system

Abstract

The system according to this embodiment aims to grasp detailed disaster risks for each region and to formulate effective disaster prevention plans. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a prediction unit, and an instruction unit. The collection unit collects data such as topographic data, weather information, past disaster history, and infrastructure status. The analysis unit comprehensively analyzes the data collected by the collection unit and creates a detailed disaster risk map for each region. The proposal unit proposes the optimal evacuation plan and infrastructure development priorities based on the analysis results obtained by the analysis unit. The prediction unit predicts disasters in real time based on the latest data. The instruction unit generates the optimal evacuation order according to the situation of each resident.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to grasp the detailed disaster risks for each region and formulate an effective disaster prevention plan.

[0005] The system according to the embodiment aims to grasp the detailed disaster risks for each region and formulate an effective disaster prevention plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a prediction unit, and an instruction unit. The data collection unit collects data such as topographic data, weather information, past disaster history, and infrastructure status. The analysis unit comprehensively analyzes the data collected by the data collection unit and creates detailed disaster risk maps for each region. The proposal unit proposes optimal evacuation plans and infrastructure development priorities based on the analysis results obtained by the analysis unit. The prediction unit predicts disasters in real time based on the latest data. The instruction unit generates optimal evacuation orders according to the situation of each resident. [Effects of the Invention]

[0007] The system according to this embodiment can grasp detailed disaster risks for each region and formulate effective disaster prevention plans. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that utilizes generative AI to analyze local disaster risks in detail and formulate effective disaster prevention plans. This system aims to strengthen the disaster prevention capabilities of local governments and promote migration by appealing to safe and secure rural living. The system comprehensively analyzes diverse data such as topographic data, weather information, past disaster history, and infrastructure status to create detailed disaster risk maps for each region. Furthermore, it proposes optimal evacuation plans and infrastructure development priorities that take into account population dynamics and regional characteristics. The system is also equipped with a real-time disaster prediction function and a personalized evacuation instruction function for residents. In addition, a disaster simulation function using VR technology supports raising residents' disaster awareness and practical evacuation drills. The system also functions as a tool to visualize the safety of a region and appeal to prospective migrants with the attractiveness of rural areas. This aims to achieve both strengthening rural disaster prevention capabilities and promoting migration. As a result, the system can analyze local disaster risks in detail and formulate effective disaster prevention plans.

[0029] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a prediction unit, and an instruction unit. The data collection unit collects data such as topographic data, meteorological data, past disaster history, and infrastructure status. For example, the data collected by the data collection unit includes elevation data and geological data for topographic data. Meteorological data includes precipitation, wind speed, temperature, etc. Past disaster history includes the type of disaster that has occurred in the past, the location of the disaster, and the extent of the damage. Infrastructure status includes the current state and durability of infrastructure such as roads, bridges, and dams. The analysis unit comprehensively analyzes the data collected by the data collection unit and creates a detailed disaster risk map for each region. For example, the analysis unit centrally manages the data using a database as a data integration method and performs analysis using statistical analysis and machine learning algorithms. The disaster risk map is created based on evaluation criteria such as flood risk, earthquake risk, and landslide risk. The proposal unit proposes the optimal evacuation plan and infrastructure development priorities based on the analysis results obtained by the analysis unit. For example, the proposal unit considers demographic trends and regional characteristics to determine the placement of evacuation shelters and evacuation routes. The system proposes prioritizing infrastructure development, for example, by prioritizing infrastructure development in areas with a high disaster risk. The prediction unit predicts disasters in real time based on the latest data. For example, the prediction unit predicts typhoon paths based on meteorological data and assesses flood risk. The instruction unit generates optimal evacuation instructions according to the circumstances of each resident. For example, the instruction unit suggests barrier-free evacuation routes to residents using wheelchairs. As a result, the system according to this embodiment can analyze local disaster risks in detail and formulate effective disaster prevention plans.

[0030] The data collection unit collects data such as topographic data, meteorological data, historical disaster data, and infrastructure status. Specifically, topographic data includes elevation data and geological data, which are collected in detail using Geographic Information Systems (GIS). Elevation data shows the difference in topography and provides important information for assessing flood and landslide risks. Geological data shows the characteristics of the ground and the type of soil, which is useful for assessing earthquake and landslide risks. Meteorological data includes precipitation, wind speed, and temperature, which are obtained in real time from weather stations and satellite data. Precipitation data is essential for assessing flood risk, and wind speed data is used to predict damage from typhoons and strong winds. Temperature data is useful for assessing the risk of heat waves and cold waves. Historical disaster history includes the types of disasters that have occurred in the past, their locations, and the extent of the damage, and this data is collected from historical records and reports. Historical disaster history is an important source of information for assessing the risk of similar disasters recurring. Infrastructure status includes the current state and durability of infrastructure such as roads, bridges, and dams, and this data is obtained from field surveys and infrastructure management systems. Current road and bridge data is used to determine evacuation routes and prioritize infrastructure development. Dam durability data is crucial for assessing flood risk. The data collection unit efficiently collects this diverse data and integrates it into a central database, unifying data management across the entire system. This allows the data collection unit to provide all the data necessary for disaster risk assessment quickly and accurately.

[0031] The Analysis Department comprehensively analyzes the data collected by the Data Collection Department to create detailed disaster risk maps for each region. Specifically, as a method of data integration, data is centrally managed using a database, and analysis is performed using statistical analysis and machine learning algorithms. Statistical analysis reveals the distribution and correlation of data and identifies patterns of disaster risk. Machine learning algorithms learn complex relationships from large amounts of data and improve the accuracy of disaster risk prediction. Disaster risk maps are created based on evaluation criteria such as flood risk, earthquake risk, and landslide risk. Flood risk maps show the probability of flooding and the extent of inundation based on elevation data and precipitation data. Earthquake risk maps show the probability of earthquakes and seismic intensity distribution based on geological data and past earthquake history. Landslide risk maps show the risk of landslides and mudslides based on topographic data and precipitation data. To create these risk maps, the Analysis Department visualizes the data using GIS, making it possible to intuitively grasp the risks for each region. Furthermore, the Analysis Department regularly updates the risk maps to provide risk assessments based on the latest data. This allows the analysis department to gain a detailed understanding of disaster risks in each region and support the development of effective disaster prevention plans.

[0032] The proposal department will propose optimal evacuation plans and infrastructure development priorities based on the analysis results obtained by the analysis department. Specifically, it will consider demographic trends and regional characteristics when determining the placement of evacuation shelters and evacuation routes. For example, in areas with a large elderly or disabled population, priority will be given to developing barrier-free evacuation shelters and routes. When utilizing public facilities such as schools and hospitals as evacuation shelters, their location and accessibility will be considered. Regarding infrastructure development priorities, the proposal will prioritize infrastructure development in areas with high disaster risk. For example, in areas with high flood risk, priority will be given to strengthening levees and developing drainage facilities. In areas with high earthquake risk, priority will be given to seismic reinforcement work and developing evacuation routes. The proposal department will compile these proposals into a concrete plan and explain it to relevant organizations and residents. Furthermore, the proposal department will evaluate the feasibility and effectiveness of the proposals and revise the plan as necessary. In this way, the proposal department can propose optimal disaster prevention plans tailored to the characteristics and needs of the region, contributing to the reduction of disaster risk.

[0033] The forecasting unit predicts disasters in real time based on the latest data. Specifically, it predicts typhoon paths and assesses flood risk based on meteorological data. Meteorological data is acquired in real time from weather stations and satellite data and input into the forecasting model. The forecasting model predicts the typhoon's path and intensity based on past meteorological data and current weather conditions. Precipitation data and river water level data are used to assess flood risk, and these data are used to predict the probability of flooding and the extent of inundation. The forecasting unit updates these prediction results in real time to respond to the latest situation. For example, if the typhoon's path changes, the forecasting unit immediately incorporates the new data and updates the prediction results. Furthermore, the forecasting unit simulates multiple scenarios to identify the most likely risk. This allows the forecasting unit to predict the occurrence of disasters in advance and support a quick and appropriate response.

[0034] The evacuation control unit generates optimal evacuation instructions tailored to each resident's situation. Specifically, it suggests barrier-free evacuation routes for residents using wheelchairs. Based on residents' attribute information and current location, the unit calculates the optimal evacuation route and notifies residents. For example, it provides evacuation route maps and voice guidance through a smartphone app. It can also monitor the congestion and traffic conditions at evacuation centers in real time and suggest changes to evacuation routes or shelters. By providing this information quickly and accurately, the unit helps residents evacuate safely. Furthermore, the unit evaluates the effectiveness of evacuation instructions and modifies them as needed. For example, if an evacuation route is congested, it suggests an alternative route. If an evacuation center is full, it directs residents to another center. In this way, the unit provides evacuation instructions that prioritize the safety of residents, minimizing confusion during disasters.

[0035] The system includes a simulation unit that performs disaster simulations using VR technology. For example, the simulation unit can perform evacuation simulations during floods. The simulation unit realistically reproduces evacuation routes and shelter conditions during a flood using VR technology. The simulation unit can also perform evacuation simulations during earthquakes. For example, the simulation unit uses VR technology to reproduce the shaking and collapse of buildings during an earthquake and simulates evacuation actions. Furthermore, the simulation unit can also perform evacuation simulations during fires. For example, the simulation unit uses VR technology to simulate the spread of smoke and securing evacuation routes during a fire. This can support improved disaster prevention awareness among residents and practical evacuation drills.

[0036] The system includes a visualization unit that visualizes regional safety. For example, the visualization unit visually displays disaster risk maps. For instance, it displays flood risk, earthquake risk, and landslide risk on a map using color coding. The visualization unit can also visually display the current state and durability of infrastructure. For example, it can display the durability of roads and bridges using color coding, indicating which infrastructure should be prioritized for development. Furthermore, the visualization unit can visually display evacuation plans and routes. For example, it can display the locations of evacuation shelters and evacuation routes on a map, making evacuation easier for residents. This allows for the visualization of regional safety and helps appeal to prospective residents by highlighting the attractiveness of rural areas.

[0037] The system includes a training unit to improve residents' disaster preparedness awareness. The training unit conducts disaster drills, for example. This includes evacuation drills and disaster preparedness lectures to raise residents' awareness of disaster preparedness. The training unit can also conduct disaster preparedness training using VR technology. For example, it can use VR technology to recreate disaster scenarios such as floods, earthquakes, and fires, allowing residents to experience evacuation actions firsthand. Furthermore, the training unit can provide information to improve disaster preparedness awareness. For example, it can provide residents with disaster preparedness information, such as how to respond during a disaster and evacuation routes. This can improve residents' disaster preparedness awareness.

[0038] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most efficient collection time slot from past data collection history and collect data during that time slot. For example, the data collection unit can optimize the collection method (sensors, drones, etc.) based on past data collection history. For example, the data collection unit can analyze past data collection history and propose a new collection method to improve data accuracy. This improves the efficiency of data collection by selecting the optimal collection method based on past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0039] The data collection unit can filter data while considering regional characteristics and seasonal variations. For example, based on regional characteristics, the data collection unit can concentrate the placement of water level sensors in areas with a high risk of flooding. For example, considering seasonal variations, the data collection unit can add anemometers during typhoon season. For example, the data collection unit can select the optimal data collection points by combining regional characteristics and seasonal variations. This allows for more accurate data to be obtained by collecting data while considering regional characteristics and seasonal variations. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on regional characteristics and seasonal variations into a generating AI and have the generating AI perform the filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the region during data collection. For example, based on geographical location information, the data collection unit can concentrate the placement of water level sensors in areas with a high risk of flooding. For example, based on geographical location information, the data collection unit can add more seismometers in areas with a high risk of earthquakes. For example, based on geographical location information, the data collection unit can select the optimal data collection points. In this way, by performing data collection while considering the geographical location information of the region, highly relevant data can be collected efficiently. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of the region into a generating AI and cause the generating AI to perform the priority collection of highly relevant data.

[0041] The data collection unit can analyze social media and news information during data collection and collect relevant data. For example, the data collection unit can analyze social media posts to collect real-time information about disasters. For example, the data collection unit can prioritize the collection of data from areas with a high disaster risk based on news information. For example, the data collection unit can combine social media and news information to select the optimal data collection points. This allows for the efficient collection of real-time information by analyzing social media and news information and performing data collection. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media and news information into a generating AI and have the generating AI perform the collection of relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a weather forecasting algorithm to meteorological data. For example, the analysis unit can apply a topographic analysis algorithm to topographic data. For example, the analysis unit can apply a disaster risk assessment algorithm to disaster history data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the latest data to provide real-time information. For example, the analysis unit can analyze long-term trends based on historical data. For example, the analysis unit can determine the priority of analysis according to the data collection timing. This allows for the provision of real-time information by determining the priority of analysis according to the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI perform the determination of analysis priorities.

[0045] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and research data during the analysis process. For example, the analysis unit can refer to relevant literature and perform analysis that reflects the latest research findings. For example, the analysis unit can optimize its analysis algorithm based on research data. For example, the analysis unit can improve the accuracy of its analysis by combining literature and research data. As a result, the accuracy of the analysis is improved by referring to relevant literature and research data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature and research data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0046] The proposal unit can adjust the level of detail in its proposals based on the importance of evacuation plans and infrastructure development. For example, the proposal unit can provide detailed proposals for highly important evacuation plans. For example, it can provide simplified proposals for less important infrastructure development. The proposal unit can also determine the priority of proposals according to the importance of evacuation plans and infrastructure development. This allows for efficient proposals by adjusting the level of detail according to the importance of evacuation plans and infrastructure development. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of evacuation plans and infrastructure development into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0047] The proposal unit can apply different proposal algorithms depending on the characteristics and demographics of the region when making a proposal. For example, in areas with a large elderly population, the proposal unit can prioritize proposals for barrier-free access. In areas with a large young population, the proposal unit can prioritize proposals for the development of educational facilities. The proposal unit can apply the most suitable proposal algorithm depending on the characteristics and demographics of the region. This makes it possible to make optimal proposals tailored to the characteristics and demographics of the region. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data on the characteristics and demographics of the region into a generating AI and have the generating AI execute the application of an appropriate proposal algorithm.

[0048] The proposal unit can make optimal proposals by considering the geographical location information of the region. For example, based on geographical location information, the proposal unit can propose to concentrate the placement of water level sensors in areas with a high risk of flooding. For example, considering geographical location information, the proposal unit can propose to increase the number of seismometers in areas with a high risk of earthquakes. For example, based on geographical location information, the proposal unit can propose the optimal infrastructure development. By considering the geographical location information of the region when making proposals, it becomes possible to make highly relevant proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the geographical location information of the region into a generating AI and have the generating AI execute the optimal proposal.

[0049] The proposal unit can improve the accuracy of its proposals by referring to relevant past proposal data when making a proposal. For example, the proposal unit can refer to past proposal data to make the optimal proposal. For example, the proposal unit can optimize its proposal algorithm based on past proposal data. For example, the proposal unit can analyze past proposal data to improve the accuracy of its proposals. As a result, the accuracy of the proposal is improved by referring to relevant past proposal data. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input past proposal data into a generation AI and have the generation AI perform the task of improving the accuracy of the proposals.

[0050] The prediction unit can optimize its prediction algorithm based on the latest data during the prediction process. For example, the prediction unit can optimize its weather forecasting algorithm based on the latest weather data. For example, the prediction unit can optimize its terrain analysis algorithm based on the latest terrain data. For example, the prediction unit can optimize its disaster risk assessment algorithm based on the latest disaster history data. By optimizing the prediction algorithm based on the latest data, the accuracy of the prediction is improved. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the latest data into a generating AI and have the generating AI perform the optimization of the prediction algorithm.

[0051] The prediction unit can improve the accuracy of its predictions by considering regional characteristics and seasonal variations during the prediction process. For example, the prediction unit can consider regional characteristics and focus on water level predictions in areas with a high risk of flooding. For example, the prediction unit can consider seasonal variations and enhance wind speed predictions during typhoon season. For example, the prediction unit can combine regional characteristics and seasonal variations to apply an optimal prediction algorithm. This improves the accuracy of predictions by considering regional characteristics and seasonal variations. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data on regional characteristics and seasonal variations into a generating AI and have the generating AI perform the task of improving prediction accuracy.

[0052] The prediction unit can make optimal predictions by considering the geographical location information of the region. For example, based on geographical location information, the prediction unit can focus on water level predictions in areas with a high risk of flooding. For example, the prediction unit can enhance earthquake predictions in areas with a high risk of earthquakes by considering geographical location information. For example, the prediction unit can apply the optimal prediction algorithm based on geographical location information. This makes it possible to make highly relevant predictions by considering the geographical location information of the region. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the geographical location information of the region into a generating AI and have the generating AI perform the optimal prediction.

[0053] The prediction unit can improve the accuracy of its predictions by referring to relevant historical prediction data during the prediction process. For example, the prediction unit can refer to historical prediction data and apply the optimal prediction algorithm. For example, the prediction unit can optimize its prediction algorithm based on historical prediction data. For example, the prediction unit can analyze historical prediction data to improve the accuracy of its predictions. This improves the accuracy of predictions by referring to relevant historical prediction data. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input historical prediction data into a generating AI and have the generating AI perform the task of improving the accuracy of the predictions.

[0054] The instruction unit can generate optimal evacuation instructions by considering the attribute information of residents when issuing instructions. For example, the instruction unit can suggest a barrier-free evacuation route to the elderly. For example, the instruction unit can provide instructions to children to evacuate with their parents. For example, the instruction unit can suggest a barrier-free evacuation route to residents using wheelchairs. In this way, by generating evacuation instructions while considering the attribute information of residents, evacuation instructions suitable for each resident can be provided. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input resident attribute information into a generation AI and have the generation AI generate the optimal evacuation instructions.

[0055] The instruction unit can update information on evacuation routes and shelters in real time when issuing an instruction. For example, the instruction unit can update the congestion status of evacuation routes in real time and suggest the optimal route. For example, the instruction unit can update the capacity status of shelters in real time and suggest available shelters. For example, the instruction unit can update information on evacuation routes and shelters in real time and provide optimal evacuation instructions. By updating information on evacuation routes and shelters in real time, evacuation instructions based on the latest information can be provided. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input information on evacuation routes and shelters into a generating AI and have the generating AI perform real-time updates.

[0056] The instruction unit can issue optimal evacuation orders by considering the geographical location information of the region when issuing orders. For example, based on geographical location information, the instruction unit can propose the concentrated placement of water level sensors in areas with a high risk of flooding. For example, based on geographical location information, the instruction unit can propose the installation of additional seismometers in areas with a high risk of earthquakes. For example, based on geographical location information, the instruction unit can propose the optimal infrastructure development. By issuing evacuation orders while considering the geographical location information of the region, it becomes possible to issue highly relevant evacuation orders. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the geographical location information of the region into a generating AI and have the generating AI execute the optimal evacuation order.

[0057] The instruction unit can improve the accuracy of its instructions by referring to relevant past evacuation instruction data when issuing instructions. For example, the instruction unit can refer to past evacuation instruction data to issue the most appropriate evacuation instructions. For example, the instruction unit can optimize its instruction algorithm based on past evacuation instruction data. For example, the instruction unit can analyze past evacuation instruction data to improve the accuracy of its instructions. This improves the accuracy of instructions by referring to relevant past evacuation instruction data. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input past evacuation instruction data into a generating AI and have the generating AI perform the task of improving the accuracy of the instructions.

[0058] The simulation unit can generate scenarios that reflect regional characteristics and past disaster history during simulation. For example, the simulation unit can generate a water level rise scenario in areas with a high flood risk, reflecting regional characteristics. For example, the simulation unit can generate an earthquake scenario in areas with a high earthquake risk, based on past disaster history. For example, the simulation unit can generate an optimal simulation scenario by combining regional characteristics and past disaster history. This enables realistic simulations by generating scenarios that reflect regional characteristics and past disaster history. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input data on regional characteristics and past disaster history into a generation AI and have the generation AI execute the scenario generation.

[0059] The simulation unit can provide a realistic evacuation experience using VR technology during simulations. For example, the simulation unit can realistically recreate a flood evacuation experience using VR technology. For example, the simulation unit can realistically recreate an earthquake evacuation experience using VR technology. For example, the simulation unit can realistically recreate a fire evacuation experience using VR technology. By providing a realistic evacuation experience using VR technology, it is possible to improve residents' disaster prevention awareness. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input VR technology into a generating AI and have the generating AI perform the task of providing a realistic evacuation experience.

[0060] The visualization unit can select the optimal visualization method when performing visualization, taking into account regional characteristics and the importance of the data. For example, the visualization unit can visualize water level rise in areas with a high flood risk, taking into account regional characteristics. For example, the visualization unit can highlight and visualize important data based on its importance. For example, the visualization unit can select the optimal visualization method by combining regional characteristics and data importance. This makes it possible to perform visualizations that are highly relevant by taking into account regional characteristics and data importance. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without using AI. For example, the visualization unit can input regional characteristics and data importance into a generating AI and have the generating AI select the optimal visualization method.

[0061] The visualization unit can improve the accuracy of visualization by referring to relevant past visualization data during visualization. For example, the visualization unit can refer to past visualization data and apply the optimal visualization method. For example, the visualization unit can optimize the visualization algorithm based on past visualization data. For example, the visualization unit can analyze past visualization data to improve the accuracy of visualization. This improves the accuracy of visualization by referring to relevant past visualization data. Some or all of the above processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input past visualization data into a generating AI and have the generating AI perform the visualization accuracy improvement.

[0062] The training unit can generate an optimal training program during training, taking into account regional characteristics and resident attribute information. For example, the training unit can provide a training program for rising water levels in areas with a high risk of flooding, taking into account regional characteristics. For example, the training unit can provide a barrier-free training program for the elderly based on resident attribute information. For example, the training unit can generate an optimal training program by combining regional characteristics and resident attribute information. This allows for the provision of training tailored to each resident by generating a training program that takes into account regional characteristics and resident attribute information. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input regional characteristics and resident attribute information into a generating AI and have the generating AI generate an optimal training program.

[0063] The training department can provide realistic evacuation drills using VR technology during training. For example, the training department can realistically recreate flood evacuation drills using VR technology. For example, the training department can realistically recreate earthquake evacuation drills using VR technology. For example, the training department can realistically recreate fire evacuation drills using VR technology. By providing realistic evacuation drills using VR technology, it is possible to improve residents' disaster prevention awareness. Some or all of the above processing in the training department may be performed using AI, for example, or without AI. For example, the training department can input VR technology into a generating AI and have the generating AI perform the provision of realistic evacuation drills.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The data collection unit can gather feedback from local residents and improve the accuracy of disaster risk maps. For example, the unit can collect detailed information on disasters that residents have experienced in the past and reflect it in the risk map. For example, the unit can collect information on problems and areas for improvement in evacuation routes that residents have noticed and use this to improve evacuation plans. For example, the unit can collect ideas for disaster prevention measures proposed by residents and reflect them in the system. This allows for the development of more practical and effective disaster prevention plans by utilizing resident feedback.

[0066] The simulation unit can simulate the economic impact of disasters. For example, it can simulate the impact on a local economy in the event of a flood. For example, it can simulate the costs of infrastructure restoration in the event of an earthquake. For example, it can simulate the impact of insurance payouts in the event of a fire. This allows for a prior understanding of the economic impact of disasters and the implementation of appropriate countermeasures.

[0067] The visualization unit can visualize the distribution of local disaster prevention resources in addition to disaster risk maps. For example, the visualization unit can display the location and capacity of evacuation shelters on a map. For example, the visualization unit can display the locations of fire stations and police stations to demonstrate their response capabilities in emergencies. For example, the visualization unit can display the location and equipment status of medical facilities to visualize their medical response capabilities during disasters. This allows for an understanding of the distribution of local disaster prevention resources and the development of effective disaster prevention plans.

[0068] The training department can provide disaster preparedness training that incorporates game elements to improve residents' disaster preparedness awareness. For example, the training department can help residents learn evacuation procedures through a game where they select an evacuation route. For example, the training department can provide a game where residents can learn how to respond to a disaster in a quiz format. For example, the training department can raise residents' disaster preparedness awareness by holding a contest to test their disaster preparedness knowledge. In this way, disaster preparedness training incorporating game elements can improve residents' disaster preparedness awareness in an enjoyable way.

[0069] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most efficient collection time slot from past data collection history and collect data during that time slot. For example, the data collection unit can optimize the collection method (sensors, drones, etc.) based on past data collection history. For example, the data collection unit can analyze past data collection history and propose new collection methods to improve data accuracy. As a result, the efficiency of data collection is improved by selecting the optimal collection method based on past data collection history.

[0070] The data collection unit can filter data while considering regional characteristics and seasonal variations. For example, based on regional characteristics, the unit can concentrate water level sensors in areas with a high risk of flooding. For example, considering seasonal variations, the unit can add anemometers during typhoon season. For example, the unit can select the optimal data collection points by combining regional characteristics and seasonal variations. This allows for more accurate data to be obtained by collecting data while considering regional characteristics and seasonal variations.

[0071] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the region during data collection. For example, based on geographical location information, the data collection unit can concentrate the placement of water level sensors in areas with a high risk of flooding. For example, based on geographical location information, the data collection unit can add more seismometers in areas with a high risk of earthquakes. For example, based on geographical location information, the data collection unit can select the optimal data collection points. In this way, by collecting data while considering the geographical location information of the region, highly relevant data can be collected efficiently.

[0072] The data collection unit can analyze social media and news information during data collection and collect relevant data. For example, the data collection unit can analyze social media posts to collect real-time information about disasters. For example, the data collection unit can prioritize the collection of data from areas with a high disaster risk based on news information. For example, the data collection unit can combine social media and news information to select the optimal data collection points. This allows for the efficient collection of real-time information by analyzing social media and news information and then collecting the data.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The data collection unit collects data such as topographic data, weather information, past disaster history, and infrastructure status. For example, topographic data includes elevation data and geological data, and weather information includes precipitation, wind speed, and temperature. Past disaster history includes the types of disasters that have occurred in the past, their locations, and the extent of the damage, while infrastructure status includes the current state and durability of infrastructure such as roads, bridges, and dams. Step 2: The analysis department comprehensively analyzes the data collected by the data collection department and creates detailed disaster risk maps for each region. For example, a database is used to centrally manage the data as a data integration method, and analysis is performed using statistical analysis and machine learning algorithms. The disaster risk maps are created based on evaluation criteria such as flood risk, earthquake risk, and landslide risk. Step 3: The proposal department proposes optimal evacuation plans and infrastructure development priorities based on the analysis results obtained by the analysis department. For example, they will consider demographic trends and regional characteristics when determining the placement of evacuation shelters and evacuation routes. For infrastructure development priorities, they will propose prioritizing infrastructure development in areas with a high disaster risk. Step 4: The prediction unit predicts disasters in real time based on the latest data. For example, it predicts typhoon paths based on weather data and assesses flood risk. Step 5: The instruction unit generates the most appropriate evacuation instructions based on each resident's situation. For example, it suggests a barrier-free evacuation route for a resident using a wheelchair.

[0075] (Example of form 2) The system according to an embodiment of the present invention is a system that utilizes generative AI to analyze local disaster risks in detail and formulate effective disaster prevention plans. This system aims to strengthen the disaster prevention capabilities of local governments and promote migration by appealing to safe and secure rural living. The system comprehensively analyzes diverse data such as topographic data, weather information, past disaster history, and infrastructure status to create detailed disaster risk maps for each region. Furthermore, it proposes optimal evacuation plans and infrastructure development priorities that take into account population dynamics and regional characteristics. The system is also equipped with a real-time disaster prediction function and a personalized evacuation instruction function for residents. In addition, a disaster simulation function using VR technology supports raising residents' disaster awareness and practical evacuation drills. The system also functions as a tool to visualize the safety of a region and appeal to prospective migrants with the attractiveness of rural areas. This aims to achieve both strengthening rural disaster prevention capabilities and promoting migration. As a result, the system can analyze local disaster risks in detail and formulate effective disaster prevention plans.

[0076] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a prediction unit, and an instruction unit. The data collection unit collects data such as topographic data, meteorological data, past disaster history, and infrastructure status. For example, the data collected by the data collection unit includes elevation data and geological data for topographic data. Meteorological data includes precipitation, wind speed, temperature, etc. Past disaster history includes the type of disaster that has occurred in the past, the location of the disaster, and the extent of the damage. Infrastructure status includes the current state and durability of infrastructure such as roads, bridges, and dams. The analysis unit comprehensively analyzes the data collected by the data collection unit and creates a detailed disaster risk map for each region. For example, the analysis unit centrally manages the data using a database as a data integration method and performs analysis using statistical analysis and machine learning algorithms. The disaster risk map is created based on evaluation criteria such as flood risk, earthquake risk, and landslide risk. The proposal unit proposes the optimal evacuation plan and infrastructure development priorities based on the analysis results obtained by the analysis unit. For example, the proposal unit considers demographic trends and regional characteristics to determine the placement of evacuation shelters and evacuation routes. The system proposes prioritizing infrastructure development, for example, by prioritizing infrastructure development in areas with a high disaster risk. The prediction unit predicts disasters in real time based on the latest data. For example, the prediction unit predicts typhoon paths based on meteorological data and assesses flood risk. The instruction unit generates optimal evacuation instructions according to the circumstances of each resident. For example, the instruction unit suggests barrier-free evacuation routes to residents using wheelchairs. As a result, the system according to this embodiment can analyze local disaster risks in detail and formulate effective disaster prevention plans.

[0077] The data collection unit collects data such as topographic data, meteorological data, historical disaster data, and infrastructure status. Specifically, topographic data includes elevation data and geological data, which are collected in detail using Geographic Information Systems (GIS). Elevation data shows the difference in topography and provides important information for assessing flood and landslide risks. Geological data shows the characteristics of the ground and the type of soil, which is useful for assessing earthquake and landslide risks. Meteorological data includes precipitation, wind speed, and temperature, which are obtained in real time from weather stations and satellite data. Precipitation data is essential for assessing flood risk, and wind speed data is used to predict damage from typhoons and strong winds. Temperature data is useful for assessing the risk of heat waves and cold waves. Historical disaster history includes the types of disasters that have occurred in the past, their locations, and the extent of the damage, and this data is collected from historical records and reports. Historical disaster history is an important source of information for assessing the risk of similar disasters recurring. Infrastructure status includes the current state and durability of infrastructure such as roads, bridges, and dams, and this data is obtained from field surveys and infrastructure management systems. Current road and bridge data is used to determine evacuation routes and prioritize infrastructure development. Dam durability data is crucial for assessing flood risk. The data collection unit efficiently collects this diverse data and integrates it into a central database, unifying data management across the entire system. This allows the data collection unit to provide all the data necessary for disaster risk assessment quickly and accurately.

[0078] The Analysis Department comprehensively analyzes the data collected by the Data Collection Department to create detailed disaster risk maps for each region. Specifically, as a method of data integration, data is centrally managed using a database, and analysis is performed using statistical analysis and machine learning algorithms. Statistical analysis reveals the distribution and correlation of data and identifies patterns of disaster risk. Machine learning algorithms learn complex relationships from large amounts of data and improve the accuracy of disaster risk prediction. Disaster risk maps are created based on evaluation criteria such as flood risk, earthquake risk, and landslide risk. Flood risk maps show the probability of flooding and the extent of inundation based on elevation data and precipitation data. Earthquake risk maps show the probability of earthquakes and seismic intensity distribution based on geological data and past earthquake history. Landslide risk maps show the risk of landslides and mudslides based on topographic data and precipitation data. To create these risk maps, the Analysis Department visualizes the data using GIS, making it possible to intuitively grasp the risks for each region. Furthermore, the Analysis Department regularly updates the risk maps to provide risk assessments based on the latest data. This allows the analysis department to gain a detailed understanding of disaster risks in each region and support the development of effective disaster prevention plans.

[0079] The proposal department will propose optimal evacuation plans and infrastructure development priorities based on the analysis results obtained by the analysis department. Specifically, it will consider demographic trends and regional characteristics when determining the placement of evacuation shelters and evacuation routes. For example, in areas with a large elderly or disabled population, priority will be given to developing barrier-free evacuation shelters and routes. When utilizing public facilities such as schools and hospitals as evacuation shelters, their location and accessibility will be considered. Regarding infrastructure development priorities, the proposal will prioritize infrastructure development in areas with high disaster risk. For example, in areas with high flood risk, priority will be given to strengthening levees and developing drainage facilities. In areas with high earthquake risk, priority will be given to seismic reinforcement work and developing evacuation routes. The proposal department will compile these proposals into a concrete plan and explain it to relevant organizations and residents. Furthermore, the proposal department will evaluate the feasibility and effectiveness of the proposals and revise the plan as necessary. In this way, the proposal department can propose optimal disaster prevention plans tailored to the characteristics and needs of the region, contributing to the reduction of disaster risk.

[0080] The forecasting unit predicts disasters in real time based on the latest data. Specifically, it predicts typhoon paths and assesses flood risk based on meteorological data. Meteorological data is acquired in real time from weather stations and satellite data and input into the forecasting model. The forecasting model predicts the typhoon's path and intensity based on past meteorological data and current weather conditions. Precipitation data and river water level data are used to assess flood risk, and these data are used to predict the probability of flooding and the extent of inundation. The forecasting unit updates these prediction results in real time to respond to the latest situation. For example, if the typhoon's path changes, the forecasting unit immediately incorporates the new data and updates the prediction results. Furthermore, the forecasting unit simulates multiple scenarios to identify the most likely risk. This allows the forecasting unit to predict the occurrence of disasters in advance and support a quick and appropriate response.

[0081] The evacuation control unit generates optimal evacuation instructions tailored to each resident's situation. Specifically, it suggests barrier-free evacuation routes for residents using wheelchairs. Based on residents' attribute information and current location, the unit calculates the optimal evacuation route and notifies residents. For example, it provides evacuation route maps and voice guidance through a smartphone app. It can also monitor the congestion and traffic conditions at evacuation centers in real time and suggest changes to evacuation routes or shelters. By providing this information quickly and accurately, the unit helps residents evacuate safely. Furthermore, the unit evaluates the effectiveness of evacuation instructions and modifies them as needed. For example, if an evacuation route is congested, it suggests an alternative route. If an evacuation center is full, it directs residents to another center. In this way, the unit provides evacuation instructions that prioritize the safety of residents, minimizing confusion during disasters.

[0082] The system includes a simulation unit that performs disaster simulations using VR technology. For example, the simulation unit can perform evacuation simulations during floods. The simulation unit realistically reproduces evacuation routes and shelter conditions during a flood using VR technology. The simulation unit can also perform evacuation simulations during earthquakes. For example, the simulation unit uses VR technology to reproduce the shaking and collapse of buildings during an earthquake and simulates evacuation actions. Furthermore, the simulation unit can also perform evacuation simulations during fires. For example, the simulation unit uses VR technology to simulate the spread of smoke and securing evacuation routes during a fire. This can support improved disaster prevention awareness among residents and practical evacuation drills.

[0083] The system includes a visualization unit that visualizes regional safety. For example, the visualization unit visually displays disaster risk maps. For instance, it displays flood risk, earthquake risk, and landslide risk on a map using color coding. The visualization unit can also visually display the current state and durability of infrastructure. For example, it can display the durability of roads and bridges using color coding, indicating which infrastructure should be prioritized for development. Furthermore, the visualization unit can visually display evacuation plans and routes. For example, it can display the locations of evacuation shelters and evacuation routes on a map, making evacuation easier for residents. This allows for the visualization of regional safety and helps appeal to prospective residents by highlighting the attractiveness of rural areas.

[0084] The system includes a training unit to improve residents' disaster preparedness awareness. The training unit conducts disaster drills, for example. This includes evacuation drills and disaster preparedness lectures to raise residents' awareness of disaster preparedness. The training unit can also conduct disaster preparedness training using VR technology. For example, it can use VR technology to recreate disaster scenarios such as floods, earthquakes, and fires, allowing residents to experience evacuation actions firsthand. Furthermore, the training unit can provide information to improve disaster preparedness awareness. For example, it can provide residents with disaster preparedness information, such as how to respond during a disaster and evacuation routes. This can improve residents' disaster preparedness awareness.

[0085] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. For example, if the user is relaxed, the data collection unit can perform detailed data collection to improve accuracy. For example, if the user is in a hurry, the data collection unit can quickly collect the necessary data and start analysis immediately. This allows for efficient data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most efficient collection time slot from past data collection history and collect data during that time slot. For example, the data collection unit can optimize the collection method (sensors, drones, etc.) based on past data collection history. For example, the data collection unit can analyze past data collection history and propose a new collection method to improve data accuracy. This improves the efficiency of data collection by selecting the optimal collection method based on past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0087] The data collection unit can filter data while considering regional characteristics and seasonal variations. For example, based on regional characteristics, the data collection unit can concentrate the placement of water level sensors in areas with a high risk of flooding. For example, considering seasonal variations, the data collection unit can add anemometers during typhoon season. For example, the data collection unit can select the optimal data collection points by combining regional characteristics and seasonal variations. This allows for more accurate data to be obtained by collecting data while considering regional characteristics and seasonal variations. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on regional characteristics and seasonal variations into a generating AI and have the generating AI perform the filtering.

[0088] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can prioritize collecting important data to provide reassurance. For example, if the user is relaxed, the data collection unit can collect detailed data to improve accuracy. For example, if the user is in a hurry, the data collection unit can quickly collect the necessary data and start analysis immediately. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the region during data collection. For example, based on geographical location information, the data collection unit can concentrate the placement of water level sensors in areas with a high risk of flooding. For example, based on geographical location information, the data collection unit can add more seismometers in areas with a high risk of earthquakes. For example, based on geographical location information, the data collection unit can select the optimal data collection points. In this way, by performing data collection while considering the geographical location information of the region, highly relevant data can be collected efficiently. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of the region into a generating AI and cause the generating AI to perform the priority collection of highly relevant data.

[0090] The data collection unit can analyze social media and news information during data collection and collect relevant data. For example, the data collection unit can analyze social media posts to collect real-time information about disasters. For example, the data collection unit can prioritize the collection of data from areas with a high disaster risk based on news information. For example, the data collection unit can combine social media and news information to select the optimal data collection points. This allows for the efficient collection of real-time information by analyzing social media and news information and performing data collection. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media and news information into a generating AI and have the generating AI perform the collection of relevant data.

[0091] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, the analysis unit can provide results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0093] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a weather forecasting algorithm to meteorological data. For example, the analysis unit can apply a topographic analysis algorithm to topographic data. For example, the analysis unit can apply a disaster risk assessment algorithm to disaster history data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0094] 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 nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0095] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the latest data to provide real-time information. For example, the analysis unit can analyze long-term trends based on historical data. For example, the analysis unit can determine the priority of analysis according to the data collection timing. This allows for the provision of real-time information by determining the priority of analysis according to the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI perform the determination of analysis priorities.

[0096] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and research data during the analysis process. For example, the analysis unit can refer to relevant literature and perform analysis that reflects the latest research findings. For example, the analysis unit can optimize its analysis algorithm based on research data. For example, the analysis unit can improve the accuracy of its analysis by combining literature and research data. As a result, the accuracy of the analysis is improved by referring to relevant literature and research data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature and research data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0097] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, it becomes possible to provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The proposal unit can adjust the level of detail in its proposals based on the importance of evacuation plans and infrastructure development. For example, the proposal unit can provide detailed proposals for highly important evacuation plans. For example, it can provide simplified proposals for less important infrastructure development. The proposal unit can also determine the priority of proposals according to the importance of evacuation plans and infrastructure development. This allows for efficient proposals by adjusting the level of detail according to the importance of evacuation plans and infrastructure development. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of evacuation plans and infrastructure development into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0099] The proposal unit can apply different proposal algorithms depending on the characteristics and demographics of the region when making a proposal. For example, in areas with a large elderly population, the proposal unit can prioritize proposals for barrier-free access. In areas with a large young population, the proposal unit can prioritize proposals for the development of educational facilities. The proposal unit can apply the most suitable proposal algorithm depending on the characteristics and demographics of the region. This makes it possible to make optimal proposals tailored to the characteristics and demographics of the region. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data on the characteristics and demographics of the region into a generating AI and have the generating AI execute the application of an appropriate proposal algorithm.

[0100] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is feeling anxious, the suggestion unit can prioritize important suggestions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. For example, if the user is in a hurry, the suggestion unit can quickly provide necessary suggestions. This allows for the priority of important suggestions by determining the priority of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0101] The proposal unit can make optimal proposals by considering the geographical location information of the region. For example, based on geographical location information, the proposal unit can propose to concentrate the placement of water level sensors in areas with a high risk of flooding. For example, considering geographical location information, the proposal unit can propose to increase the number of seismometers in areas with a high risk of earthquakes. For example, based on geographical location information, the proposal unit can propose the optimal infrastructure development. By considering the geographical location information of the region when making proposals, it becomes possible to make highly relevant proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the geographical location information of the region into a generating AI and have the generating AI execute the optimal proposal.

[0102] The proposal unit can improve the accuracy of its proposals by referring to relevant past proposal data when making a proposal. For example, the proposal unit can refer to past proposal data to make the optimal proposal. For example, the proposal unit can optimize its proposal algorithm based on past proposal data. For example, the proposal unit can analyze past proposal data to improve the accuracy of its proposals. As a result, the accuracy of the proposal is improved by referring to relevant past proposal data. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input past proposal data into a generation AI and have the generation AI perform the task of improving the accuracy of the proposals.

[0103] The prediction unit can estimate the user's emotions and adjust the display method of the prediction based on the estimated user emotions. For example, if the user is nervous, the prediction unit can provide a simple and highly visible display method. For example, if the user is relaxed, the prediction unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the prediction unit can provide a display method that gets straight to the point. By adjusting the display method of the prediction according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0104] The prediction unit can optimize its prediction algorithm based on the latest data during the prediction process. For example, the prediction unit can optimize its weather forecasting algorithm based on the latest weather data. For example, the prediction unit can optimize its terrain analysis algorithm based on the latest terrain data. For example, the prediction unit can optimize its disaster risk assessment algorithm based on the latest disaster history data. By optimizing the prediction algorithm based on the latest data, the accuracy of the prediction is improved. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the latest data into a generating AI and have the generating AI perform the optimization of the prediction algorithm.

[0105] The prediction unit can improve the accuracy of its predictions by considering regional characteristics and seasonal variations during the prediction process. For example, the prediction unit can consider regional characteristics and focus on water level predictions in areas with a high risk of flooding. For example, the prediction unit can consider seasonal variations and enhance wind speed predictions during typhoon season. For example, the prediction unit can combine regional characteristics and seasonal variations to apply an optimal prediction algorithm. This improves the accuracy of predictions by considering regional characteristics and seasonal variations. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data on regional characteristics and seasonal variations into a generating AI and have the generating AI perform the task of improving prediction accuracy.

[0106] The prediction unit can estimate the user's emotions and prioritize prediction results based on the estimated emotions. For example, if the user is feeling anxious, the prediction unit can prioritize providing important prediction results. For example, if the user is relaxed, the prediction unit can provide detailed prediction results. For example, if the user is in a hurry, the prediction unit can quickly provide the necessary prediction results. This allows for the prioritization of important prediction results by determining the priority of prediction results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI or not using AI. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0107] The prediction unit can make optimal predictions by considering the geographical location information of the region. For example, based on geographical location information, the prediction unit can focus on water level predictions in areas with a high risk of flooding. For example, the prediction unit can enhance earthquake predictions in areas with a high risk of earthquakes by considering geographical location information. For example, the prediction unit can apply the optimal prediction algorithm based on geographical location information. This makes it possible to make highly relevant predictions by considering the geographical location information of the region. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the geographical location information of the region into a generating AI and have the generating AI perform the optimal prediction.

[0108] The prediction unit can improve the accuracy of its predictions by referring to relevant historical prediction data during the prediction process. For example, the prediction unit can refer to historical prediction data and apply the optimal prediction algorithm. For example, the prediction unit can optimize its prediction algorithm based on historical prediction data. For example, the prediction unit can analyze historical prediction data to improve the accuracy of its predictions. This improves the accuracy of predictions by referring to relevant historical prediction data. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input historical prediction data into a generating AI and have the generating AI perform the task of improving the accuracy of the predictions.

[0109] The instruction unit can estimate the user's emotions and adjust the way evacuation instructions are presented based on the estimated emotions. For example, if the user is tense, the instruction unit can provide simple and easily visible evacuation instructions. For example, if the user is relaxed, the instruction unit can provide detailed evacuation instructions. For example, if the user is in a hurry, the instruction unit can provide concise evacuation instructions. By adjusting the way evacuation instructions are presented according to the user's emotions, it becomes possible to provide evacuation instructions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0110] The instruction unit can generate optimal evacuation instructions by considering the attribute information of residents when issuing instructions. For example, the instruction unit can suggest a barrier-free evacuation route to the elderly. For example, the instruction unit can provide instructions to children to evacuate with their parents. For example, the instruction unit can suggest a barrier-free evacuation route to residents using wheelchairs. In this way, by generating evacuation instructions while considering the attribute information of residents, evacuation instructions suitable for each resident can be provided. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input resident attribute information into a generation AI and have the generation AI generate the optimal evacuation instructions.

[0111] The instruction unit can update information on evacuation routes and shelters in real time when issuing an instruction. For example, the instruction unit can update the congestion status of evacuation routes in real time and suggest the optimal route. For example, the instruction unit can update the capacity status of shelters in real time and suggest available shelters. For example, the instruction unit can update information on evacuation routes and shelters in real time and provide optimal evacuation instructions. By updating information on evacuation routes and shelters in real time, evacuation instructions based on the latest information can be provided. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input information on evacuation routes and shelters into a generating AI and have the generating AI perform real-time updates.

[0112] The instruction unit can estimate the user's emotions and determine the priority of evacuation instructions based on the estimated emotions. For example, if the user is feeling anxious, the instruction unit can prioritize important evacuation instructions. For example, if the user is relaxed, the instruction unit can provide detailed evacuation instructions. For example, if the user is in a hurry, the instruction unit can quickly provide necessary evacuation instructions. This ensures that important evacuation instructions are prioritized by determining the priority of evacuation instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using AI or not using AI. For example, the instruction unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0113] The instruction unit can issue optimal evacuation orders by considering the geographical location information of the region when issuing orders. For example, based on geographical location information, the instruction unit can propose the concentrated placement of water level sensors in areas with a high risk of flooding. For example, based on geographical location information, the instruction unit can propose the installation of additional seismometers in areas with a high risk of earthquakes. For example, based on geographical location information, the instruction unit can propose the optimal infrastructure development. By issuing evacuation orders while considering the geographical location information of the region, it becomes possible to issue highly relevant evacuation orders. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the geographical location information of the region into a generating AI and have the generating AI execute the optimal evacuation order.

[0114] The instruction unit can improve the accuracy of its instructions by referring to relevant past evacuation instruction data when issuing instructions. For example, the instruction unit can refer to past evacuation instruction data to issue the most appropriate evacuation instructions. For example, the instruction unit can optimize its instruction algorithm based on past evacuation instruction data. For example, the instruction unit can analyze past evacuation instruction data to improve the accuracy of its instructions. This improves the accuracy of instructions by referring to relevant past evacuation instruction data. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input past evacuation instruction data into a generating AI and have the generating AI perform the task of improving the accuracy of the instructions.

[0115] The simulation unit can estimate the user's emotions and adjust the simulation scenario based on the estimated emotions. For example, if the user is nervous, the simulation unit can provide a simple and easy-to-understand scenario. For example, if the user is relaxed, the simulation unit can provide a detailed scenario. For example, if the user is in a hurry, the simulation unit can provide a concise scenario. By adjusting the simulation scenario according to the user's emotions, a scenario that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0116] The simulation unit can generate scenarios that reflect regional characteristics and past disaster history during simulation. For example, the simulation unit can generate a water level rise scenario in areas with a high flood risk, reflecting regional characteristics. For example, the simulation unit can generate an earthquake scenario in areas with a high earthquake risk, based on past disaster history. For example, the simulation unit can generate an optimal simulation scenario by combining regional characteristics and past disaster history. This enables realistic simulations by generating scenarios that reflect regional characteristics and past disaster history. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input data on regional characteristics and past disaster history into a generation AI and have the generation AI execute the scenario generation.

[0117] The simulation unit can estimate the user's emotions and determine the priority of simulations based on the estimated user emotions. For example, if the user is feeling anxious, the simulation unit can prioritize providing important simulations. For example, if the user is relaxed, the simulation unit can provide detailed simulations. For example, if the user is in a hurry, the simulation unit can quickly provide the necessary simulations. This allows for the priority of important simulations by determining the priority of simulations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, for example, or not using AI. For example, the simulation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0118] The simulation unit can provide a realistic evacuation experience using VR technology during simulations. For example, the simulation unit can realistically recreate a flood evacuation experience using VR technology. For example, the simulation unit can realistically recreate an earthquake evacuation experience using VR technology. For example, the simulation unit can realistically recreate a fire evacuation experience using VR technology. By providing a realistic evacuation experience using VR technology, it is possible to improve residents' disaster prevention awareness. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input VR technology into a generating AI and have the generating AI perform the task of providing a realistic evacuation experience.

[0119] The visualization unit can estimate the user's emotions and adjust the visualization method based on the estimated emotions. For example, if the user is tense, the visualization unit can provide a simple and highly visible visualization method. For example, if the user is relaxed, the visualization unit can provide a visualization method that includes detailed information. For example, if the user is in a hurry, the visualization unit can provide a visualization method that gets straight to the point. By adjusting the visualization method according to the user's emotions, it becomes possible to create visualizations that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0120] The visualization unit can select the optimal visualization method when performing visualization, taking into account regional characteristics and the importance of the data. For example, the visualization unit can visualize water level rise in areas with a high flood risk, taking into account regional characteristics. For example, the visualization unit can highlight and visualize important data based on its importance. For example, the visualization unit can select the optimal visualization method by combining regional characteristics and data importance. This makes it possible to perform visualizations that are highly relevant by taking into account regional characteristics and data importance. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without using AI. For example, the visualization unit can input regional characteristics and data importance into a generating AI and have the generating AI select the optimal visualization method.

[0121] The visualization unit can estimate the user's emotions and determine visualization priorities based on the estimated user emotions. For example, if the user is feeling anxious, the visualization unit can prioritize the visualization of important data. For example, if the user is relaxed, the visualization unit can visualize detailed data. For example, if the user is in a hurry, the visualization unit can quickly visualize the data they need. This allows for the prioritization of visualization priorities based on the user's emotions, thereby prioritizing the visualization of important data. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the visualization unit may be performed using AI, or not using AI. For example, the visualization unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0122] The visualization unit can improve the accuracy of visualization by referring to relevant past visualization data during visualization. For example, the visualization unit can refer to past visualization data and apply the optimal visualization method. For example, the visualization unit can optimize the visualization algorithm based on past visualization data. For example, the visualization unit can analyze past visualization data to improve the accuracy of visualization. This improves the accuracy of visualization by referring to relevant past visualization data. Some or all of the above processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input past visualization data into a generating AI and have the generating AI perform the visualization accuracy improvement.

[0123] The training unit can estimate the user's emotions and adjust the training content based on the estimated emotions. For example, if the user is nervous, the training unit can provide simple and highly visual training content. For example, if the user is relaxed, the training unit can provide detailed training content. For example, if the user is in a hurry, the training unit can provide concise training content. By adjusting the training content according to the user's emotions, training that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0124] The training unit can generate an optimal training program during training, taking into account regional characteristics and resident attribute information. For example, the training unit can provide a training program for rising water levels in areas with a high risk of flooding, taking into account regional characteristics. For example, the training unit can provide a barrier-free training program for the elderly based on resident attribute information. For example, the training unit can generate an optimal training program by combining regional characteristics and resident attribute information. This allows for the provision of training tailored to each resident by generating a training program that takes into account regional characteristics and resident attribute information. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input regional characteristics and resident attribute information into a generating AI and have the generating AI generate an optimal training program.

[0125] The training unit can estimate the user's emotions and determine training priorities based on the estimated emotions. For example, if the user is feeling anxious, the training unit can prioritize important training. For example, if the user is relaxed, the training unit can provide detailed training. For example, if the user is in a hurry, the training unit can quickly provide the necessary training. This ensures that important training is prioritized by determining training priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the training unit may be performed using AI or not using AI. For example, the training unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0126] The training department can provide realistic evacuation drills using VR technology during training. For example, the training department can realistically recreate flood evacuation drills using VR technology. For example, the training department can realistically recreate earthquake evacuation drills using VR technology. For example, the training department can realistically recreate fire evacuation drills using VR technology. By providing realistic evacuation drills using VR technology, it is possible to improve residents' disaster prevention awareness. Some or all of the above processing in the training department may be performed using AI, for example, or without AI. For example, the training department can input VR technology into a generating AI and have the generating AI perform the provision of realistic evacuation drills.

[0127] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0128] The data collection unit can gather feedback from local residents and improve the accuracy of disaster risk maps. For example, the unit can collect detailed information on disasters that residents have experienced in the past and reflect it in the risk map. For example, the unit can collect information on problems and areas for improvement in evacuation routes that residents have noticed and use this to improve evacuation plans. For example, the unit can collect ideas for disaster prevention measures proposed by residents and reflect them in the system. This allows for the development of more practical and effective disaster prevention plans by utilizing resident feedback.

[0129] The simulation unit can simulate the economic impact of disasters. For example, it can simulate the impact on a local economy in the event of a flood. For example, it can simulate the costs of infrastructure restoration in the event of an earthquake. For example, it can simulate the impact of insurance payouts in the event of a fire. This allows for a prior understanding of the economic impact of disasters and the implementation of appropriate countermeasures.

[0130] The visualization unit can visualize the distribution of local disaster prevention resources in addition to disaster risk maps. For example, the visualization unit can display the location and capacity of evacuation shelters on a map. For example, the visualization unit can display the locations of fire stations and police stations to demonstrate their response capabilities in emergencies. For example, the visualization unit can display the location and equipment status of medical facilities to visualize their medical response capabilities during disasters. This allows for an understanding of the distribution of local disaster prevention resources and the development of effective disaster prevention plans.

[0131] The training department can provide disaster preparedness training that incorporates game elements to improve residents' disaster preparedness awareness. For example, the training department can help residents learn evacuation procedures through a game where they select an evacuation route. For example, the training department can provide a game where residents can learn how to respond to a disaster in a quiz format. For example, the training department can raise residents' disaster preparedness awareness by holding a contest to test their disaster preparedness knowledge. In this way, disaster preparedness training incorporating game elements can improve residents' disaster preparedness awareness in an enjoyable way.

[0132] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is stressed, the data collection unit can simplify the data collection method to reduce the burden. For example, if the user is relaxed, the data collection unit can perform detailed data collection to improve accuracy. For example, if the user is in a hurry, the data collection unit can quickly collect the necessary data and start analysis immediately. This allows for efficient data collection by adjusting the data collection method according to the user's emotions.

[0133] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most efficient collection time slot from past data collection history and collect data during that time slot. For example, the data collection unit can optimize the collection method (sensors, drones, etc.) based on past data collection history. For example, the data collection unit can analyze past data collection history and propose new collection methods to improve data accuracy. As a result, the efficiency of data collection is improved by selecting the optimal collection method based on past data collection history.

[0134] The data collection unit can filter data while considering regional characteristics and seasonal variations. For example, based on regional characteristics, the unit can concentrate water level sensors in areas with a high risk of flooding. For example, considering seasonal variations, the unit can add anemometers during typhoon season. For example, the unit can select the optimal data collection points by combining regional characteristics and seasonal variations. This allows for more accurate data to be obtained by collecting data while considering regional characteristics and seasonal variations.

[0135] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is feeling anxious, the unit can prioritize collecting important data to provide reassurance. For example, if the user is relaxed, the unit can collect detailed data to improve accuracy. For example, if the user is in a hurry, the unit can quickly collect the necessary data and start analysis immediately. This allows for the priority collection of important data by prioritizing data according to the user's emotions.

[0136] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the region during data collection. For example, based on geographical location information, the data collection unit can concentrate the placement of water level sensors in areas with a high risk of flooding. For example, based on geographical location information, the data collection unit can add more seismometers in areas with a high risk of earthquakes. For example, based on geographical location information, the data collection unit can select the optimal data collection points. In this way, by collecting data while considering the geographical location information of the region, highly relevant data can be collected efficiently.

[0137] The data collection unit can analyze social media and news information during data collection and collect relevant data. For example, the data collection unit can analyze social media posts to collect real-time information about disasters. For example, the data collection unit can prioritize the collection of data from areas with a high disaster risk based on news information. For example, the data collection unit can combine social media and news information to select the optimal data collection points. This allows for the efficient collection of real-time information by analyzing social media and news information and then collecting the data.

[0138] The following briefly describes the processing flow for example form 2.

[0139] Step 1: The data collection unit collects data such as topographic data, weather information, past disaster history, and infrastructure status. For example, topographic data includes elevation data and geological data, and weather information includes precipitation, wind speed, and temperature. Past disaster history includes the types of disasters that have occurred in the past, their locations, and the extent of the damage, while infrastructure status includes the current state and durability of infrastructure such as roads, bridges, and dams. Step 2: The analysis department comprehensively analyzes the data collected by the data collection department and creates detailed disaster risk maps for each region. For example, a database is used to centrally manage the data as a data integration method, and analysis is performed using statistical analysis and machine learning algorithms. The disaster risk maps are created based on evaluation criteria such as flood risk, earthquake risk, and landslide risk. Step 3: The proposal department proposes optimal evacuation plans and infrastructure development priorities based on the analysis results obtained by the analysis department. For example, they will consider demographic trends and regional characteristics when determining the placement of evacuation shelters and evacuation routes. For infrastructure development priorities, they will propose prioritizing infrastructure development in areas with a high disaster risk. Step 4: The prediction unit predicts disasters in real time based on the latest data. For example, it predicts typhoon paths based on weather data and assesses flood risk. Step 5: The instruction unit generates the most appropriate evacuation instructions based on each resident's situation. For example, it suggests a barrier-free evacuation route for a resident using a wheelchair.

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0142] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, prediction unit, instruction unit, simulation unit, visualization unit, and training unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and sensors of the smart device 14 and analyzes it comprehensively by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a disaster risk map based on the collected data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal evacuation plan and infrastructure development priorities based on the analysis results. The prediction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and predicts disasters in real time. The instruction unit is implemented by, for example, the control unit 46A of the smart device 14 and provides optimal evacuation instructions to each resident. The simulation unit performs disaster simulations using, for example, the VR technology of the smart device 14. The visualization unit visually displays a disaster risk map using, for example, the display 40A of the smart device 14. The training unit conducts disaster prevention training using, for example, the VR technology of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0144] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0145] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, prediction unit, instruction unit, simulation unit, visualization unit, and training unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and sensors of the smart glasses 214 and analyzes it comprehensively by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a disaster risk map based on the collected data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal evacuation plan and infrastructure development priorities based on the analysis results. The prediction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and predicts disasters in real time. The instruction unit is implemented by, for example, the control unit 46A of the smart glasses 214 and provides optimal evacuation instructions to each resident. The simulation unit performs disaster simulations using, for example, the VR technology of the smart glasses 214. The visualization unit, for example, visually displays a disaster risk map using the display of the smart glasses 214. The training unit, for example, conducts disaster prevention training using the VR technology of the smart glasses 214. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0160] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0161] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, prediction unit, instruction unit, simulation unit, visualization unit, and training unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and sensors of the headset terminal 314 and analyzes it comprehensively by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a disaster risk map based on the collected data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal evacuation plan and infrastructure development priorities based on the analysis results. The prediction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and predicts disasters in real time. The instruction unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides optimal evacuation instructions to each resident. The simulation unit performs disaster simulations using, for example, the VR technology of the headset terminal 314. The visualization unit, for example, visually displays a disaster risk map using the display of the headset terminal 314. The training unit, for example, conducts disaster prevention training using the VR technology of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0176] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0177] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0178] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0179] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0180] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0181] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0182] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0183] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0184] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0185] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0186] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0187] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0188] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0189] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0190] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0191] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or external devices, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or external devices.

[0192] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, prediction unit, instruction unit, simulation unit, visualization unit, and training unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and sensors of the robot 414 and analyzes it comprehensively by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a disaster risk map based on the collected data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal evacuation plan and infrastructure development priorities based on the analysis results. The prediction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and predicts disasters in real time. The instruction unit is implemented by, for example, the control unit 46A of the robot 414 and provides optimal evacuation instructions to each resident. The simulation unit performs disaster simulations using, for example, the VR technology of the robot 414. The visualization unit visually displays the disaster risk map using, for example, the display of the robot 414. The training department, for example, conducts disaster prevention training using the VR technology of robot 414. The correspondence between each department and the devices and control units is not limited to the example described above and can be modified in various ways.

[0193] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0194] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0195] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0196] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0197] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0198] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0199] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0200] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0201] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0202] 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.

[0203] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0204] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0205] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0206] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0207] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0208] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0209] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0210] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0211] (Note 1) The data collection unit collects data such as topographic data, weather information, past disaster history, and infrastructure status. The analysis unit comprehensively analyzes the data collected by the aforementioned collection unit and creates detailed disaster risk maps for each region. Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal evacuation plan and priority for infrastructure development. A prediction unit that predicts disasters in real time based on the latest data, It includes an instruction unit that generates the optimal evacuation order according to the situation of each resident. A system characterized by the following features. (Note 2) It is equipped with a simulation unit that performs disaster simulations using VR technology. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a visualization unit that visualizes the safety of the area. The system described in Appendix 1, characterized by the features described herein. (Note 4) It has a training department to improve residents' disaster preparedness awareness. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting data, filtering is performed taking into account regional characteristics and seasonal variations. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the geographical location information of the region. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, social media and news information are analyzed to gather relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, we refer to relevant literature and research data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of evacuation plans and infrastructure development. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the characteristics and demographics of the region. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, we will consider the geographical location information of the region to provide the most suitable proposal. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, we refer to relevant past proposal data to improve the accuracy of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, It estimates the user's emotions and adjusts how predictions are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The prediction unit, When making a prediction, optimize the prediction algorithm based on the latest data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The prediction unit, When making predictions, we improve the accuracy of the forecast by taking into account regional characteristics and seasonal variations. The system described in Appendix 1, characterized by the features described herein. (Note 26) The prediction unit, It estimates the user's emotions and prioritizes the prediction results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The prediction unit, When making predictions, the optimal prediction is made by considering the geographical location information of the region. The system described in Appendix 1, characterized by the features described herein. (Note 28) The prediction unit, When making predictions, we refer to relevant historical prediction data to improve prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 29) The indicator unit is, The system estimates the user's emotions and adjusts the way evacuation orders are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The indicator unit is, When issuing instructions, the system generates optimal evacuation orders by considering the attribute information of the residents. The system described in Appendix 1, characterized by the features described herein. (Note 31) The indicator unit is, When issuing instructions, information on evacuation routes and shelters will be updated in real time. The system described in Appendix 1, characterized by the features described herein. (Note 32) The indicator unit is, The system estimates the user's emotions and determines the priority of evacuation orders based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The indicator unit is, When issuing instructions, the most appropriate evacuation orders will be given, taking into account the geographical location information of the area. The system described in Appendix 1, characterized by the features described herein. (Note 34) The indicator unit is, When issuing instructions, we refer to relevant past evacuation order data to improve the accuracy of the instructions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation scenario based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned simulation unit, During the simulation, scenarios are generated that reflect the characteristics of the region and its past disaster history. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned simulation unit, It estimates the user's emotions and determines the priority of simulations based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned simulation unit, During the simulation, VR technology is used to provide a realistic evacuation experience. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned visualization unit, It estimates the user's emotions and adjusts the visualization method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned visualization unit, When visualizing data, the optimal visualization method is selected considering regional characteristics and the importance of the data. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned visualization unit, It estimates the user's emotions and determines the visualization priority based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned visualization unit, When visualizing, we improve the accuracy of the visualization by referring to relevant historical visualization data. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned training unit, It estimates the user's emotions and adjusts the training content based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned training unit, During training, the system generates an optimal training program that takes into account regional characteristics and resident demographic information. The system described in Appendix 4, characterized by the features described herein. (Note 45) The aforementioned training unit, It estimates the user's emotions and determines training priorities based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned training unit, During training, VR technology is used to provide realistic evacuation drills. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0212] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection unit collects data such as topographic data, weather information, past disaster history, and infrastructure status. The analysis unit comprehensively analyzes the data collected by the aforementioned collection unit and creates detailed disaster risk maps for each region. Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal evacuation plan and priority for infrastructure development. A prediction unit that predicts disasters in real time based on the latest data, It includes an instruction unit that generates the optimal evacuation order according to the situation of each resident. A system characterized by the following features.

2. It is equipped with a simulation unit that performs disaster simulations using VR technology. The system according to feature 1.

3. It is equipped with a visualization unit that visualizes the safety of the area. The system according to feature 1.

4. It has a training department to improve residents' disaster preparedness awareness. The system according to feature 1.

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

6. The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system according to feature 1.

7. The aforementioned collection unit is When collecting data, filtering is performed taking into account regional characteristics and seasonal variations. The system according to feature 1.

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

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