system

Abstract

The system according to this embodiment aims to optimize resource allocation during disasters and facilitate seamless communication between multiple organizations. [Solution] The system according to the embodiment comprises a data collection unit, a predictive analysis unit, a resource allocation unit, a communication unit, and a citizen safety network unit. The data collection unit collects information from multiple data sources. The predictive analysis unit analyzes the information collected by the data collection unit and predicts the impact of disasters and the need for resources. The resource allocation unit optimizes the allocation of resources based on the prediction results obtained by the predictive analysis unit. The communication unit facilitates seamless communication between multiple agencies. The citizen safety network unit provides citizens with real-time updates and individual safety instructions.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the distribution of resources during disasters and the communication between multiple institutions are not fully optimized, and there is room for improvement.

[0005] The system according to the embodiment aims to optimize the distribution of resources during disasters and promote seamless communication between multiple institutions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a predictive analysis unit, a resource allocation unit, a communication unit, and a citizen safety network unit. The data collection unit collects information from multiple data sources. The predictive analysis unit analyzes the information collected by the data collection unit and predicts the impact of disasters and the need for resources. The resource allocation unit optimizes resource allocation based on the prediction results obtained by the predictive analysis unit. The communication unit facilitates seamless communication between multiple agencies. The citizen safety network unit provides citizens with real-time updates and individual safety instructions. [Effects of the Invention]

[0007] The system according to this embodiment can optimize resource allocation during disasters and facilitate seamless communication between multiple organizations. [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 manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a 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) An AI-Powered Disaster Response Coordinator, according to an embodiment of the present invention, is an advanced system designed to revolutionize disaster management. This system integrates real-time data from multiple data sources and leverages state-of-the-art AI algorithms to optimize resource allocation, enhance communication, and significantly improve response times during natural disasters such as earthquakes, tsunamis, and typhoons. This solution aims to protect lives and minimize the economic impact on Japan, a disaster-prone region, by utilizing advanced technology. For example, the AI-Powered Disaster Response Coordinator collects information from satellites, IoT sensors, social media, and official channels. Next, the AI-Powered Disaster Response Coordinator uses AI to predict the impact of a disaster and the need for resources. Furthermore, based on the predictions, the AI-Powered Disaster Response Coordinator optimizes the allocation of emergency response personnel and supplies. The AI-Powered Disaster Response Coordinator facilitates seamless communication between multiple agencies. Additionally, the AI-Powered Disaster Response Coordinator provides citizens with real-time updates and personalized safety instructions. The AI-Powered Disaster Response Coordinator provides multilingual support to assist foreign residents. It generates scenarios and creates simulations for preparedness planning. It analyzes emergency communications and social media using natural language processing to perform real-time situation assessments. It processes satellite and drone imagery using computer vision to assess damage and direct rescue operations.The AI-Powered Disaster Response Coordinator predicts the impact of a disaster and the need for resources based on current data and historical patterns. This allows the AI-Powered Disaster Response Coordinator to minimize the impact of a disaster and enable a rapid and effective response.

[0029] The AI-Powered Disaster Response Coordinator according to this embodiment comprises a data collection unit, a predictive analysis unit, a resource allocation unit, a communication unit, and a civil safety network unit. The data collection unit collects information from multiple data sources. For example, the data collection unit can collect information from satellites, IoT sensors, social media, and official channels. For example, the data collection unit can acquire data from satellites in real time and monitor the situation of a disaster. The data collection unit can also collect earthquake vibration data and tsunami water level data using IoT sensors. Furthermore, the data collection unit can also collect information related to disasters by analyzing posts from social media. For example, the data collection unit can collect posts related to disasters on social media in real time to understand the situation of a disaster. It also collects information from official channels and takes disaster response actions based on official announcements from the government and local authorities. The predictive analysis unit analyzes the information collected by the data collection unit and predicts the impact of the disaster and the need for resources. For example, the predictive analysis unit can use AI to predict the epicenter of an earthquake and the arrival time of a tsunami. The predictive analysis unit can also use AI to predict the extent of the disaster's impact and the degree of damage. For example, the Predictive Analysis Department predicts the extent of damage based on the distance from the earthquake's epicenter and topographical information. The Resource Allocation Department optimizes resource allocation based on the prediction results obtained by the Predictive Analysis Department. The Resource Allocation Department can optimize the allocation of emergency response personnel and supplies, for example. The Resource Allocation Department can also use AI to optimize the deployment of emergency response personnel and the allocation of supplies. For example, the Resource Allocation Department makes optimal deployments based on the extent of damage and the number of emergency response personnel. The Communications Department facilitates seamless communication between multiple agencies. For example, the Communications Department can facilitate information sharing between disaster response agencies. The Communications Department can also use AI to optimize information sharing between disaster response agencies. For example, the Communications Department can share information between disaster response agencies in real time, enabling a rapid response. The Citizen Safety Network Department provides citizens with real-time updates and individual safety instructions. For example, the Citizen Safety Network Department can issue evacuation orders to citizens in the event of a disaster.The Citizen Safety Network Department can also use AI to provide citizens with optimal evacuation instructions. For example, the Citizen Safety Network Department can provide the optimal evacuation route based on citizens' location information. As a result, the AI-Powered Disaster Response Coordinator according to this embodiment minimizes the impact of disasters and enables a rapid and effective response.

[0030] The data collection unit gathers information from multiple data sources. For example, it can collect information from satellites, IoT sensors, social media, and official channels. Specifically, it acquires data from satellites in real time to monitor the situation of disasters. Satellite data provides high-resolution images and weather data, enabling rapid detection of natural disasters such as earthquakes, tsunamis, and floods. The data collection unit can also collect earthquake vibration data and tsunami water level data using IoT sensors. These sensors include seismometers, water level gauges, and temperature sensors, and collect data in real time, transmitting it to a central database. Furthermore, the data collection unit can also collect disaster-related information by analyzing posts from social media. For example, the data collection unit collects disaster-related posts on social media in real time to understand the situation of the disaster. This includes using natural language processing technology to analyze the content of posts and extract keywords and location information related to the disaster. Information from official channels is also collected, and disaster response is carried out based on official announcements from governments and local authorities. As a result, the data collection unit can collect a wide range of information from diverse data sources and understand the situation in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the predictive analytics unit and the resource allocation unit. Adjusting the data collection frequency and accuracy also allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The Predictive Analysis Department analyzes information collected by the Data Collection Department to predict the impact of disasters and the need for resources. For example, the Predictive Analysis Department can use AI to predict the epicenter of earthquakes and the arrival time of tsunamis. Specifically, the AI ​​analyzes seismic wave data to identify the location of the epicenter and the magnitude of the earthquake. It also performs tsunami simulations to predict the arrival time and affected area of ​​tsunamis. Furthermore, the Predictive Analysis Department can also use AI to predict the extent of the disaster's impact and the degree of damage. For example, the Predictive Analysis Department predicts the degree of damage based on the distance from the earthquake's epicenter and topographic information. This includes methods that utilize past disaster data and geographic information systems (GIS) to analyze damage patterns. The Predictive Analysis Department can analyze collected data in real time and provide prediction results quickly. Furthermore, the Predictive Analysis Department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past earthquake data, it can predict fluctuations in risk in specific regions and time periods and formulate future countermeasures. Furthermore, the predictive analytics unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early warnings. This allows the predictive analytics unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The Resource Allocation Unit optimizes resource allocation based on prediction results obtained by the Predictive Analytics Unit. For example, the Resource Allocation Unit can optimize the allocation of emergency response personnel and supplies. Specifically, it uses AI to optimize the deployment of emergency response personnel and the allocation of supplies. For instance, the Resource Allocation Unit makes optimal deployments based on the extent of damage and the number of emergency response personnel. This includes methods for integrating damage prediction data and resource inventory information to formulate an optimal allocation plan. The Resource Allocation Unit can continuously adjust resource allocation based on real-time updated data. For example, if the damage situation changes, the Resource Allocation Unit immediately incorporates new data and updates the allocation plan. Furthermore, the Resource Allocation Unit can perform more accurate resource allocation by considering regional characteristics and past disaster history. This allows the Resource Allocation Unit to always provide highly accurate resource allocation based on the latest information, supporting a rapid and appropriate response. In addition, the Resource Allocation Unit can collaborate with other departments and agencies to promote the efficient use of resources. For example, it can share information with other disaster response agencies to prevent resource duplication and shortages. Furthermore, the resource allocation unit can handle not only emergency resource allocation but also normal resource management and stockpiling planning, thereby improving the overall efficiency and sustainability of the system.

[0033] The Ministry of Communications facilitates seamless communication between multiple agencies. For example, it can streamline information sharing among disaster response agencies. Specifically, it uses AI to optimize information sharing among disaster response agencies. For instance, the Ministry of Communications can share information among disaster response agencies in real time, enabling rapid response. This includes functions such as automatic data aggregation and filtering, and prioritization of critical information. The Ministry of Communications can absorb differences in protocols and formats between different agencies and provide a unified information sharing platform. Furthermore, the Ministry of Communications strengthens collaboration among disaster response agencies and promotes joint responses. For example, it provides collaborative workspaces and real-time chat functions to support rapid decision-making. In addition, the Ministry of Communications can collaborate not only with disaster response agencies but also with local residents and volunteer groups to promote information sharing and cooperation. This allows the Ministry of Communications to improve the efficiency and effectiveness of disaster response. Moreover, the Ministry of Communications can analyze data collected during the disaster response process and identify areas for future improvement. For example, based on data from past disaster responses, it can identify communication bottlenecks and challenges and propose solutions. This allows the Ministry of Communications to continuously improve the quality of disaster response.

[0034] The Citizen Safety Network Department provides citizens with real-time updates and individual safety instructions. For example, it can issue evacuation orders to citizens during a disaster. Specifically, it uses AI to provide optimal evacuation instructions to citizens. For instance, it provides optimal evacuation routes based on citizens' location information. This includes calculating evacuation routes that take into account real-time traffic information and the progression of the disaster. The Citizen Safety Network Department can provide information to citizens quickly and reliably through smartphone apps, SMS, and voice calls. Furthermore, it can collect feedback from citizens to continuously improve the accuracy and effectiveness of evacuation orders. For example, it can revise evacuation routes and improve instruction content based on feedback from citizens who have received evacuation orders. The Citizen Safety Network Department can also provide more accurate evacuation orders by considering regional characteristics and past disaster history. This allows the Citizen Safety Network Department to provide citizens with quick and appropriate evacuation instructions, minimizing the risk of disaster. Additionally, the Citizen Safety Network Department can provide information to citizens even during the preparation phase before a disaster occurs, promoting disaster preparedness. For example, if the likelihood of a disaster increases, a notification will be sent in advance urging people to prepare for evacuation. This allows the Citizen Safety Network Department to ensure the safety of citizens at all stages of disaster response and support a swift and effective response.

[0035] The data collection unit can collect information from satellites, IoT sensors, social media, and official channels. For example, the data collection unit can acquire data from satellites in real time and monitor the situation of a disaster. The data collection unit can also collect earthquake vibration data and tsunami water level data using IoT sensors. For example, the data collection unit can also collect information about a disaster by analyzing posts from social media. For example, the data collection unit can collect posts about a disaster on social media in real time to understand the situation of the disaster. The data collection unit can also collect information from official channels and respond to disasters based on official announcements from the government and local authorities. This allows for more comprehensive disaster information to be obtained by collecting information from diverse data sources. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data from satellites into AI, which can then analyze the data to monitor the situation of a disaster.

[0036] The predictive analysis unit can use AI to predict the impact of a disaster and the need for resources. For example, the predictive analysis unit can use AI to predict the epicenter of an earthquake and the arrival time of a tsunami. For example, the predictive analysis unit can also use AI to predict the extent of the impact and the degree of damage from a disaster. For example, the predictive analysis unit predicts the degree of damage based on the distance from the earthquake's epicenter and topographical information. This allows for highly accurate prediction of the impact of a disaster and the need for resources by using AI. Some or all of the above-described processes in the predictive analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the predictive analysis unit can input data from the data collection unit into the generative AI, which can then analyze the data to predict the impact of a disaster and the need for resources.

[0037] The resource allocation unit can optimize the allocation of emergency response personnel and supplies based on prediction results. For example, the resource allocation unit can optimize the allocation of emergency response personnel and supplies. The resource allocation unit can also optimize the deployment of emergency response personnel and supplies using AI, for example. For example, the resource allocation unit makes optimal deployments based on the extent of damage and the number of emergency response personnel. This enables rapid and effective disaster response by optimizing resource allocation based on prediction results. Some or all of the above-described processes in the resource allocation unit may be performed using, for example, a generating AI, or without a generating AI. For example, the resource allocation unit can input data from the predictive analysis unit into a generating AI, which can then analyze the data to optimize resource allocation.

[0038] The Communications Department can facilitate seamless communication between multiple agencies. For example, it can facilitate information sharing between disaster response agencies. The Communications Department can also optimize information sharing between disaster response agencies using AI. For example, it can share information between disaster response agencies in real time, enabling a rapid response. This improves the efficiency of disaster response by facilitating seamless communication between multiple agencies. Some or all of the above-described processes in the Communications Department may be performed using, for example, generative AI, or not using generative AI. For example, the Communications Department can input information between disaster response agencies into a generative AI, which can then analyze the information to facilitate optimal communication.

[0039] The Citizen Safety Network Department can provide citizens with real-time updates and individual safety instructions. For example, the Citizen Safety Network Department can issue evacuation instructions to citizens in the event of a disaster. The Citizen Safety Network Department can also use AI to provide citizens with optimal evacuation instructions. For example, the Citizen Safety Network Department can provide optimal evacuation routes based on citizens' location information. This ensures the safety of citizens by providing them with real-time updates and individual safety instructions. Some or all of the above-described processes in the Citizen Safety Network Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Citizen Safety Network Department can input citizens' location information into a generative AI, which can then provide optimal evacuation routes.

[0040] The Citizen Safety Network Department can provide multilingual support to assist foreign residents. For example, the Citizen Safety Network Department can automatically set the language of safety instructions based on the language settings of the user's device. The Citizen Safety Network Department can also provide a language switching function if the user uses multiple languages. For example, if the Citizen Safety Network Department selects a specific language, it can provide safety instructions in that language. By providing multilingual support, foreign residents can also receive appropriate safety instructions. Some or all of the above processes in the Citizen Safety Network Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Citizen Safety Network Department can input the user's language settings into a generative AI, which can then provide safety instructions in the most appropriate language.

[0041] The predictive analysis unit can generate scenarios and create simulations for disaster preparedness planning. For example, the predictive analysis unit can use AI to generate scenarios for disaster occurrences and create simulations for disaster preparedness planning. For example, the predictive analysis unit can generate scenarios for earthquake occurrences and simulate evacuation plans. For example, the predictive analysis unit can generate scenarios for typhoon approaching and simulate evacuation plans. By generating scenarios and performing simulations, disaster preparedness plans can be effectively formulated. Some or all of the above-described processes in the predictive analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the predictive analysis unit can input disaster occurrence data into a generation AI, which can then generate scenarios and perform simulations.

[0042] The predictive analytics unit can analyze emergency communications and social media using natural language processing to perform real-time situation assessments. For example, the predictive analytics unit can use natural language processing to analyze the content of emergency communications and assess the disaster situation. The predictive analytics unit can also analyze social media posts to collect information about the disaster and perform real-time situation assessments. For example, the predictive analytics unit can analyze the content of emergency communications and assess the extent of the damage. In this way, real-time situation assessments can be performed from emergency communications and social media using natural language processing. Some or all of the above processing in the predictive analytics unit may be performed using, for example, generative AI, or without generative AI. For example, the predictive analytics unit can input emergency communication data into a generative AI, which can then analyze the data to perform real-time situation assessments.

[0043] The predictive analysis unit can process satellite and drone images using computer vision to assess damage and direct rescue operations. For example, the predictive analysis unit can use computer vision to analyze satellite images and assess the extent of damage. The predictive analysis unit can also analyze drone images and assess the extent of damage. For example, the predictive analysis unit can combine satellite and drone images to perform a detailed assessment of the damage. This allows for the assessment of damage from satellite and drone images using computer vision and the effective direction of rescue operations. Some or all of the processing described above in the predictive analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the predictive analysis unit can input satellite and drone image data into a generative AI, which can then analyze the data to assess damage and direct rescue operations.

[0044] The predictive analysis unit can predict the impact of a disaster and the need for resources based on current data and past patterns. For example, the predictive analysis unit can predict the extent of an earthquake's impact based on current data. The predictive analysis unit can also predict the degree of damage based on past earthquake data. For example, the predictive analysis unit can predict the impact of a disaster and the need for resources with high accuracy by combining current data and past patterns. This allows for high-accuracy prediction of the impact of a disaster and the need for resources based on current data and past patterns. Some or all of the above processing in the predictive analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the predictive analysis unit can input current data and past patterns into a generative AI, which can analyze the data to predict the impact of a disaster and the need for resources.

[0045] The data collection unit can optimize its data collection method by referring to past disaster data during data collection. For example, the data collection unit can prioritize collecting data around the epicenter based on past earthquake data. For example, the data collection unit can focus on collecting wind speed and rainfall data based on past typhoon data. For example, the data collection unit can prioritize collecting coastal water level data based on past tsunami data. This allows the data collection method to be optimized by referring to past disaster data. Some or all of the above processing in the data collection unit may be performed using, for example, a generation AI, or without a generation AI. For example, the data collection unit can input past disaster data into a generation AI, and the generation AI can analyze the data to optimize the data collection method.

[0046] The data collection unit can apply different collection algorithms depending on the type of data being collected. For example, when collecting weather data, the data collection unit can apply a real-time weather forecasting algorithm. For example, when collecting social media data, the data collection unit can apply a natural language processing algorithm. For example, when collecting satellite data, the data collection unit can apply an image analysis algorithm. By applying different collection algorithms depending on the type of data being collected, the accuracy of data collection is improved. Some or all of the above-described processing in the data collection unit may be performed using, for example, generative AI, or without using generative AI. For example, the data collection unit can have the generative AI apply an appropriate algorithm depending on the type of data being collected.

[0047] The data collection unit can adjust its collection range by considering geographical information during data collection. For example, when an earthquake occurs, the data collection unit can prioritize collecting data around the epicenter. For example, when a typhoon approaches, the data collection unit can focus on collecting data in areas along its path. For example, when a tsunami warning is issued, the data collection unit can prioritize collecting data in coastal areas. By adjusting the collection range by considering geographical information, more effective data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input geographical information into a generative AI, and the generative AI can analyze the data and adjust the collection range.

[0048] The data collection unit can analyze social media trends during data collection and prioritize the collection of relevant data. For example, the data collection unit can prioritize the collection of data from areas that are trending on social media. For example, the data collection unit can focus on collecting data from areas with a high number of posts related to emergencies. For example, the data collection unit can prioritize the collection of images and videos shared on social media. This allows for the priority collection of relevant data by analyzing social media trends. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input social media trend data into a generative AI, which can then analyze the data and prioritize the collection of relevant data.

[0049] The predictive analysis unit can optimize its prediction algorithm by referring to past disaster patterns during predictive analysis. For example, the predictive analysis unit can optimize an algorithm to predict the impact around the epicenter based on past earthquake data. For example, the predictive analysis unit can optimize an algorithm to predict the impact of wind speed and rainfall based on past typhoon data. For example, the predictive analysis unit can optimize an algorithm to predict the impact on coastal areas based on past tsunami data. In this way, the prediction algorithm can be optimized by referring to past disaster patterns. Some or all of the above processing in the predictive analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the predictive analysis unit can input past disaster data into a generative AI, and the generative AI can analyze the data to optimize the prediction algorithm.

[0050] The predictive analysis unit can apply different prediction models depending on the type of disaster during predictive analysis. For example, when an earthquake occurs, the predictive analysis unit applies a prediction model specifically for earthquakes. For example, when a typhoon approaches, the predictive analysis unit can apply a prediction model specifically for typhoons. For example, when a tsunami warning is issued, the predictive analysis unit applies a prediction model specifically for tsunamis. This allows the application of the optimal prediction model according to the type of disaster. Some or all of the above-described processes in the predictive analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the predictive analysis unit can input data according to the type of disaster into a generative AI, and the generative AI can analyze the data and apply the optimal prediction model.

[0051] The predictive analysis unit can adjust the prediction range by taking geographical information into consideration during predictive analysis. For example, when an earthquake occurs, the predictive analysis unit can prioritize adjusting the prediction range around the epicenter. For example, when a typhoon approaches, the predictive analysis unit can adjust the prediction range for areas along its path. For example, when a tsunami warning is issued, the predictive analysis unit can prioritize adjusting the prediction range for coastal areas. By adjusting the prediction range by taking geographical information into consideration, more effective predictions become possible. Some or all of the above processing in the predictive analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the predictive analysis unit can input geographical information into a generative AI, and the generative AI can analyze the data and adjust the prediction range.

[0052] The predictive analytics unit can analyze social media data during predictive analysis to perform real-time situation assessments. For example, the predictive analytics unit can assess the situation in a region that is trending on social media in real time. For example, the predictive analytics unit can assess the situation in a region that has many posts related to emergencies in real time. For example, the predictive analytics unit can analyze image and video data shared on social media to assess the situation in real time. This makes real-time situation assessment possible by analyzing social media data. Some or all of the above processing in the predictive analytics unit may be performed using, for example, a generative AI, or without a generative AI. For example, the predictive analytics unit can input social media data into a generative AI, and the generative AI can analyze the data to perform real-time situation assessments.

[0053] The resource allocation unit can optimize the allocation method by referring to past resource allocation data when allocating resources. For example, the resource allocation unit can optimize resource allocation around the epicenter based on resource allocation data from past earthquakes. For example, the resource allocation unit can optimize resource allocation in areas along the path of typhoons based on resource allocation data from past typhoons. For example, the resource allocation unit can optimize resource allocation in coastal areas based on resource allocation data from past tsunamis. In this way, the allocation method can be optimized by referring to past resource allocation data. Some or all of the above processing in the resource allocation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the resource allocation unit can input past resource allocation data into a generation AI, and the generation AI can analyze the data to optimize the allocation method.

[0054] The resource allocation unit can apply different allocation algorithms depending on the type of disaster when allocating resources. For example, when an earthquake occurs, the resource allocation unit applies an allocation algorithm specifically for earthquakes. For example, when a typhoon approaches, the resource allocation unit can apply an allocation algorithm specifically for typhoons. For example, when a tsunami warning is issued, the resource allocation unit applies an allocation algorithm specifically for tsunamis. This allows the application of the optimal allocation algorithm according to the type of disaster. Some or all of the above-described processes in the resource allocation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the resource allocation unit can input data according to the type of disaster into a generation AI, and the generation AI can analyze the data and apply the optimal allocation algorithm.

[0055] The resource allocation unit can adjust the allocation range when allocating resources, taking geographical information into consideration. For example, when an earthquake occurs, the resource allocation unit can prioritize adjusting the resource allocation range around the epicenter. For example, when a typhoon approaches, the resource allocation unit can adjust the resource allocation range for areas along its path. For example, when a tsunami warning is issued, the resource allocation unit can prioritize adjusting the resource allocation range for coastal areas. By adjusting the allocation range while considering geographical information, more effective resource allocation becomes possible. Some or all of the above processing in the resource allocation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the resource allocation unit can input geographical information into a generative AI, and the generative AI can analyze the data and adjust the allocation range.

[0056] The resource allocation unit can analyze social media data to assess real-time resource needs when allocating resources. For example, the resource allocation unit can assess the resource needs of areas that are trending on social media in real time. For example, the resource allocation unit can assess the resource needs of areas with many posts related to emergencies in real time. For example, the resource allocation unit can analyze image and video data shared on social media to assess real-time resource needs. In this way, real-time resource needs can be assessed by analyzing social media data. Some or all of the above processing in the resource allocation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the resource allocation unit can input social media data into a generative AI, and the generative AI can analyze the data to assess real-time resource needs.

[0057] The communications unit can optimize the communication method by referring to past communication data during communication. For example, the communications unit can optimize the communication method around the epicenter based on communication data from past earthquakes. For example, the communications unit can optimize the communication method for areas along the path of a typhoon based on communication data from past typhoons. For example, the communications unit can optimize the communication method for coastal areas based on communication data from past tsunamis. In this way, the communications method can be optimized by referring to past communication data. Some or all of the above processing in the communications unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the communications unit can input past communication data into a generative AI, and the generative AI can analyze the data and optimize the communication method.

[0058] The communications unit can apply different communication protocols depending on the type of disaster during communication. For example, when an earthquake occurs, the communications unit applies a communication protocol specifically for earthquakes. For example, when a typhoon approaches, the communications unit can apply a communication protocol specifically for typhoons. For example, when a tsunami warning is issued, the communications unit applies a communication protocol specifically for tsunamis. This allows the communications unit to apply the most appropriate communication protocol according to the type of disaster. Some or all of the above-described processing in the communications unit may be performed using, for example, a generation AI, or without a generation AI. For example, the communications unit can input data according to the type of disaster into a generation AI, and the generation AI can analyze the data and apply the most appropriate communication protocol.

[0059] The communications unit can adjust its communication range by taking geographical information into consideration during communication. For example, when an earthquake occurs, the communications unit will prioritize adjusting the communication range around the epicenter. For example, when a typhoon approaches, the communications unit can adjust the communication range for areas along its path. For example, when a tsunami warning is issued, the communications unit will prioritize adjusting the communication range for coastal areas. By adjusting the communication range by taking geographical information into consideration, more effective communication becomes possible. Some or all of the above processing in the communications unit may be performed using, for example, a generative AI, or without a generative AI. For example, the communications unit can input geographical information into a generative AI, and the generative AI can analyze the data and adjust the communication range.

[0060] The Ministry of Communications can analyze social media data during communication to assess real-time communication needs. For example, the Ministry can assess the communication needs of areas that are trending on social media in real time. For example, the Ministry can assess the communication needs of areas with many posts about emergencies in real time. For example, the Ministry can analyze image and video data shared on social media to assess real-time communication needs. In this way, real-time communication needs can be assessed by analyzing social media data. Some or all of the above processing by the Ministry of Communications may be performed using, for example, generative AI, or without generative AI. For example, the Ministry of Communications can input social media data into a generative AI, and the generative AI can analyze the data to assess real-time communication needs.

[0061] The Citizen Safety Network Department can optimize its instructions when issuing safety instructions by referring to past disaster data. For example, the Citizen Safety Network Department can optimize instructions around the epicenter based on safety instruction data from past earthquakes. For example, the Citizen Safety Network Department can optimize instructions for areas along the path of a typhoon based on safety instruction data from past typhoons. For example, the Citizen Safety Network Department can optimize instructions for coastal areas based on safety instruction data from past tsunamis. In this way, instructions can be optimized by referring to past disaster data. Some or all of the above processing in the Citizen Safety Network Department may be performed using, for example, a generating AI, or without using a generating AI. For example, the Citizen Safety Network Department can input past disaster data into a generating AI, and the generating AI can analyze the data to optimize instructions.

[0062] The Citizen Safety Network Unit can apply different instruction algorithms depending on the type of disaster when issuing safety instructions. For example, when an earthquake occurs, the Citizen Safety Network Unit applies an instruction algorithm specifically for earthquakes. For example, when a typhoon approaches, the Citizen Safety Network Unit can apply an instruction algorithm specifically for typhoons. For example, when a tsunami warning is issued, the Citizen Safety Network Unit applies an instruction algorithm specifically for tsunamis. This allows the application of the most appropriate instruction algorithm for each type of disaster. Some or all of the above-described processes in the Citizen Safety Network Unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the Citizen Safety Network Unit can input data corresponding to the type of disaster into a generative AI, which can then analyze the data and apply the most appropriate instruction algorithm.

[0063] The Citizen Safety Network Department can adjust the range of safety instructions when issuing instructions, taking geographical information into consideration. For example, when an earthquake occurs, the Citizen Safety Network Department will prioritize adjusting the instruction range around the epicenter. For example, when a typhoon approaches, the Citizen Safety Network Department can adjust the instruction range for areas along the typhoon's path. For example, when a tsunami warning is issued, the Citizen Safety Network Department will prioritize adjusting the instruction range for coastal areas. By adjusting the instruction range while taking geographical information into consideration, more effective safety instructions become possible. Some or all of the above processing in the Citizen Safety Network Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Citizen Safety Network Department can input geographical information into a generative AI, and the generative AI can analyze the data and adjust the instruction range.

[0064] The Citizen Safety Network Department can analyze social media data to assess real-time safety needs when issuing safety instructions. For example, the Citizen Safety Network Department can assess real-time safety needs in areas that are trending on social media. For example, the Citizen Safety Network Department can assess real-time safety needs in areas with many posts related to emergencies. For example, the Citizen Safety Network Department can analyze image and video data shared on social media to assess real-time safety needs. In this way, real-time safety needs can be assessed by analyzing social media data. Some or all of the above processing in the Citizen Safety Network Department may be performed using, for example, generative AI, or without generative AI. For example, the Citizen Safety Network Department can input social media data into a generative AI, which can then analyze the data to assess real-time safety needs.

[0065] The Citizen Safety Network Department can provide multilingual support to assist foreign residents. For example, the Citizen Safety Network Department can automatically set the language of safety instructions based on the language settings of the user's device. For example, the Citizen Safety Network Department can provide a language switching function if the user uses multiple languages. For example, if the Citizen Safety Network Department selects a specific language, the Citizen Safety Network Department can provide safety instructions in that language. In this way, by providing multilingual support, foreign residents can also receive appropriate safety instructions. Some or all of the above processing in the Citizen Safety Network Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Citizen Safety Network Department can input the user's language settings into a generative AI, and the generative AI can provide safety instructions in the most appropriate language.

[0066] The Citizen Safety Network Unit can update the user's current location information in real time when issuing safety instructions. For example, the Citizen Safety Network Unit can update the user's current location in real time while the user is moving and issue safety instructions. For example, the Citizen Safety Network Unit can update the user's current location in real time as the user approaches a dangerous area and provide optimal safety instructions. For example, if the user gets lost, the Citizen Safety Network Unit can update the user's current location in real time and issue safety instructions again. This allows for more appropriate safety instructions by updating the user's current location information in real time. Some or all of the above processing in the Citizen Safety Network Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Citizen Safety Network Unit can input the user's location information into a generative AI, which can then analyze the data and issue safety instructions.

[0067] The Citizen Safety Network Unit can provide optimal safety instructions by considering the user's health condition when issuing safety instructions. For example, if the user is tired, the Citizen Safety Network Unit can provide evacuation instructions via the shortest route. For example, if the user is seeking healthy exercise, the Citizen Safety Network Unit can provide an evacuation route that is slightly longer. For example, if the user is feeling unwell, the Citizen Safety Network Unit can provide an evacuation route that includes rest points. This allows for more appropriate safety instructions by considering the user's health condition. Some or all of the above processing in the Citizen Safety Network Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Citizen Safety Network Unit can input the user's health data into a generative AI, which can then analyze the data to provide optimal safety instructions.

[0068] The Citizen Safety Network Unit can select the optimal display method when issuing safety instructions, taking into account the user's device information. For example, if the user is using a smartphone, the Citizen Safety Network Unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the Citizen Safety Network Unit can provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the Citizen Safety Network Unit can provide a concise and highly visible display method. This makes it possible to provide more appropriate information by taking into account the user's device information. Some or all of the above processing in the Citizen Safety Network Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Citizen Safety Network Unit can input the user's device information into a generative AI, and the generative AI can analyze the data and select the optimal display method.

[0069] The Citizen Safety Network Unit can provide multilingual safety instructions according to the user's language settings when issuing safety instructions. For example, the Citizen Safety Network Unit can automatically set the language of safety instructions based on the language settings of the user's device. For example, the Citizen Safety Network Unit can provide a language switching function if the user uses multiple languages. For example, if the user selects a specific language, the Citizen Safety Network Unit can provide safety instructions in that language. This enables the provision of more appropriate information by providing multilingual safety instructions according to the user's language settings. Some or all of the above processing in the Citizen Safety Network Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Citizen Safety Network Unit can input the user's language settings into a generative AI, which can then analyze the data to provide multilingual safety instructions.

[0070] The Citizen Safety Network Unit can provide scheduled safety instructions by referring to the user's calendar information. For example, the Citizen Safety Network Unit can automatically set safety instructions by referring to the schedule registered in the user's calendar. For example, the Citizen Safety Network Unit can provide safety instructions by considering locations related to specific events from the user's calendar information. For example, the Citizen Safety Network Unit can suggest the optimal evacuation route based on the schedule, using the user's calendar information. This makes it possible to provide more appropriate safety instructions by referring to the user's calendar information. Some or all of the above processing in the Citizen Safety Network Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Citizen Safety Network Unit can input the user's calendar information into a generative AI, which can analyze the data and provide scheduled safety instructions.

[0071] The Citizen Safety Network Unit can provide optimal safety instructions by referring to the user's past movement history when issuing safety instructions. For example, the Citizen Safety Network Unit can provide safety instructions by considering places the user has frequently visited in the past. For example, the Citizen Safety Network Unit can predict and suggest evacuation routes to be used during a specific time period based on the user's past movement history. For example, the Citizen Safety Network Unit can analyze the user's past movement patterns and suggest the most efficient evacuation route. This makes it possible to provide more appropriate safety instructions by referring to the user's past movement history. Some or all of the above processing in the Citizen Safety Network Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Citizen Safety Network Unit can input the user's past movement history into a generative AI, which can then analyze the data and provide optimal safety instructions.

[0072] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0073] The data collection unit can optimize its data collection method by referring to past disaster data during data collection. For example, the data collection unit can prioritize collecting data around the epicenter based on past earthquake data. For example, the data collection unit can focus on collecting wind speed and rainfall data based on past typhoon data. For example, the data collection unit can prioritize collecting coastal water level data based on past tsunami data. This allows the data collection method to be optimized by referring to past disaster data. Some or all of the above processing in the data collection unit may be performed using, for example, a generation AI, or without a generation AI. For example, the data collection unit can input past disaster data into a generation AI, and the generation AI can analyze the data to optimize the data collection method.

[0074] The predictive analysis unit can optimize its prediction algorithm by referring to past disaster patterns during predictive analysis. For example, the predictive analysis unit can optimize an algorithm to predict the impact around the epicenter based on past earthquake data. For example, the predictive analysis unit can optimize an algorithm to predict the impact of wind speed and rainfall based on past typhoon data. For example, the predictive analysis unit can optimize an algorithm to predict the impact on coastal areas based on past tsunami data. In this way, the prediction algorithm can be optimized by referring to past disaster patterns. Some or all of the above processing in the predictive analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the predictive analysis unit can input past disaster data into a generative AI, and the generative AI can analyze the data to optimize the prediction algorithm.

[0075] The resource allocation unit can optimize the allocation method by referring to past resource allocation data when allocating resources. For example, the resource allocation unit can optimize resource allocation around the epicenter based on resource allocation data from past earthquakes. For example, the resource allocation unit can optimize resource allocation in areas along the path of typhoons based on resource allocation data from past typhoons. For example, the resource allocation unit can optimize resource allocation in coastal areas based on resource allocation data from past tsunamis. In this way, the allocation method can be optimized by referring to past resource allocation data. Some or all of the above processing in the resource allocation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the resource allocation unit can input past resource allocation data into a generation AI, and the generation AI can analyze the data to optimize the allocation method.

[0076] The communications unit can optimize the communication method by referring to past communication data during communication. For example, the communications unit can optimize the communication method around the epicenter based on communication data from past earthquakes. For example, the communications unit can optimize the communication method for areas along the path of a typhoon based on communication data from past typhoons. For example, the communications unit can optimize the communication method for coastal areas based on communication data from past tsunamis. In this way, the communications method can be optimized by referring to past communication data. Some or all of the above processing in the communications unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the communications unit can input past communication data into a generative AI, and the generative AI can analyze the data and optimize the communication method.

[0077] The Citizen Safety Network Unit can provide optimal safety instructions by considering the user's health condition when issuing safety instructions. For example, if the user is tired, the Citizen Safety Network Unit can provide evacuation instructions via the shortest route. For example, if the user is seeking healthy exercise, the Citizen Safety Network Unit can provide an evacuation route that is slightly longer. For example, if the user is feeling unwell, the Citizen Safety Network Unit can provide an evacuation route that includes rest points. This allows for more appropriate safety instructions by considering the user's health condition. Some or all of the above processing in the Citizen Safety Network Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Citizen Safety Network Unit can input the user's health data into a generative AI, which can then analyze the data to provide optimal safety instructions.

[0078] The following briefly describes the processing flow for example form 1.

[0079] Step 1: The data collection unit gathers information from multiple data sources. For example, it can collect information from satellites, IoT sensors, social media, and official channels. The data collection unit acquires data from satellites in real time to monitor the situation of the disaster. It can also collect earthquake vibration data and tsunami water level data using IoT sensors. Furthermore, it can analyze posts from social media to collect information related to the disaster. It also collects information from official channels and uses official announcements from the government and local authorities to carry out disaster response. Step 2: The Predictive Analysis Unit analyzes the information collected by the Data Collection Unit to predict the impact of the disaster and the need for resources. For example, AI can be used to predict the epicenter of an earthquake and the arrival time of a tsunami. Furthermore, it can also predict the extent of the disaster's impact and the degree of damage. The degree of damage is predicted based on the distance from the earthquake's epicenter and topographical information. Step 3: The resource allocation unit optimizes resource allocation based on the prediction results obtained by the predictive analysis unit. For example, it can optimize the allocation of emergency response personnel and supplies. AI can also be used to optimize the deployment of emergency response personnel and the allocation of supplies. The optimal deployment is made based on the extent of damage and the number of emergency response personnel. Step 4: The communications department facilitates seamless communication between multiple agencies. For example, it can streamline information sharing between disaster response agencies. AI can also be used to optimize information sharing between disaster response agencies. Information can be shared in real time between disaster response agencies, enabling a rapid response. Step 5: The Citizen Safety Network Department provides citizens with real-time updates and individual safety instructions. For example, it can issue evacuation orders to citizens in the event of a disaster. It can also use AI to provide optimal evacuation instructions to citizens. It can provide the optimal evacuation route based on citizens' location information.

[0080] (Example of form 2) An AI-Powered Disaster Response Coordinator, according to an embodiment of the present invention, is an advanced system designed to revolutionize disaster management. This system integrates real-time data from multiple data sources and leverages state-of-the-art AI algorithms to optimize resource allocation, enhance communication, and significantly improve response times during natural disasters such as earthquakes, tsunamis, and typhoons. This solution aims to protect lives and minimize the economic impact on Japan, a disaster-prone region, by utilizing advanced technology. For example, the AI-Powered Disaster Response Coordinator collects information from satellites, IoT sensors, social media, and official channels. Next, the AI-Powered Disaster Response Coordinator uses AI to predict the impact of a disaster and the need for resources. Furthermore, based on the predictions, the AI-Powered Disaster Response Coordinator optimizes the allocation of emergency response personnel and supplies. The AI-Powered Disaster Response Coordinator facilitates seamless communication between multiple agencies. Additionally, the AI-Powered Disaster Response Coordinator provides citizens with real-time updates and personalized safety instructions. The AI-Powered Disaster Response Coordinator provides multilingual support to assist foreign residents. It generates scenarios and creates simulations for preparedness planning. It analyzes emergency communications and social media using natural language processing to perform real-time situation assessments. It processes satellite and drone imagery using computer vision to assess damage and direct rescue operations.The AI-Powered Disaster Response Coordinator predicts the impact of a disaster and the need for resources based on current data and historical patterns. This allows the AI-Powered Disaster Response Coordinator to minimize the impact of a disaster and enable a rapid and effective response.

[0081] The AI-Powered Disaster Response Coordinator according to this embodiment comprises a data collection unit, a predictive analysis unit, a resource allocation unit, a communication unit, and a civil safety network unit. The data collection unit collects information from multiple data sources. For example, the data collection unit can collect information from satellites, IoT sensors, social media, and official channels. For example, the data collection unit can acquire data from satellites in real time and monitor the situation of a disaster. The data collection unit can also collect earthquake vibration data and tsunami water level data using IoT sensors. Furthermore, the data collection unit can also collect information related to disasters by analyzing posts from social media. For example, the data collection unit can collect posts related to disasters on social media in real time to understand the situation of a disaster. It also collects information from official channels and takes disaster response actions based on official announcements from the government and local authorities. The predictive analysis unit analyzes the information collected by the data collection unit and predicts the impact of the disaster and the need for resources. For example, the predictive analysis unit can use AI to predict the epicenter of an earthquake and the arrival time of a tsunami. The predictive analysis unit can also use AI to predict the extent of the disaster's impact and the degree of damage. For example, the Predictive Analysis Department predicts the extent of damage based on the distance from the earthquake's epicenter and topographical information. The Resource Allocation Department optimizes resource allocation based on the prediction results obtained by the Predictive Analysis Department. The Resource Allocation Department can optimize the allocation of emergency response personnel and supplies, for example. The Resource Allocation Department can also use AI to optimize the deployment of emergency response personnel and the allocation of supplies. For example, the Resource Allocation Department makes optimal deployments based on the extent of damage and the number of emergency response personnel. The Communications Department facilitates seamless communication between multiple agencies. For example, the Communications Department can facilitate information sharing between disaster response agencies. The Communications Department can also use AI to optimize information sharing between disaster response agencies. For example, the Communications Department can share information between disaster response agencies in real time, enabling a rapid response. The Citizen Safety Network Department provides citizens with real-time updates and individual safety instructions. For example, the Citizen Safety Network Department can issue evacuation orders to citizens in the event of a disaster.The Citizen Safety Network Department can also use AI to provide citizens with optimal evacuation instructions. For example, the Citizen Safety Network Department can provide the optimal evacuation route based on citizens' location information. As a result, the AI-Powered Disaster Response Coordinator according to this embodiment minimizes the impact of disasters and enables a rapid and effective response.

[0082] The data collection unit gathers information from multiple data sources. For example, it can collect information from satellites, IoT sensors, social media, and official channels. Specifically, it acquires data from satellites in real time to monitor the situation of disasters. Satellite data provides high-resolution images and weather data, enabling rapid detection of natural disasters such as earthquakes, tsunamis, and floods. The data collection unit can also collect earthquake vibration data and tsunami water level data using IoT sensors. These sensors include seismometers, water level gauges, and temperature sensors, and collect data in real time, transmitting it to a central database. Furthermore, the data collection unit can also collect disaster-related information by analyzing posts from social media. For example, the data collection unit collects disaster-related posts on social media in real time to understand the situation of the disaster. This includes using natural language processing technology to analyze the content of posts and extract keywords and location information related to the disaster. Information from official channels is also collected, and disaster response is carried out based on official announcements from governments and local authorities. As a result, the data collection unit can collect a wide range of information from diverse data sources and understand the situation in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the predictive analytics unit and the resource allocation unit. Adjusting the data collection frequency and accuracy also allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0083] The Predictive Analysis Department analyzes information collected by the Data Collection Department to predict the impact of disasters and the need for resources. For example, the Predictive Analysis Department can use AI to predict the epicenter of earthquakes and the arrival time of tsunamis. Specifically, the AI ​​analyzes seismic wave data to identify the location of the epicenter and the magnitude of the earthquake. It also performs tsunami simulations to predict the arrival time and affected area of ​​tsunamis. Furthermore, the Predictive Analysis Department can also use AI to predict the extent of the disaster's impact and the degree of damage. For example, the Predictive Analysis Department predicts the degree of damage based on the distance from the earthquake's epicenter and topographic information. This includes methods that utilize past disaster data and geographic information systems (GIS) to analyze damage patterns. The Predictive Analysis Department can analyze collected data in real time and provide prediction results quickly. Furthermore, the Predictive Analysis Department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past earthquake data, it can predict fluctuations in risk in specific regions and time periods and formulate future countermeasures. Furthermore, the predictive analytics unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early warnings. This allows the predictive analytics unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0084] The Resource Allocation Unit optimizes resource allocation based on prediction results obtained by the Predictive Analytics Unit. For example, the Resource Allocation Unit can optimize the allocation of emergency response personnel and supplies. Specifically, it uses AI to optimize the deployment of emergency response personnel and the allocation of supplies. For instance, the Resource Allocation Unit makes optimal deployments based on the extent of damage and the number of emergency response personnel. This includes methods for integrating damage prediction data and resource inventory information to formulate an optimal allocation plan. The Resource Allocation Unit can continuously adjust resource allocation based on real-time updated data. For example, if the damage situation changes, the Resource Allocation Unit immediately incorporates new data and updates the allocation plan. Furthermore, the Resource Allocation Unit can perform more accurate resource allocation by considering regional characteristics and past disaster history. This allows the Resource Allocation Unit to always provide highly accurate resource allocation based on the latest information, supporting a rapid and appropriate response. In addition, the Resource Allocation Unit can collaborate with other departments and agencies to promote the efficient use of resources. For example, it can share information with other disaster response agencies to prevent resource duplication and shortages. Furthermore, the resource allocation unit can handle not only emergency resource allocation but also normal resource management and stockpiling planning, thereby improving the overall efficiency and sustainability of the system.

[0085] The Ministry of Communications facilitates seamless communication between multiple agencies. For example, it can streamline information sharing among disaster response agencies. Specifically, it uses AI to optimize information sharing among disaster response agencies. For instance, the Ministry of Communications can share information among disaster response agencies in real time, enabling rapid response. This includes functions such as automatic data aggregation and filtering, and prioritization of critical information. The Ministry of Communications can absorb differences in protocols and formats between different agencies and provide a unified information sharing platform. Furthermore, the Ministry of Communications strengthens collaboration among disaster response agencies and promotes joint responses. For example, it provides collaborative workspaces and real-time chat functions to support rapid decision-making. In addition, the Ministry of Communications can collaborate not only with disaster response agencies but also with local residents and volunteer groups to promote information sharing and cooperation. This allows the Ministry of Communications to improve the efficiency and effectiveness of disaster response. Moreover, the Ministry of Communications can analyze data collected during the disaster response process and identify areas for future improvement. For example, based on data from past disaster responses, it can identify communication bottlenecks and challenges and propose solutions. This allows the Ministry of Communications to continuously improve the quality of disaster response.

[0086] The Citizen Safety Network Department provides citizens with real-time updates and individual safety instructions. For example, it can issue evacuation orders to citizens during a disaster. Specifically, it uses AI to provide optimal evacuation instructions to citizens. For instance, it provides optimal evacuation routes based on citizens' location information. This includes calculating evacuation routes that take into account real-time traffic information and the progression of the disaster. The Citizen Safety Network Department can provide information to citizens quickly and reliably through smartphone apps, SMS, and voice calls. Furthermore, it can collect feedback from citizens to continuously improve the accuracy and effectiveness of evacuation orders. For example, it can revise evacuation routes and improve instruction content based on feedback from citizens who have received evacuation orders. The Citizen Safety Network Department can also provide more accurate evacuation orders by considering regional characteristics and past disaster history. This allows the Citizen Safety Network Department to provide citizens with quick and appropriate evacuation instructions, minimizing the risk of disaster. Additionally, the Citizen Safety Network Department can provide information to citizens even during the preparation phase before a disaster occurs, promoting disaster preparedness. For example, if the likelihood of a disaster increases, a notification will be sent in advance urging people to prepare for evacuation. This allows the Citizen Safety Network Department to ensure the safety of citizens at all stages of disaster response and support a swift and effective response.

[0087] The data collection unit can collect information from satellites, IoT sensors, social media, and official channels. For example, the data collection unit can acquire data from satellites in real time and monitor the situation of a disaster. The data collection unit can also collect earthquake vibration data and tsunami water level data using IoT sensors. For example, the data collection unit can also collect information about a disaster by analyzing posts from social media. For example, the data collection unit can collect posts about a disaster on social media in real time to understand the situation of the disaster. The data collection unit can also collect information from official channels and respond to disasters based on official announcements from the government and local authorities. This allows for more comprehensive disaster information to be obtained by collecting information from diverse data sources. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data from satellites into AI, which can then analyze the data to monitor the situation of a disaster.

[0088] The predictive analysis unit can use AI to predict the impact of a disaster and the need for resources. For example, the predictive analysis unit can use AI to predict the epicenter of an earthquake and the arrival time of a tsunami. For example, the predictive analysis unit can also use AI to predict the extent of the impact and the degree of damage from a disaster. For example, the predictive analysis unit predicts the degree of damage based on the distance from the earthquake's epicenter and topographical information. This allows for highly accurate prediction of the impact of a disaster and the need for resources by using AI. Some or all of the above-described processes in the predictive analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the predictive analysis unit can input data from the data collection unit into the generative AI, which can then analyze the data to predict the impact of a disaster and the need for resources.

[0089] The resource allocation unit can optimize the allocation of emergency response personnel and supplies based on prediction results. For example, the resource allocation unit can optimize the allocation of emergency response personnel and supplies. The resource allocation unit can also optimize the deployment of emergency response personnel and supplies using AI, for example. For example, the resource allocation unit makes optimal deployments based on the extent of damage and the number of emergency response personnel. This enables rapid and effective disaster response by optimizing resource allocation based on prediction results. Some or all of the above-described processes in the resource allocation unit may be performed using, for example, a generating AI, or without a generating AI. For example, the resource allocation unit can input data from the predictive analysis unit into a generating AI, which can then analyze the data to optimize resource allocation.

[0090] The Communications Department can facilitate seamless communication between multiple agencies. For example, it can facilitate information sharing between disaster response agencies. The Communications Department can also optimize information sharing between disaster response agencies using AI. For example, it can share information between disaster response agencies in real time, enabling a rapid response. This improves the efficiency of disaster response by facilitating seamless communication between multiple agencies. Some or all of the above-described processes in the Communications Department may be performed using, for example, generative AI, or not using generative AI. For example, the Communications Department can input information between disaster response agencies into a generative AI, which can then analyze the information to facilitate optimal communication.

[0091] The Citizen Safety Network Department can provide citizens with real-time updates and individual safety instructions. For example, the Citizen Safety Network Department can issue evacuation instructions to citizens in the event of a disaster. The Citizen Safety Network Department can also use AI to provide citizens with optimal evacuation instructions. For example, the Citizen Safety Network Department can provide optimal evacuation routes based on citizens' location information. This ensures the safety of citizens by providing them with real-time updates and individual safety instructions. Some or all of the above-described processes in the Citizen Safety Network Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Citizen Safety Network Department can input citizens' location information into a generative AI, which can then provide optimal evacuation routes.

[0092] The Citizen Safety Network Department can provide multilingual support to assist foreign residents. For example, the Citizen Safety Network Department can automatically set the language of safety instructions based on the language settings of the user's device. The Citizen Safety Network Department can also provide a language switching function if the user uses multiple languages. For example, if the Citizen Safety Network Department selects a specific language, it can provide safety instructions in that language. By providing multilingual support, foreign residents can also receive appropriate safety instructions. Some or all of the above processes in the Citizen Safety Network Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Citizen Safety Network Department can input the user's language settings into a generative AI, which can then provide safety instructions in the most appropriate language.

[0093] The predictive analysis unit can generate scenarios and create simulations for disaster preparedness planning. For example, the predictive analysis unit can use AI to generate scenarios for disaster occurrences and create simulations for disaster preparedness planning. For example, the predictive analysis unit can generate scenarios for earthquake occurrences and simulate evacuation plans. For example, the predictive analysis unit can generate scenarios for typhoon approaching and simulate evacuation plans. By generating scenarios and performing simulations, disaster preparedness plans can be effectively formulated. Some or all of the above-described processes in the predictive analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the predictive analysis unit can input disaster occurrence data into a generation AI, which can then generate scenarios and perform simulations.

[0094] The predictive analytics unit can analyze emergency communications and social media using natural language processing to perform real-time situation assessments. For example, the predictive analytics unit can use natural language processing to analyze the content of emergency communications and assess the disaster situation. The predictive analytics unit can also analyze social media posts to collect information about the disaster and perform real-time situation assessments. For example, the predictive analytics unit can analyze the content of emergency communications and assess the extent of the damage. In this way, real-time situation assessments can be performed from emergency communications and social media using natural language processing. Some or all of the above processing in the predictive analytics unit may be performed using, for example, generative AI, or without generative AI. For example, the predictive analytics unit can input emergency communication data into a generative AI, which can then analyze the data to perform real-time situation assessments.

[0095] The predictive analysis unit can process satellite and drone images using computer vision to assess damage and direct rescue operations. For example, the predictive analysis unit can use computer vision to analyze satellite images and assess the extent of damage. The predictive analysis unit can also analyze drone images and assess the extent of damage. For example, the predictive analysis unit can combine satellite and drone images to perform a detailed assessment of the damage. This allows for the assessment of damage from satellite and drone images using computer vision and the effective direction of rescue operations. Some or all of the processing described above in the predictive analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the predictive analysis unit can input satellite and drone image data into a generative AI, which can then analyze the data to assess damage and direct rescue operations.

[0096] The predictive analysis unit can predict the impact of a disaster and the need for resources based on current data and past patterns. For example, the predictive analysis unit can predict the extent of an earthquake's impact based on current data. The predictive analysis unit can also predict the degree of damage based on past earthquake data. For example, the predictive analysis unit can predict the impact of a disaster and the need for resources with high accuracy by combining current data and past patterns. This allows for high-accuracy prediction of the impact of a disaster and the need for resources based on current data and past patterns. Some or all of the above processing in the predictive analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the predictive analysis unit can input current data and past patterns into a generative AI, which can analyze the data to predict the impact of a disaster and the need for resources.

[0097] The data collection unit can estimate the user's emotions and adjust the priority of data collection based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can prioritize collecting urgent data. For example, if the user is calm, the data collection unit can collect detailed data to improve the accuracy of the analysis. For example, if the user is confused, the data collection unit can prioritize collecting concise and important data. This allows for more appropriate data collection by adjusting the priority of data collection based on 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 a generative AI, or not. For example, the data collection unit can input user emotion data into a generative AI, which can analyze the data and adjust the priority of data collection.

[0098] The data collection unit can optimize its data collection method by referring to past disaster data during data collection. For example, the data collection unit can prioritize collecting data around the epicenter based on past earthquake data. For example, the data collection unit can focus on collecting wind speed and rainfall data based on past typhoon data. For example, the data collection unit can prioritize collecting coastal water level data based on past tsunami data. This allows the data collection method to be optimized by referring to past disaster data. Some or all of the above processing in the data collection unit may be performed using, for example, a generation AI, or without a generation AI. For example, the data collection unit can input past disaster data into a generation AI, and the generation AI can analyze the data to optimize the data collection method.

[0099] The data collection unit can apply different collection algorithms depending on the type of data being collected. For example, when collecting weather data, the data collection unit can apply a real-time weather forecasting algorithm. For example, when collecting social media data, the data collection unit can apply a natural language processing algorithm. For example, when collecting satellite data, the data collection unit can apply an image analysis algorithm. By applying different collection algorithms depending on the type of data being collected, the accuracy of data collection is improved. Some or all of the above-described processing in the data collection unit may be performed using, for example, generative AI, or without using generative AI. For example, the data collection unit can have the generative AI apply an appropriate algorithm depending on the type of data being collected.

[0100] The data collection unit can estimate the user's emotions and filter the data to be collected based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can filter and provide only urgent data. If the user is calm, the data collection unit can filter and provide detailed data. If the user is confused, the data collection unit can filter and provide only concise and important data. This allows for the provision of more appropriate data by filtering data based on 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 a generative AI, or not using a generative AI. For example, the data collection unit can input user emotion data into a generative AI, which can then analyze the data and filter the data to be collected.

[0101] The data collection unit can adjust its collection range by considering geographical information during data collection. For example, when an earthquake occurs, the data collection unit can prioritize collecting data around the epicenter. For example, when a typhoon approaches, the data collection unit can focus on collecting data in areas along its path. For example, when a tsunami warning is issued, the data collection unit can prioritize collecting data in coastal areas. By adjusting the collection range by considering geographical information, more effective data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input geographical information into a generative AI, and the generative AI can analyze the data and adjust the collection range.

[0102] The data collection unit can analyze social media trends during data collection and prioritize the collection of relevant data. For example, the data collection unit can prioritize the collection of data from areas that are trending on social media. For example, the data collection unit can focus on collecting data from areas with a high number of posts related to emergencies. For example, the data collection unit can prioritize the collection of images and videos shared on social media. This allows for the priority collection of relevant data by analyzing social media trends. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input social media trend data into a generative AI, which can then analyze the data and prioritize the collection of relevant data.

[0103] The predictive analytics unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, if the user is feeling anxious, the predictive analytics unit can provide a concise and highly visible display method. For example, if the user is calm, the predictive analytics unit can provide a display method that includes detailed information. For example, if the user is confused, the predictive analytics unit can provide a display method that gets straight to the point. By adjusting the display method of the prediction results based on the user's emotions, it becomes possible to provide more appropriate information. 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 predictive analytics unit may be performed using a generative AI, for example, or without a generative AI. For example, the predictive analytics unit can input user emotion data into a generative AI, and the generative AI can analyze the data and adjust the display method of the prediction results.

[0104] The predictive analysis unit can optimize its prediction algorithm by referring to past disaster patterns during predictive analysis. For example, the predictive analysis unit can optimize an algorithm to predict the impact around the epicenter based on past earthquake data. For example, the predictive analysis unit can optimize an algorithm to predict the impact of wind speed and rainfall based on past typhoon data. For example, the predictive analysis unit can optimize an algorithm to predict the impact on coastal areas based on past tsunami data. In this way, the prediction algorithm can be optimized by referring to past disaster patterns. Some or all of the above processing in the predictive analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the predictive analysis unit can input past disaster data into a generative AI, and the generative AI can analyze the data to optimize the prediction algorithm.

[0105] The predictive analysis unit can apply different prediction models depending on the type of disaster during predictive analysis. For example, when an earthquake occurs, the predictive analysis unit applies a prediction model specifically for earthquakes. For example, when a typhoon approaches, the predictive analysis unit can apply a prediction model specifically for typhoons. For example, when a tsunami warning is issued, the predictive analysis unit applies a prediction model specifically for tsunamis. This allows the application of the optimal prediction model according to the type of disaster. Some or all of the above-described processes in the predictive analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the predictive analysis unit can input data according to the type of disaster into a generative AI, and the generative AI can analyze the data and apply the optimal prediction model.

[0106] The predictive analytics 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 predictive analytics unit can prioritize displaying prediction results that indicate urgency. For example, if the user is calm, the predictive analytics unit can prioritize displaying detailed prediction results. For example, if the user is confused, the predictive analytics unit can prioritize displaying concise and important prediction results. This allows for the provision of more appropriate information by prioritizing prediction results based on 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-described processing in the predictive analytics unit may be performed using, for example, generative AI, or without generative AI. For example, the predictive analytics unit can input user emotion data into a generative AI, which can then analyze the data to determine the priority of prediction results.

[0107] The predictive analysis unit can adjust the prediction range by taking geographical information into consideration during predictive analysis. For example, when an earthquake occurs, the predictive analysis unit can prioritize adjusting the prediction range around the epicenter. For example, when a typhoon approaches, the predictive analysis unit can adjust the prediction range for areas along its path. For example, when a tsunami warning is issued, the predictive analysis unit can prioritize adjusting the prediction range for coastal areas. By adjusting the prediction range by taking geographical information into consideration, more effective predictions become possible. Some or all of the above processing in the predictive analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the predictive analysis unit can input geographical information into a generative AI, and the generative AI can analyze the data and adjust the prediction range.

[0108] The predictive analytics unit can analyze social media data during predictive analysis to perform real-time situation assessments. For example, the predictive analytics unit can assess the situation in a region that is trending on social media in real time. For example, the predictive analytics unit can assess the situation in a region that has many posts related to emergencies in real time. For example, the predictive analytics unit can analyze image and video data shared on social media to assess the situation in real time. This makes real-time situation assessment possible by analyzing social media data. Some or all of the above processing in the predictive analytics unit may be performed using, for example, a generative AI, or without a generative AI. For example, the predictive analytics unit can input social media data into a generative AI, and the generative AI can analyze the data to perform real-time situation assessments.

[0109] The resource allocation unit can estimate the user's emotions and adjust resource allocation priorities based on the estimated emotions. For example, if the user is feeling anxious, the resource allocation unit will prioritize allocating urgent resources. For example, if the user is calm, the resource allocation unit can allocate resources in detail. For example, if the user is confused, the resource allocation unit will prioritize allocating concise and important resources. This allows for more appropriate resource allocation by adjusting resource allocation priorities based on the user's emotions. 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 resource allocation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the resource allocation unit can input user emotion data into a generative AI, which can analyze the data and adjust resource allocation priorities.

[0110] The resource allocation unit can optimize the allocation method by referring to past resource allocation data when allocating resources. For example, the resource allocation unit can optimize resource allocation around the epicenter based on resource allocation data from past earthquakes. For example, the resource allocation unit can optimize resource allocation in areas along the path of typhoons based on resource allocation data from past typhoons. For example, the resource allocation unit can optimize resource allocation in coastal areas based on resource allocation data from past tsunamis. In this way, the allocation method can be optimized by referring to past resource allocation data. Some or all of the above processing in the resource allocation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the resource allocation unit can input past resource allocation data into a generation AI, and the generation AI can analyze the data to optimize the allocation method.

[0111] The resource allocation unit can apply different allocation algorithms depending on the type of disaster when allocating resources. For example, when an earthquake occurs, the resource allocation unit applies an allocation algorithm specifically for earthquakes. For example, when a typhoon approaches, the resource allocation unit can apply an allocation algorithm specifically for typhoons. For example, when a tsunami warning is issued, the resource allocation unit applies an allocation algorithm specifically for tsunamis. This allows the application of the optimal allocation algorithm according to the type of disaster. Some or all of the above-described processes in the resource allocation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the resource allocation unit can input data according to the type of disaster into a generation AI, and the generation AI can analyze the data and apply the optimal allocation algorithm.

[0112] The resource allocation unit can estimate the user's emotions and adjust the display method of resource allocation based on the estimated user emotions. For example, if the user is feeling anxious, the resource allocation unit can provide a concise and highly visible display method. For example, if the user is calm, the resource allocation unit can provide a display method that includes detailed information. For example, if the user is confused, the resource allocation unit can provide a display method that gets straight to the point. By adjusting the display method of resource allocation based on the user's emotions, it becomes possible to provide more appropriate information. 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 resource allocation unit may be performed using a generative AI, or not using a generative AI. For example, the resource allocation unit can input user emotion data into a generative AI, and the generative AI can analyze the data and adjust the display method of resource allocation.

[0113] The resource allocation unit can adjust the allocation range when allocating resources, taking geographical information into consideration. For example, when an earthquake occurs, the resource allocation unit can prioritize adjusting the resource allocation range around the epicenter. For example, when a typhoon approaches, the resource allocation unit can adjust the resource allocation range for areas along its path. For example, when a tsunami warning is issued, the resource allocation unit can prioritize adjusting the resource allocation range for coastal areas. By adjusting the allocation range while considering geographical information, more effective resource allocation becomes possible. Some or all of the above processing in the resource allocation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the resource allocation unit can input geographical information into a generative AI, and the generative AI can analyze the data and adjust the allocation range.

[0114] The resource allocation unit can analyze social media data to assess real-time resource needs when allocating resources. For example, the resource allocation unit can assess the resource needs of areas that are trending on social media in real time. For example, the resource allocation unit can assess the resource needs of areas with many posts related to emergencies in real time. For example, the resource allocation unit can analyze image and video data shared on social media to assess real-time resource needs. In this way, real-time resource needs can be assessed by analyzing social media data. Some or all of the above processing in the resource allocation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the resource allocation unit can input social media data into a generative AI, and the generative AI can analyze the data to assess real-time resource needs.

[0115] The communication unit can estimate the user's emotions and adjust the priority of communication content based on the estimated emotions. For example, if the user is feeling anxious, the communication unit will prioritize sending urgent communication content. For example, if the user is calm, the communication unit can send detailed communication content. For example, if the user is confused, the communication unit will prioritize sending concise and important communication content. This allows for the provision of more appropriate information by adjusting the priority of communication content based on 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 communication unit may be performed using a generative AI, or not using a generative AI. For example, the communication unit can input user emotion data into a generative AI, which can analyze the data and adjust the priority of communication content.

[0116] The communications unit can optimize the communication method by referring to past communication data during communication. For example, the communications unit can optimize the communication method around the epicenter based on communication data from past earthquakes. For example, the communications unit can optimize the communication method for areas along the path of a typhoon based on communication data from past typhoons. For example, the communications unit can optimize the communication method for coastal areas based on communication data from past tsunamis. In this way, the communications method can be optimized by referring to past communication data. Some or all of the above processing in the communications unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the communications unit can input past communication data into a generative AI, and the generative AI can analyze the data and optimize the communication method.

[0117] The communications unit can apply different communication protocols depending on the type of disaster during communication. For example, when an earthquake occurs, the communications unit applies a communication protocol specifically for earthquakes. For example, when a typhoon approaches, the communications unit can apply a communication protocol specifically for typhoons. For example, when a tsunami warning is issued, the communications unit applies a communication protocol specifically for tsunamis. This allows the communications unit to apply the most appropriate communication protocol according to the type of disaster. Some or all of the above-described processing in the communications unit may be performed using, for example, a generation AI, or without a generation AI. For example, the communications unit can input data according to the type of disaster into a generation AI, and the generation AI can analyze the data and apply the most appropriate communication protocol.

[0118] The communication unit can estimate the user's emotions and adjust the display method of the communication content based on the estimated user emotions. For example, if the user is feeling anxious, the communication unit can provide a concise and highly visible display method. For example, if the user is calm, the communication unit can provide a display method that includes detailed information. For example, if the user is confused, the communication unit can provide a display method that gets straight to the point. By adjusting the display method of the communication content based on the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 communication unit may be performed using a generative AI, for example, or without a generative AI. For example, the communication unit can input user emotion data into a generative AI, and the generative AI can analyze the data and adjust the display method of the communication content.

[0119] The communications unit can adjust its communication range by taking geographical information into consideration during communication. For example, when an earthquake occurs, the communications unit will prioritize adjusting the communication range around the epicenter. For example, when a typhoon approaches, the communications unit can adjust the communication range for areas along its path. For example, when a tsunami warning is issued, the communications unit will prioritize adjusting the communication range for coastal areas. By adjusting the communication range by taking geographical information into consideration, more effective communication becomes possible. Some or all of the above processing in the communications unit may be performed using, for example, a generative AI, or without a generative AI. For example, the communications unit can input geographical information into a generative AI, and the generative AI can analyze the data and adjust the communication range.

[0120] The Ministry of Communications can analyze social media data during communication to assess real-time communication needs. For example, the Ministry can assess the communication needs of areas that are trending on social media in real time. For example, the Ministry can assess the communication needs of areas with many posts about emergencies in real time. For example, the Ministry can analyze image and video data shared on social media to assess real-time communication needs. In this way, real-time communication needs can be assessed by analyzing social media data. Some or all of the above processing by the Ministry of Communications may be performed using, for example, generative AI, or without generative AI. For example, the Ministry of Communications can input social media data into a generative AI, and the generative AI can analyze the data to assess real-time communication needs.

[0121] The Citizen Safety Network Unit can estimate the user's emotions and adjust the display method of safety instructions based on the estimated user emotions. For example, if the user is feeling anxious, the Citizen Safety Network Unit can provide a concise and highly visible display method. For example, if the user is calm, the Citizen Safety Network Unit can provide a display method that includes detailed information. For example, if the user is confused, the Citizen Safety Network Unit can provide a display method that gets straight to the point. By adjusting the display method of safety instructions based on the user's emotions, it becomes possible to provide more appropriate information. 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 Citizen Safety Network Unit may be performed using a generative AI, or not using a generative AI. For example, the Citizen Safety Network Unit can input user emotion data into a generative AI, and the generative AI can analyze the data and adjust the display method of safety instructions.

[0122] The Citizen Safety Network Department can optimize its instructions when issuing safety instructions by referring to past disaster data. For example, the Citizen Safety Network Department can optimize instructions around the epicenter based on safety instruction data from past earthquakes. For example, the Citizen Safety Network Department can optimize instructions for areas along the path of a typhoon based on safety instruction data from past typhoons. For example, the Citizen Safety Network Department can optimize instructions for coastal areas based on safety instruction data from past tsunamis. In this way, instructions can be optimized by referring to past disaster data. Some or all of the above processing in the Citizen Safety Network Department may be performed using, for example, a generating AI, or without using a generating AI. For example, the Citizen Safety Network Department can input past disaster data into a generating AI, and the generating AI can analyze the data to optimize instructions.

[0123] The Citizen Safety Network Unit can apply different instruction algorithms depending on the type of disaster when issuing safety instructions. For example, when an earthquake occurs, the Citizen Safety Network Unit applies an instruction algorithm specifically for earthquakes. For example, when a typhoon approaches, the Citizen Safety Network Unit can apply an instruction algorithm specifically for typhoons. For example, when a tsunami warning is issued, the Citizen Safety Network Unit applies an instruction algorithm specifically for tsunamis. This allows the application of the most appropriate instruction algorithm for each type of disaster. Some or all of the above-described processes in the Citizen Safety Network Unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the Citizen Safety Network Unit can input data corresponding to the type of disaster into a generative AI, which can then analyze the data and apply the most appropriate instruction algorithm.

[0124] The Citizen Safety Network Unit can estimate the user's emotions and determine the priority of safety instructions based on the estimated emotions. For example, if the user is feeling anxious, the Citizen Safety Network Unit will prioritize displaying urgent safety instructions. For example, if the user is calm, the Citizen Safety Network Unit can prioritize displaying detailed safety instructions. For example, if the user is confused, the Citizen Safety Network Unit will prioritize displaying concise and important safety instructions. This allows for the provision of more appropriate information by prioritizing safety instructions based on 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 Citizen Safety Network Unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the Citizen Safety Network Unit can input user emotion data into a generative AI, which can then analyze the data to determine the priority of safety instructions.

[0125] The Citizen Safety Network Department can adjust the range of safety instructions when issuing instructions, taking geographical information into consideration. For example, when an earthquake occurs, the Citizen Safety Network Department will prioritize adjusting the instruction range around the epicenter. For example, when a typhoon approaches, the Citizen Safety Network Department can adjust the instruction range for areas along the typhoon's path. For example, when a tsunami warning is issued, the Citizen Safety Network Department will prioritize adjusting the instruction range for coastal areas. By adjusting the instruction range while taking geographical information into consideration, more effective safety instructions become possible. Some or all of the above processing in the Citizen Safety Network Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Citizen Safety Network Department can input geographical information into a generative AI, and the generative AI can analyze the data and adjust the instruction range.

[0126] The Citizen Safety Network Department can analyze social media data to assess real-time safety needs when issuing safety instructions. For example, the Citizen Safety Network Department can assess real-time safety needs in areas that are trending on social media. For example, the Citizen Safety Network Department can assess real-time safety needs in areas with many posts related to emergencies. For example, the Citizen Safety Network Department can analyze image and video data shared on social media to assess real-time safety needs. In this way, real-time safety needs can be assessed by analyzing social media data. Some or all of the above processing in the Citizen Safety Network Department may be performed using, for example, generative AI, or without generative AI. For example, the Citizen Safety Network Department can input social media data into a generative AI, which can then analyze the data to assess real-time safety needs.

[0127] The Citizen Safety Network Department can provide multilingual support to assist foreign residents. For example, the Citizen Safety Network Department can automatically set the language of safety instructions based on the language settings of the user's device. For example, the Citizen Safety Network Department can provide a language switching function if the user uses multiple languages. For example, if the Citizen Safety Network Department selects a specific language, the Citizen Safety Network Department can provide safety instructions in that language. In this way, by providing multilingual support, foreign residents can also receive appropriate safety instructions. Some or all of the above processing in the Citizen Safety Network Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Citizen Safety Network Department can input the user's language settings into a generative AI, and the generative AI can provide safety instructions in the most appropriate language.

[0128] The Citizen Safety Network Unit can update the user's current location information in real time when issuing safety instructions. For example, the Citizen Safety Network Unit can update the user's current location in real time while the user is moving and issue safety instructions. For example, the Citizen Safety Network Unit can update the user's current location in real time as the user approaches a dangerous area and provide optimal safety instructions. For example, if the user gets lost, the Citizen Safety Network Unit can update the user's current location in real time and issue safety instructions again. This allows for more appropriate safety instructions by updating the user's current location information in real time. Some or all of the above processing in the Citizen Safety Network Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Citizen Safety Network Unit can input the user's location information into a generative AI, which can then analyze the data and issue safety instructions.

[0129] The Citizen Safety Network Unit can provide optimal safety instructions by considering the user's health condition when issuing safety instructions. For example, if the user is tired, the Citizen Safety Network Unit can provide evacuation instructions via the shortest route. For example, if the user is seeking healthy exercise, the Citizen Safety Network Unit can provide an evacuation route that is slightly longer. For example, if the user is feeling unwell, the Citizen Safety Network Unit can provide an evacuation route that includes rest points. This allows for more appropriate safety instructions by considering the user's health condition. Some or all of the above processing in the Citizen Safety Network Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Citizen Safety Network Unit can input the user's health data into a generative AI, which can then analyze the data to provide optimal safety instructions.

[0130] The Citizen Safety Network Unit can select the optimal display method when issuing safety instructions, taking into account the user's device information. For example, if the user is using a smartphone, the Citizen Safety Network Unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the Citizen Safety Network Unit can provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the Citizen Safety Network Unit can provide a concise and highly visible display method. This makes it possible to provide more appropriate information by taking into account the user's device information. Some or all of the above processing in the Citizen Safety Network Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Citizen Safety Network Unit can input the user's device information into a generative AI, and the generative AI can analyze the data and select the optimal display method.

[0131] The Citizen Safety Network Unit can provide multilingual safety instructions according to the user's language settings when issuing safety instructions. For example, the Citizen Safety Network Unit can automatically set the language of safety instructions based on the language settings of the user's device. For example, the Citizen Safety Network Unit can provide a language switching function if the user uses multiple languages. For example, if the user selects a specific language, the Citizen Safety Network Unit can provide safety instructions in that language. This enables the provision of more appropriate information by providing multilingual safety instructions according to the user's language settings. Some or all of the above processing in the Citizen Safety Network Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Citizen Safety Network Unit can input the user's language settings into a generative AI, which can then analyze the data to provide multilingual safety instructions.

[0132] The Citizen Safety Network Unit can provide scheduled safety instructions by referring to the user's calendar information. For example, the Citizen Safety Network Unit can automatically set safety instructions by referring to the schedule registered in the user's calendar. For example, the Citizen Safety Network Unit can provide safety instructions by considering locations related to specific events from the user's calendar information. For example, the Citizen Safety Network Unit can suggest the optimal evacuation route based on the schedule, using the user's calendar information. This makes it possible to provide more appropriate safety instructions by referring to the user's calendar information. Some or all of the above processing in the Citizen Safety Network Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Citizen Safety Network Unit can input the user's calendar information into a generative AI, which can analyze the data and provide scheduled safety instructions.

[0133] The Citizen Safety Network Unit can provide optimal safety instructions by referring to the user's past movement history when issuing safety instructions. For example, the Citizen Safety Network Unit can provide safety instructions by considering places the user has frequently visited in the past. For example, the Citizen Safety Network Unit can predict and suggest evacuation routes to be used during a specific time period based on the user's past movement history. For example, the Citizen Safety Network Unit can analyze the user's past movement patterns and suggest the most efficient evacuation route. This makes it possible to provide more appropriate safety instructions by referring to the user's past movement history. Some or all of the above processing in the Citizen Safety Network Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Citizen Safety Network Unit can input the user's past movement history into a generative AI, which can then analyze the data and provide optimal safety instructions.

[0134] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0135] The data collection unit can estimate the user's emotions and adjust the priority of data collection based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can prioritize collecting urgent data. For example, if the user is calm, the data collection unit can collect detailed data to improve the accuracy of the analysis. For example, if the user is confused, the data collection unit can prioritize collecting concise and important data. This allows for more appropriate data collection by adjusting the priority of data collection based on 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 a generative AI, or not. For example, the data collection unit can input user emotion data into a generative AI, which can analyze the data and adjust the priority of data collection.

[0136] The predictive analytics unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, if the user is feeling anxious, the predictive analytics unit can provide a concise and highly visible display method. For example, if the user is calm, the predictive analytics unit can provide a display method that includes detailed information. For example, if the user is confused, the predictive analytics unit can provide a display method that gets straight to the point. By adjusting the display method of the prediction results based on the user's emotions, it becomes possible to provide more appropriate information. 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 predictive analytics unit may be performed using a generative AI, for example, or without a generative AI. For example, the predictive analytics unit can input user emotion data into a generative AI, and the generative AI can analyze the data and adjust the display method of the prediction results.

[0137] The resource allocation unit can estimate the user's emotions and adjust resource allocation priorities based on the estimated emotions. For example, if the user is feeling anxious, the resource allocation unit will prioritize allocating urgent resources. For example, if the user is calm, the resource allocation unit can allocate resources in detail. For example, if the user is confused, the resource allocation unit will prioritize allocating concise and important resources. This allows for more appropriate resource allocation by adjusting resource allocation priorities based on the user's emotions. 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 resource allocation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the resource allocation unit can input user emotion data into a generative AI, which can analyze the data and adjust resource allocation priorities.

[0138] The communication unit can estimate the user's emotions and adjust the priority of communication content based on the estimated emotions. For example, if the user is feeling anxious, the communication unit will prioritize sending urgent communication content. For example, if the user is calm, the communication unit can send detailed communication content. For example, if the user is confused, the communication unit will prioritize sending concise and important communication content. This allows for the provision of more appropriate information by adjusting the priority of communication content based on 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 communication unit may be performed using a generative AI, or not using a generative AI. For example, the communication unit can input user emotion data into a generative AI, which can analyze the data and adjust the priority of communication content.

[0139] The Citizen Safety Network Unit can estimate the user's emotions and adjust the display method of safety instructions based on the estimated user emotions. For example, if the user is feeling anxious, the Citizen Safety Network Unit can provide a concise and highly visible display method. For example, if the user is calm, the Citizen Safety Network Unit can provide a display method that includes detailed information. For example, if the user is confused, the Citizen Safety Network Unit can provide a display method that gets straight to the point. By adjusting the display method of safety instructions based on the user's emotions, it becomes possible to provide more appropriate information. 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 Citizen Safety Network Unit may be performed using a generative AI, or not using a generative AI. For example, the Citizen Safety Network Unit can input user emotion data into a generative AI, and the generative AI can analyze the data and adjust the display method of safety instructions.

[0140] The data collection unit can optimize its data collection method by referring to past disaster data during data collection. For example, the data collection unit can prioritize collecting data around the epicenter based on past earthquake data. For example, the data collection unit can focus on collecting wind speed and rainfall data based on past typhoon data. For example, the data collection unit can prioritize collecting coastal water level data based on past tsunami data. This allows the data collection method to be optimized by referring to past disaster data. Some or all of the above processing in the data collection unit may be performed using, for example, a generation AI, or without a generation AI. For example, the data collection unit can input past disaster data into a generation AI, and the generation AI can analyze the data to optimize the data collection method.

[0141] The predictive analysis unit can optimize its prediction algorithm by referring to past disaster patterns during predictive analysis. For example, the predictive analysis unit can optimize an algorithm to predict the impact around the epicenter based on past earthquake data. For example, the predictive analysis unit can optimize an algorithm to predict the impact of wind speed and rainfall based on past typhoon data. For example, the predictive analysis unit can optimize an algorithm to predict the impact on coastal areas based on past tsunami data. In this way, the prediction algorithm can be optimized by referring to past disaster patterns. Some or all of the above processing in the predictive analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the predictive analysis unit can input past disaster data into a generative AI, and the generative AI can analyze the data to optimize the prediction algorithm.

[0142] The resource allocation unit can optimize the allocation method by referring to past resource allocation data when allocating resources. For example, the resource allocation unit can optimize resource allocation around the epicenter based on resource allocation data from past earthquakes. For example, the resource allocation unit can optimize resource allocation in areas along the path of typhoons based on resource allocation data from past typhoons. For example, the resource allocation unit can optimize resource allocation in coastal areas based on resource allocation data from past tsunamis. In this way, the allocation method can be optimized by referring to past resource allocation data. Some or all of the above processing in the resource allocation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the resource allocation unit can input past resource allocation data into a generation AI, and the generation AI can analyze the data to optimize the allocation method.

[0143] The communications unit can optimize the communication method by referring to past communication data during communication. For example, the communications unit can optimize the communication method around the epicenter based on communication data from past earthquakes. For example, the communications unit can optimize the communication method for areas along the path of a typhoon based on communication data from past typhoons. For example, the communications unit can optimize the communication method for coastal areas based on communication data from past tsunamis. In this way, the communications method can be optimized by referring to past communication data. Some or all of the above processing in the communications unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the communications unit can input past communication data into a generative AI, and the generative AI can analyze the data and optimize the communication method.

[0144] The Citizen Safety Network Unit can provide optimal safety instructions by considering the user's health condition when issuing safety instructions. For example, if the user is tired, the Citizen Safety Network Unit can provide evacuation instructions via the shortest route. For example, if the user is seeking healthy exercise, the Citizen Safety Network Unit can provide an evacuation route that is slightly longer. For example, if the user is feeling unwell, the Citizen Safety Network Unit can provide an evacuation route that includes rest points. This allows for more appropriate safety instructions by considering the user's health condition. Some or all of the above processing in the Citizen Safety Network Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Citizen Safety Network Unit can input the user's health data into a generative AI, which can then analyze the data to provide optimal safety instructions.

[0145] The following briefly describes the processing flow for example form 2.

[0146] Step 1: The data collection unit gathers information from multiple data sources. For example, it can collect information from satellites, IoT sensors, social media, and official channels. The data collection unit acquires data from satellites in real time to monitor the situation of the disaster. It can also collect earthquake vibration data and tsunami water level data using IoT sensors. Furthermore, it can analyze posts from social media to collect information related to the disaster. It also collects information from official channels and uses official announcements from the government and local authorities to carry out disaster response. Step 2: The Predictive Analysis Unit analyzes the information collected by the Data Collection Unit to predict the impact of the disaster and the need for resources. For example, AI can be used to predict the epicenter of an earthquake and the arrival time of a tsunami. Furthermore, it can also predict the extent of the disaster's impact and the degree of damage. The degree of damage is predicted based on the distance from the earthquake's epicenter and topographical information. Step 3: The resource allocation unit optimizes resource allocation based on the prediction results obtained by the predictive analysis unit. For example, it can optimize the allocation of emergency response personnel and supplies. AI can also be used to optimize the deployment of emergency response personnel and the allocation of supplies. The optimal deployment is made based on the extent of damage and the number of emergency response personnel. Step 4: The communications department facilitates seamless communication between multiple agencies. For example, it can streamline information sharing between disaster response agencies. AI can also be used to optimize information sharing between disaster response agencies. Information can be shared in real time between disaster response agencies, enabling a rapid response. Step 5: The Citizen Safety Network Department provides citizens with real-time updates and individual safety instructions. For example, it can issue evacuation orders to citizens in the event of a disaster. It can also use AI to provide optimal evacuation instructions to citizens. It can provide the optimal evacuation route based on citizens' location information.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the data collection unit, predictive analysis unit, resource allocation unit, communication unit, and citizen safety network 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 information using the camera 42 and sensors of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The predictive analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts the impact of a disaster and the need for resources based on the collected data. The resource allocation unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes resource allocation based on the prediction results. The communication unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12 and facilitates information sharing between multiple organizations. The citizen safety network unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12 and provides citizens with real-time update information and individual safety instructions. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0151] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the data collection unit, predictive analysis unit, resource allocation unit, communication unit, and citizen safety network unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects information using the camera 42 and sensors of the smart glasses 214 and analyzes it by the specific processing unit 290 of the data processing unit 12. The predictive analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts the impact of a disaster and the need for resources based on the collected data. The resource allocation unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the allocation of resources based on the prediction results. The communication unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12 and facilitates information sharing between multiple organizations. The citizen safety network unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12 and provides citizens with real-time updated information and individual safety instructions. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0167] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] Each of the multiple elements described above, including the data collection unit, predictive analysis unit, resource allocation unit, communication unit, and citizen safety network 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 information using the camera 42 and sensors of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The predictive analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts the impact of a disaster and the need for resources based on the collected data. The resource allocation unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes resource allocation based on the prediction results. The communication unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12 and facilitates information sharing between multiple organizations. The citizen safety network unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12 and provides citizens with real-time update information and individual safety instructions. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0183] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.).

[0196] 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.

[0197] 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.

[0198] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0199] Each of the multiple elements described above, including the data collection unit, predictive analysis unit, resource allocation unit, communication unit, and citizen safety network unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects information using the camera 42 and sensors of the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. The predictive analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts the impact of a disaster and the need for resources based on the collected data. The resource allocation unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes resource allocation based on the prediction results. The communication unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12 and facilitates information sharing between multiple organizations. The citizen safety network unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12 and provides citizens with real-time updated information and individual safety instructions. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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."

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] (Note 1) A collection unit that collects information from multiple data sources, A predictive analysis unit analyzes the information collected by the aforementioned collection unit to predict the impact of the disaster and the need for resources, A resource allocation unit optimizes resource allocation based on the prediction results obtained by the predictive analysis unit, The Communications Department facilitates seamless communication between multiple agencies, It includes a Citizen Safety Network Department that provides citizens with real-time updates and individual safety instructions. A system characterized by the following features. (Note 2) The aforementioned collection unit is Gather information from satellites, IoT sensors, social media, and official channels. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned predictive analysis unit, Using AI to predict the impact of disasters and resource needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The resource allocation unit, Optimize the allocation of emergency response personnel and supplies based on prediction results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned communications unit is Facilitating seamless communication between multiple organizations The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned Citizen Safety Network Department Providing citizens with real-time updates and personalized safety instructions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned Citizen Safety Network Department Providing multilingual support to assist foreign residents. The system described in Appendix 6, characterized by the features described herein. (Note 8) The aforementioned predictive analysis unit, We generate scenarios and create simulations for preparation planning. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned predictive analysis unit, We use natural language processing to analyze emergency communications and social media to perform real-time situation assessments. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned predictive analysis unit, Using computer vision, we process satellite and drone images to assess damage and direct rescue operations. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned predictive analysis unit, Predicting the impact of a disaster and resource needs based on current data and historical patterns. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates user sentiment and adjusts data collection priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting data, we optimize the collection method by referring to past disaster data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting data, different collection algorithms are applied depending on the type of data being collected. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is It estimates the user's emotions and filters the data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is When collecting data, adjust the collection range considering geographical information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is During data collection, analyze social media trends and prioritize the collection of relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned predictive analysis unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned predictive analysis unit, During predictive analysis, refer to past disaster patterns to optimize the prediction algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned predictive analysis unit, When performing predictive analysis, different prediction models are applied depending on the type of disaster. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned predictive analysis 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 22) The aforementioned predictive analysis unit, When performing predictive analytics, adjust the forecast range by taking geographical information into account. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned predictive analysis unit, During predictive analytics, social media data is analyzed to provide real-time situational assessments. The system described in Appendix 1, characterized by the features described herein. (Note 24) The resource allocation unit, It estimates user sentiment and adjusts resource allocation priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The resource allocation unit, When allocating resources, the allocation method is optimized by referring to past resource allocation data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The resource allocation unit, When allocating resources, different allocation algorithms are applied depending on the type of disaster. The system described in Appendix 1, characterized by the features described herein. (Note 27) The resource allocation unit, It estimates the user's emotions and adjusts how resource allocation is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The resource allocation unit, When allocating resources, adjust the allocation range by taking geographical information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The resource allocation unit, When allocating resources, social media data is analyzed to assess real-time resource needs. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned communications unit is It estimates the user's emotions and adjusts the priority of communication content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned communications unit is During communication, the system optimizes the communication method by referring to past communication data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned communications unit is When communicating, different communication protocols are applied depending on the type of disaster. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned communications unit is It estimates the user's emotions and adjusts how communication content is displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned communications unit is During communication, the communication range is adjusted taking geographical information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned communications unit is During communication, social media data is analyzed to assess real-time communication needs. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned Citizen Safety Network Department The system estimates the user's emotions and adjusts how safety instructions are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned Citizen Safety Network Department When issuing safety instructions, refer to past disaster data to optimize the instruction method. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned Citizen Safety Network Department When issuing safety instructions, different instruction algorithms are applied depending on the type of disaster. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned Citizen Safety Network Department The system estimates the user's emotions and prioritizes safety instructions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned Citizen Safety Network Department Adjust the instruction range considering geographical information during safety instructions The system according to Appendix 1, characterized by the above. (Appendix 41) The citizen safety network department Analyzes social media data to evaluate real-time safety needs during safety instructions The system according to Appendix 1, characterized by the above. (Appendix 42) The citizen safety network department Provides multilingual support to assist foreign residents The system according to Appendix 6, characterized by the above. (Appendix 43) The citizen safety network department Updates the user's current location information in real time and gives safety instructions during safety instructions The system according to Appendix 1, characterized by the above. (Appendix 44) The citizen safety network department Considers the user's health status and provides optimal safety instructions during safety instructions The system according to Appendix 1, characterized by the above. (Appendix 45) The citizen safety network department Selects an optimal display method considering the user's device information during safety instructions The system according to Appendix 1, characterized by the above. (Appendix 46) The citizen safety network department Provides multilingual safety instructions according to the user's language settings during safety instructions The system according to Appendix 1, characterized by the above. (Appendix 47) The citizen safety network department Refers to the user's calendar information and makes proposals based on the schedule during safety instructions The system according to Appendix 1, characterized by the above. (Appendix 48) The citizen safety network department When giving safety instructions, provide optimal safety instructions by referring to the user's past movement history The system according to appended note 1, characterized by this

Explanation of symbols

[0219] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects information from multiple data sources, A predictive analysis unit analyzes the information collected by the aforementioned collection unit to predict the impact of the disaster and the need for resources, A resource allocation unit optimizes resource allocation based on the prediction results obtained by the predictive analysis unit, The Communications Department facilitates seamless communication between multiple agencies, It includes a Citizen Safety Network Department that provides citizens with real-time updates and individual safety instructions. A system characterized by the following features.

2. The aforementioned collection unit is Gather information from satellites, IoT sensors, social media, and official channels. The system according to feature 1.

3. The aforementioned predictive analysis unit, Using AI to predict the impact of disasters and the need for resources. The system according to feature 1.

4. The resource allocation unit, Optimize the allocation of emergency response personnel and supplies based on prediction results. The system according to feature 1.

5. The aforementioned communications unit is Facilitating seamless communication between multiple organizations The system according to feature 1.

6. The aforementioned Citizen Safety Network Department Providing citizens with real-time updates and personalized safety instructions. The system according to feature 1.

7. The aforementioned Citizen Safety Network Department Providing multilingual support to assist foreign residents. The system described in claim 6.

8. The aforementioned predictive analysis unit, We generate scenarios and create simulations for preparation planning. The system according to feature 1.

Images