation route and notify residents by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, elevation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect earthquake and tsunami data using the camera 42 and microphone 238 of the robot 414. For example, the analysis unit can analyze the collected data by the specific processing unit 290 of the data processing device 12. For example, the elevation unit can raise a breakwater by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the provision unit can determine the optimal evacuation route by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and notify residents of the route.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The analysis unit can analyze the behavioral patterns of residents in the event of a disaster and optimize evacuation routes. For example, the analysis unit collects data on the movements of residents during past disasters and predicts the congestion status of evacuation routes. The analysis unit can also evaluate the safety of evacuation routes based on the age group and health status of residents. Furthermore, the analysis unit can also consider the capacity of evacuation shelters and diverge evacuation routes. This makes it possible to optimize evacuation routes taking into account the behavioral patterns of residents, resulting in safer and faster evacuation.

[0098] The providing unit can estimate the user's emotions and select an evacuation shelter based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit can provide the nearest and safest evacuation shelter with priority. In addition, if the user is feeling relaxed, the providing unit can also select an evacuation shelter taking into consideration the evacuation shelter's capacity and the level of facilities. Furthermore, if the user is facing an emergency, the providing unit can immediately provide information about evacuation shelters and encourage rapid evacuation. This makes it possible to select an evacuation shelter according to the user's emotions, thereby achieving more appropriate evacuation.

[0099] The collection unit can collect information on infrastructure damage in the event of a disaster in real time and provide it to the analysis unit. For example, the collection unit uses sensors to detect road damage and bridge collapse and collect data. The collection unit can also monitor the supply of electricity and water and immediately notify if an abnormality occurs. Furthermore, the collection unit can monitor the status of communication infrastructure and provide alternative means in the event of a communication failure. This allows the status of infrastructure damage to be grasped in real time and enables rapid response.

[0100] The providing unit can estimate the user's emotions and adjust the evacuation route guidance method based on the estimated user's emotions. For example, if the user feels anxious, the providing unit can provide a simple and easy-to-understand evacuation route. Alternatively, if the user feels relaxed, the providing unit can provide a detailed evacuation route. Furthermore, if the user is facing an emergency, the providing unit can quickly provide important information and encourage evacuation. This makes it possible to provide evacuation route guidance according to the user's emotions, thereby achieving more appropriate evacuation.

[0101] The analysis unit can analyze traffic conditions in the event of a disaster and optimize evacuation routes. For example, the analysis unit collects real-time traffic data and predicts congestion. The analysis unit can also analyze traffic accident occurrences and evaluate the safety of evacuation routes. Furthermore, the analysis unit can monitor the operation status of public transportation and provide optimal evacuation methods. This makes it possible to optimize evacuation routes taking traffic conditions into account, resulting in safer and faster evacuation.

[0102] The providing unit can estimate the user's emotions and notify the user of the shelter occupancy status based on the estimated user's emotions. For example, if the user feels anxious, the providing unit can preferentially guide the user to shelters with a small capacity. Also, if the user feels relaxed, the providing unit can guide the user to shelters with a large capacity. Furthermore, if the user is facing an emergency, the providing unit can immediately notify the user of the shelter occupancy status and encourage the user to evacuate quickly. This makes it possible to notify the user of the shelter occupancy status according to the user's emotions, thereby enabling more appropriate evacuation.

[0103] The collection unit can collect meteorological data in real time when a disaster occurs and provide it to the analysis unit. For example, the collection unit uses sensors to measure and collect data such as temperature, humidity, wind speed, and rainfall. The collection unit can also receive data from meteorological satellites to grasp weather conditions over a wide area. Furthermore, the collection unit can predict the progression of a disaster based on the meteorological data and provide the forecast to the analysis unit. This allows for real-time collection of meteorological data and enables rapid response.

[0104] The providing unit can estimate the user's emotions and evaluate the safety of evacuation routes based on the estimated user's emotions. For example, if the user feels anxious, the providing unit can prioritize providing safer evacuation routes. Also, if the user feels relaxed, the providing unit can provide shorter evacuation routes. Furthermore, if the user is facing an emergency, the providing unit can immediately provide safer evacuation routes and encourage rapid evacuation. This makes it possible to evaluate the safety of evacuation routes according to the user's emotions, thereby achieving more appropriate evacuation.

[0105] The analysis unit can analyze the operating status of medical institutions in the event of a disaster and optimize evacuation routes. For example, the analysis unit can grasp the capacity and operating status of medical institutions in real time and provide optimal evacuation routes. The analysis unit can also evaluate the safety of evacuation routes based on the location information of medical institutions. Furthermore, the analysis unit can also consider the equipment status of medical institutions and diversify evacuation routes. This makes it possible to optimize evacuation routes taking into account the operating status of medical institutions, resulting in safer and faster evacuation.

[0106] The providing unit can estimate the user's emotions and adjust the frequency of evacuation route guidance based on the estimated user's emotions. For example, if the user feels anxious, the providing unit can frequently provide evacuation route guidance. Alternatively, if the user feels relaxed, the providing unit can provide evacuation route guidance only when necessary. Furthermore, if the user is facing an emergency, the providing unit can immediately provide evacuation route guidance to encourage rapid evacuation. This makes it possible to adjust the frequency of evacuation route guidance according to the user's emotions, thereby achieving more appropriate evacuation.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The collection unit collects earthquake and tsunami data. Data such as earthquake intensity, tsunami height, and occurrence time are measured and collected in real time. For example, the earthquake intensity is measured by a sensor, the tsunami height is measured by a tsunami meter, and the occurrence time is recorded with a timestamp. Step 2: The analysis unit analyzes the data collected by the collection unit. It applies statistical analysis of the data and machine learning algorithms to analyze and predict the occurrence patterns of earthquakes and tsunamis. It also clusters the data to extract characteristics of earthquakes and tsunamis. Step 3: The raising section raises the breakwater based on the analysis results obtained by the analysis section. The height and speed of the breakwater's rise are adjusted according to the scale of the earthquake and tsunami, optimizing the control method. Step 4: The information provider determines the appropriate evacuation route based on the latest disaster information and evacuation route database, and notifies residents. It collects weather, earthquake, and traffic information, and determines the optimal evacuation route by taking into account the safety and distance of the evacuation route, as well as the capacity of evacuation shelters.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0153] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0154] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0171] 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.

[0172] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0173] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0174] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0180] [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects earthquake and tsunami data, an analysis unit that analyzes the data collected by the collection unit; a raising unit that raises the breakwater based on the analysis results obtained by the analysis unit; A provision unit that determines the appropriate evacuation route from the latest disaster information and evacuation route database and notifies residents. A system characterized by:

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

3. The collecting unit When collecting earthquake and tsunami data, improve collection methods by referring to past disaster data.

2. The system of claim 1.

4. The collecting unit During data collection, the collection range is changed in real time based on the epicenter of the earthquake or the source of the tsunami.

2. The system of claim 1.

5. The collecting unit When collecting data, data from multiple sensors can be integrated to improve collection accuracy.

2. The system of claim 1.

6. The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit When collecting data, prioritize important data based on geographic location 2. The system of claim 1.

8. The collecting unit During data collection, we analyze information from social media and collect relevant data.

2. The system of claim 1.

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