system

Abstract

The system according to this embodiment aims to analyze information during a disaster, set priorities according to the urgency, and efficiently allocate resources. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a priority setting unit, and a resource management unit. The collection unit collects real-time data collected during a disaster. The analysis unit analyzes the data collected by the collection unit. The priority setting unit sets priorities according to the urgency based on the data analyzed by the analysis unit. The resource management unit efficiently allocates personnel and supplies based on the priorities set by the priority setting unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult to process a huge amount of information and set optimal priorities in case of disasters, and there is a risk that the distribution of resources becomes inefficient.

[0005] The system according to the embodiment aims to analyze information in case of disasters, set priorities according to the urgency, and efficiently distribute resources.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a priority setting unit, and a resource management unit. The data collection unit collects real-time data during a disaster. The analysis unit analyzes the data collected by the data collection unit. The priority setting unit sets priorities according to urgency based on the data analyzed by the analysis unit. The resource management unit efficiently allocates personnel and supplies based on the priorities set by the priority setting unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze information during a disaster, set priorities according to the urgency, and efficiently allocate resources. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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) The triage AI agent system according to an embodiment of the present invention is a system designed to support a rapid and accurate response during disasters. The triage AI agent system analyzes real-time data collected during a disaster and automatically sets priorities for rescue and distribution of relief supplies based on urgency. Furthermore, it works in conjunction with a resource management system to efficiently allocate personnel and supplies. This ensures that appropriate support reaches the most affected areas and regions quickly. For example, the triage AI agent system analyzes real-time data collected during a disaster. This data includes the situation in the affected area, the number of victims, and the types of relief supplies needed. Next, based on the analyzed data, it sets priorities according to urgency. For example, it prioritizes the rescue of seriously injured people, followed by the distribution of food and medicine. Furthermore, the triage AI agent system works in conjunction with a resource management system to efficiently allocate personnel and supplies. For example, optimizing the deployment of rescue teams and the delivery routes of relief supplies enables rapid and efficient support. In addition, AI-based data analysis improves the efficiency of support planning and contributes to cost reduction. This system automates decision-making based on urgency, enabling rapid and appropriate rescue operations and resource management. This significantly reduces the risk of delays in providing assistance to disaster victims and improves social trust and satisfaction. Furthermore, the triage AI agent system targets emergency rescue agencies, NGOs and aid organizations, and disaster management personnel, making it extremely useful in today's world where the demand for disaster response technology is increasing. The vision of the triage AI agent system is to save many lives through the provision of rapid and accurate information in emergency response during disasters. This is expected to lead to more efficient disaster response and improved reliability. As a result, the triage AI agent system enables rapid and accurate rescue operations and resource management.

[0029] The triage AI agent system according to this embodiment comprises a collection unit, an analysis unit, a priority setting unit, and a resource management unit. The collection unit collects real-time data collected during a disaster. The collection unit can collect, for example, sensor data, video data, and audio data. The collection unit can also collect information on the situation in the disaster area using drones and sensors. For example, the collection unit can collect aerial footage of the disaster area using a drone. The collection unit can also collect environmental data such as temperature and humidity using sensors. Furthermore, the collection unit can collect audio data of disaster victims to understand their situation. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data using generative AI. For example, the analysis unit analyzes the data using deep learning technology. The analysis unit can also analyze audio data using natural language processing technology. Furthermore, the analysis unit can also analyze video data using image recognition technology. The priority setting unit sets priorities according to the urgency based on the data analyzed by the analysis unit. The priority setting unit includes an algorithm that sets priorities according to the urgency based on the analysis results. For example, the priority setting unit sets priorities based on the scale of the damage and the number of victims. The priority setting unit can also set priorities based on the situation in the affected area. Furthermore, the priority setting unit can also set priorities based on the health status of the victims. The resource management unit efficiently allocates personnel and supplies based on the priorities set by the priority setting unit. The resource management unit includes protocols for efficiently allocating personnel and supplies in cooperation with the resource management system. For example, the resource management unit optimizes the deployment of rescue teams and the delivery routes of relief supplies. Furthermore, the resource management unit can optimize the deployment of rescue teams and the delivery routes of relief supplies in cooperation with the resource management system. As a result, the triage AI agent system according to this embodiment can achieve rapid and accurate rescue operations and resource management.

[0030] The data collection unit collects real-time data during disasters. This unit can collect, for example, sensor data, video data, and audio data. Specifically, it uses drones and sensors to collect information about the situation in disaster-stricken areas. Drones fly over the disaster area, collecting wide-area aerial footage in real time. This allows for a rapid assessment of the overall situation in the affected area. Drones are also equipped with infrared and thermal imaging cameras, enabling the identification of victims' locations even at night or in poor visibility conditions. Sensors are installed on the ground to collect environmental data such as temperature, humidity, gas concentration, and vibration. This allows for early detection of dangers such as fires and hazardous gas leaks. Furthermore, the data collection unit uses speech recognition technology to collect audio data from victims. It collects real-time calls for help and descriptions of the situation from victims and transmits them to the analysis unit. This allows for a rapid assessment of the victims' locations and conditions. The data collection unit has a communication protocol for centrally managing and transmitting this data to the analysis unit. This ensures that collected data is transmitted quickly and accurately to the analysis unit, enabling real-time situation assessment.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data using generative AI. Specifically, it analyzes the data using deep learning technology. Deep learning technology can learn from large amounts of data and perform advanced pattern recognition. For example, it uses image recognition technology to analyze video data taken by drones to understand the situation in the disaster area in detail. It can identify the extent of building collapses, road closures, and fire outbreaks. It also uses natural language processing technology to analyze the voice data of disaster victims. It converts the voices of victims calling for help and describing their situation into text data to understand their location and condition. Furthermore, it analyzes sensor data and can detect dangerous situations early based on environmental data such as temperature, humidity, and gas concentration. The analysis unit integrates this data to understand the situation of the entire disaster area in real time. The analysis results are transmitted to the priority setting unit and used to set priorities according to the urgency.

[0032] The priority setting unit sets priorities according to urgency based on the data analyzed by the analysis unit. The priority setting unit is equipped with an algorithm that sets priorities according to urgency based on the analysis results. Specifically, priorities are set based on the scale of the damage and the number of victims. For example, situations that endanger lives, such as building collapses or fires, are given the highest priority. Priorities can also be set based on the situation in the affected area. For example, situations that hinder rescue operations, such as road closures or bridge collapses, are given priority. Furthermore, priorities can also be set based on the health status of the victims. For example, seriously injured people, the elderly, and children, who require special rescue, are given priority. The priority setting unit makes a comprehensive judgment based on this information and sets the optimal priorities. The set priorities are transmitted to the resource management unit and used for the efficient allocation of personnel and supplies.

[0033] The Resource Management Department efficiently allocates personnel and supplies based on priorities set by the Prioritization Department. The Resource Management Department has protocols for efficiently allocating personnel and supplies in conjunction with the Resource Management System. Specifically, it optimizes the deployment of rescue teams and the delivery routes of relief supplies. For example, it quickly dispatches rescue teams to high-priority areas and prioritizes the delivery of necessary relief supplies. Furthermore, the Resource Management Department continuously optimizes the deployment of rescue teams and the delivery routes of relief supplies based on real-time updated data. This enables rapid response to changing situations and efficient resource allocation. In addition, the Resource Management Department has protocols for optimizing the deployment of rescue teams and the delivery routes of relief supplies in conjunction with the Resource Management System. This allows the Resource Management Department to achieve rapid and accurate rescue operations and resource management.

[0034] The data collection unit can collect information about the situation in disaster-stricken areas using drones and sensors. For example, the unit can collect aerial footage of the disaster area using drones. The unit can optimize the drone's flight path to efficiently cover the entire disaster area. The unit can also collect environmental data such as temperature and humidity using sensors. The unit can adjust the placement of sensors to prioritize data collection in critical areas. Furthermore, the unit can collect audio data of disaster victims to understand their situation. The unit can enhance the coordination between drones and sensors to grasp detailed situations in real time. This allows for detailed collection of information about the situation in disaster-stricken areas.

[0035] The analysis unit can analyze data collected using generative AI. For example, the analysis unit can analyze data using deep learning technology. By using deep learning technology, the analysis unit can improve the accuracy of data analysis. Furthermore, the analysis unit can analyze audio data using natural language processing technology. By using natural language processing technology, the analysis unit can improve the accuracy of audio data analysis. In addition, the analysis unit can analyze video data using image recognition technology. By using image recognition technology, the analysis unit can improve the accuracy of video data analysis. Thus, using generative AI improves the accuracy of data analysis.

[0036] The priority setting unit includes an algorithm that sets priorities according to urgency based on the analysis results. For example, the priority setting unit sets priorities based on the scale of the damage and the number of affected people. The priority setting unit can set higher priorities the larger the scale of the damage. It can also set higher priorities the larger the number of affected people. Furthermore, the priority setting unit can also set priorities based on the situation in the affected area. The priority setting unit can set higher priorities the worse the situation in the affected area. This allows for the automatic setting of priorities according to urgency.

[0037] The Resource Management Department has protocols for efficiently allocating personnel and supplies in conjunction with the resource management system. For example, the Resource Management Department optimizes the deployment of rescue teams and the delivery routes for relief supplies. By optimizing the deployment of rescue teams, the Resource Management Department enables rapid and efficient assistance. Furthermore, by optimizing the delivery routes for relief supplies, the Resource Management Department enables rapid and efficient assistance. In addition, the Resource Management Department can optimize the deployment of rescue teams and the delivery routes for relief supplies in conjunction with the resource management system. This allows for the efficient allocation of personnel and supplies.

[0038] The Resource Management Department can optimize the deployment of rescue teams and the delivery routes of relief supplies. For example, the Resource Management Department can optimize the deployment of rescue teams. By optimizing the deployment of rescue teams, the Resource Management Department can provide rapid and efficient assistance. The Resource Management Department can also optimize the delivery routes of relief supplies. By optimizing the delivery routes of relief supplies, the Resource Management Department can provide rapid and efficient assistance. Furthermore, the Resource Management Department has protocols for optimizing the deployment of rescue teams and the delivery routes of relief supplies. This enables rapid and efficient assistance by optimizing the deployment of rescue teams and the delivery routes of relief supplies.

[0039] The data collection unit can optimize the placement of drones and sensors to collect more detailed information about the disaster area. For example, the data collection unit can optimize drone flight routes to efficiently cover the entire disaster area. The data collection unit can also adjust sensor placement to prioritize data collection from critical areas. Furthermore, the data collection unit can enhance the coordination between drones and sensors to gain a real-time, detailed understanding of the situation. This allows for more detailed data collection of the disaster area by optimizing the placement of drones and sensors. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input drone and sensor placement data into a generating AI and have the generating AI perform the optimization of the placement.

[0040] The data collection unit can dynamically change the type of data it collects according to the situation in the disaster area. For example, if the situation in the disaster area changes, the data collection unit can automatically change the type of data it collects. By automatically changing the type of data it collects when the situation in the disaster area changes, the data collection unit can prioritize the collection of necessary data. The data collection unit can also prioritize the collection of necessary data according to the situation in the disaster area. The data collection unit can prioritize the collection of necessary data according to the situation in the disaster area. Furthermore, the data collection unit can adjust the type of data it collects in real time based on the situation in the disaster area. By adjusting the type of data it collects in real time based on the situation in the disaster area, the data collection unit can prioritize the collection of necessary data. This allows for the priority collection of necessary data by dynamically changing the type of data collected according to the situation in the disaster area. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input disaster area situation data into a generating AI and have the generating AI change the type of data to collect.

[0041] The data collection unit can adjust its data collection method while considering the geographical characteristics of the disaster area. For example, the data collection unit can adjust the drone's flight route according to the terrain of the disaster area. By adjusting the drone's flight route according to the terrain of the disaster area, the data collection unit can efficiently collect data. The data collection unit can also optimize the placement of sensors based on the geographical characteristics of the disaster area. By optimizing the placement of sensors based on the geographical characteristics of the disaster area, the data collection unit can efficiently collect data. Furthermore, the data collection unit can change the data collection method while considering the geographical characteristics of the disaster area. By changing the data collection method while considering the geographical characteristics of the disaster area, the data collection unit can efficiently collect data. Thus, by adjusting the data collection method while considering the geographical characteristics of the disaster area, efficient data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input geographical characteristic data of the disaster area into a generating AI and have the generating AI perform the adjustment of the data collection method.

[0042] The data collection unit can select data collection methods while considering the infrastructure conditions of the disaster-stricken area. For example, the data collection unit can decide whether to use drones or sensors depending on the infrastructure conditions of the disaster-stricken area. By deciding whether to use drones or sensors depending on the infrastructure conditions of the disaster-stricken area, the data collection unit can achieve optimal data collection. The data collection unit can also select data collection methods while considering the infrastructure conditions of the disaster-stricken area. By selecting data collection methods while considering the infrastructure conditions of the disaster-stricken area, the data collection unit can achieve optimal data collection. Furthermore, the data collection unit can select the optimal data collection method based on the infrastructure conditions of the disaster-stricken area. By selecting the optimal data collection method based on the infrastructure conditions of the disaster-stricken area, the data collection unit can achieve optimal data collection. Thus, by selecting data collection methods while considering the infrastructure conditions of the disaster-stricken area, optimal data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input disaster-stricken area infrastructure data into a generating AI and have the generating AI select the data collection method.

[0043] The analysis unit can optimize its analysis algorithm by referring to past disaster data during analysis. For example, the analysis unit adjusts the analysis algorithm based on past disaster data. By adjusting the analysis algorithm based on past disaster data, the analysis unit can improve the accuracy of the analysis. The analysis unit can also improve the accuracy of the analysis by referring to past disaster data. By improving the accuracy of the analysis by referring to past disaster data, the analysis unit can provide more accurate analysis results. Furthermore, the analysis unit can optimize the analysis algorithm by utilizing past disaster data. By optimizing the analysis algorithm by utilizing past disaster data, the analysis unit can improve the accuracy of the analysis. As a result, the accuracy of the analysis is improved by optimizing the analysis algorithm by referring to past disaster data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past disaster data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0044] The analysis unit can evaluate the reliability of the collected data during analysis and prioritize the analysis of highly reliable data. For example, the analysis unit evaluates the reliability of the collected data and prioritizes the analysis of highly reliable data. By evaluating the reliability of the collected data and prioritizing the analysis of highly reliable data, the analysis unit can provide highly accurate results. Furthermore, the analysis unit can also determine the priority of analysis based on the reliability of the data. By determining the priority of analysis based on the reliability of the data, the analysis unit can provide highly accurate results. In addition, the analysis unit can prioritize the analysis of highly reliable data and provide highly accurate results. By prioritizing the analysis of highly reliable data and providing highly accurate results, the analysis unit can provide highly accurate results. This allows for the evaluation of the reliability of the collected data and the prioritization of the analysis of highly reliable data, thereby providing highly accurate results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the reliability of the collected data into a generating AI and have the generating AI perform the reliability evaluation.

[0045] The analysis unit can select an analysis method while considering the socioeconomic conditions of the disaster-stricken area. For example, the analysis unit selects an analysis method based on the socioeconomic conditions of the disaster-stricken area. By selecting an analysis method based on the socioeconomic conditions of the disaster-stricken area, the analysis unit can perform the optimal analysis. The analysis unit can also select the optimal analysis method while considering the socioeconomic conditions of the disaster-stricken area. By selecting the optimal analysis method while considering the socioeconomic conditions of the disaster-stricken area, the analysis unit can perform the optimal analysis. Furthermore, the analysis unit can adjust the analysis method according to the socioeconomic conditions of the disaster-stricken area. By adjusting the analysis method according to the socioeconomic conditions of the disaster-stricken area, the analysis unit can perform the optimal analysis. Thus, by selecting an analysis method while considering the socioeconomic conditions of the disaster-stricken area, the optimal analysis can be achieved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input socioeconomic data of the disaster-stricken area into a generating AI and have the generating AI perform the selection of the analysis method.

[0046] The analysis unit can improve the accuracy of its analysis by referring to weather data from the disaster-stricken area during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to weather data from the disaster-stricken area. By improving the accuracy of the analysis by referring to weather data from the disaster-stricken area, the analysis unit can provide more accurate analysis results. Furthermore, the analysis unit can adjust its analysis method based on the weather data. By adjusting its analysis method based on the weather data, the analysis unit can provide more accurate analysis results. In addition, the analysis unit can improve the accuracy of its analysis by utilizing weather data from the disaster-stricken area. By improving the accuracy of the analysis by utilizing weather data from the disaster-stricken area, the analysis unit can provide more accurate analysis results. Thus, by improving the accuracy of the analysis by referring to weather data from the disaster-stricken area, more accurate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input weather data from the disaster-stricken area into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0047] The priority setting unit can set priorities while considering the health status of the victims. For example, the priority setting unit sets priorities based on the health status of the victims. By setting priorities based on the health status of the victims, the priority setting unit can prioritize the rescue of seriously injured victims. The priority setting unit can also prioritize the rescue of victims whose health status is deteriorating. By prioritizing the rescue of victims whose health status is deteriorating, the priority setting unit can prioritize the rescue of seriously injured victims. Furthermore, the priority setting unit can also adjust priorities while considering the health status of the victims. By adjusting priorities while considering the health status of the victims, the priority setting unit can prioritize the rescue of seriously injured victims. As a result, by setting priorities while considering the health status of the victims, it is possible to prioritize the rescue of seriously injured victims. Some or all of the above-described processes in the priority setting unit may be performed using AI, for example, or without using AI. For example, the priority setting unit can input victim health status data into a generating AI and have the generating AI perform the priority setting.

[0048] The priority setting unit can set priorities while considering the infrastructure situation in the disaster-stricken area. For example, the priority setting unit sets priorities based on the infrastructure situation in the disaster-stricken area. By setting priorities based on the infrastructure situation in the disaster-stricken area, the priority setting unit can prioritize support to areas with damaged infrastructure. The priority setting unit can also prioritize support to areas with damaged infrastructure. By prioritizing support to areas with damaged infrastructure, the priority setting unit can prioritize support to areas with damaged infrastructure. Furthermore, the priority setting unit can adjust priorities while considering the infrastructure situation in the disaster-stricken area. By adjusting priorities while considering the infrastructure situation in the disaster-stricken area, the priority setting unit can prioritize support to areas with damaged infrastructure. As a result, by setting priorities while considering the infrastructure situation in the disaster-stricken area, areas with damaged infrastructure can be prioritized for support. Some or all of the above processing in the priority setting unit may be performed using AI, for example, or without using AI. For example, the priority setting unit can input infrastructure situation data of the disaster-stricken area into a generating AI and have the generating AI perform the priority setting.

[0049] The priority setting unit can set priorities while considering the geographical characteristics of the disaster-stricken area. For example, the priority setting unit sets priorities based on the geographical characteristics of the disaster-stricken area. By setting priorities based on the geographical characteristics of the disaster-stricken area, the priority setting unit can prioritize support for geographically isolated areas. The priority setting unit can also prioritize support for geographically isolated areas. By prioritizing support for geographically isolated areas, the priority setting unit can prioritize support for geographically isolated areas. Furthermore, the priority setting unit can adjust priorities while considering the geographical characteristics of the disaster-stricken area. By adjusting priorities while considering the geographical characteristics of the disaster-stricken area, the priority setting unit can prioritize support for geographically isolated areas. As a result, by setting priorities while considering the geographical characteristics of the disaster-stricken area, geographically isolated areas can be prioritized. Some or all of the above processing in the priority setting unit may be performed using AI, for example, or without using AI. For example, the priority setting unit can input geographical characteristic data of the disaster-stricken area into a generating AI and have the generating AI perform the priority setting.

[0050] The priority setting unit can set priorities while considering the population density of the disaster-stricken area. For example, the priority setting unit sets priorities based on the population density of the disaster-stricken area. By setting priorities based on the population density of the disaster-stricken area, the priority setting unit can prioritize support for areas with high population density. The priority setting unit can also prioritize support for areas with high population density. By prioritizing support for areas with high population density, the priority setting unit can prioritize support for areas with high population density. Furthermore, the priority setting unit can adjust priorities while considering the population density of the disaster-stricken area. By adjusting priorities while considering the population density of the disaster-stricken area, the priority setting unit can prioritize support for areas with high population density. As a result, by setting priorities while considering the population density of the disaster-stricken area, areas with high population density can be prioritized for support. Some or all of the above processing in the priority setting unit may be performed using AI, for example, or without using AI. For example, the priority setting unit can input population density data of the disaster-stricken area into a generating AI and have the generating AI perform the priority setting.

[0051] The resource management unit can optimize the allocation route when allocating resources, taking into account the traffic conditions in the disaster-stricken area. For example, the resource management unit optimizes the allocation route based on the traffic conditions in the disaster-stricken area. By optimizing the allocation route based on the traffic conditions in the disaster-stricken area, the resource management unit enables efficient resource allocation. The resource management unit can also prioritize selecting routes that avoid traffic congestion. By prioritizing the selection of routes that avoid traffic congestion, the resource management unit enables efficient resource allocation. Furthermore, the resource management unit can determine the optimal allocation route by taking into account the traffic conditions in the disaster-stricken area. By determining the optimal allocation route by taking into account the traffic conditions in the disaster-stricken area, the resource management unit enables efficient resource allocation. Thus, by optimizing the allocation route by taking into account the traffic conditions in the disaster-stricken area, efficient resource allocation becomes possible. Some or all of the above processing in the resource management unit may be performed using AI, for example, or without using AI. For example, the resource management unit can input traffic condition data from the disaster-stricken area into a generating AI and have the generating AI perform the optimization of the allocation route.

[0052] The resource management unit can select a resource allocation method considering the infrastructure situation in the disaster-stricken area when allocating resources. For example, the resource management unit can select the optimal allocation method based on the infrastructure situation in the disaster-stricken area. By selecting the optimal allocation method based on the infrastructure situation in the disaster-stricken area, the resource management unit can achieve optimal resource allocation. The resource management unit can also use drones or helicopters in areas where infrastructure is damaged. By using drones or helicopters in areas where infrastructure is damaged, the resource management unit can achieve optimal resource allocation. Furthermore, the resource management unit can select an efficient allocation method considering the infrastructure situation in the disaster-stricken area. By selecting an efficient allocation method considering the infrastructure situation in the disaster-stricken area, the resource management unit can achieve optimal resource allocation. Thus, by selecting an allocation method considering the infrastructure situation in the disaster-stricken area, optimal resource allocation becomes possible. Some or all of the above processing in the resource management unit may be performed using AI, for example, or without AI. For example, the resource management unit can input infrastructure situation data of the disaster-stricken area into a generating AI and have the generating AI select the allocation method.

[0053] The resource management unit can adjust the resource allocation method when allocating resources, taking into account the geographical characteristics of the disaster-stricken area. For example, the resource management unit adjusts the resource allocation method based on the geographical characteristics of the disaster-stricken area. By adjusting the resource allocation method based on the geographical characteristics of the disaster-stricken area, the resource management unit can enable efficient resource allocation. The resource management unit can also use drones or helicopters in geographically isolated areas. By using drones or helicopters in geographically isolated areas, the resource management unit can enable efficient resource allocation. Furthermore, the resource management unit can select an efficient resource allocation method, taking into account the geographical characteristics of the disaster-stricken area. By selecting an efficient resource allocation method, taking into account the geographical characteristics of the disaster-stricken area, the resource management unit can enable efficient resource allocation. Thus, by adjusting the allocation method, taking into account the geographical characteristics of the disaster-stricken area, efficient resource allocation becomes possible. Some or all of the above processing in the resource management unit may be performed using AI, for example, or without AI. For example, the resource management unit can input geographical characteristic data of the disaster-stricken area into a generating AI and have the generating AI perform the adjustment of the allocation method.

[0054] The resource management unit can select a resource allocation method considering the population density of the disaster-stricken area when allocating resources. For example, the resource management unit can select the optimal allocation method based on the population density of the disaster-stricken area. By selecting the optimal allocation method based on the population density of the disaster-stricken area, the resource management unit can achieve optimal resource allocation. The resource management unit can also select a method for rapidly distributing large quantities of supplies to areas with high population density. By selecting a method for rapidly distributing large quantities of supplies to areas with high population density, the resource management unit can achieve optimal resource allocation. Furthermore, the resource management unit can also select an efficient allocation method considering the population density of the disaster-stricken area. By selecting an efficient allocation method considering the population density of the disaster-stricken area, the resource management unit can achieve optimal resource allocation. Thus, by selecting an allocation method considering the population density of the disaster-stricken area, optimal resource allocation becomes possible. Some or all of the above processing in the resource management unit may be performed using AI, for example, or without AI. For example, the resource management unit can input population density data of the disaster-stricken area into a generating AI and have the generating AI select the allocation method.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The triage AI agent system can optimize support plans by considering the geographical characteristics of disaster-stricken areas. For example, it can use drones and helicopters to deliver supplies to areas that are difficult to access, such as mountainous regions and remote islands. In urban areas, it can also calculate the optimal delivery route to avoid traffic congestion. Furthermore, it can set support priorities based on the terrain and infrastructure conditions of the disaster-stricken area. This enables efficient support tailored to the geographical conditions of the disaster-stricken area.

[0057] The triage AI agent system can monitor the health status of disaster victims in real time and provide necessary support quickly. For example, it can measure the body temperature and heart rate of disaster victims using sensors and immediately dispatch medical assistance if an abnormality is detected. It can also prioritize support based on the health status of the disaster victims. Furthermore, it can analyze health data to assess the health risks of the entire disaster area. This enables a swift and appropriate response to protect the health of disaster victims.

[0058] The triage AI agent system can adjust support plans to take into account the socioeconomic conditions of disaster-stricken areas. For example, it can provide special support to low-income areas or areas with a large elderly population. It can also prioritize infrastructure restoration to quickly restart economic activity in the affected areas. Furthermore, it can analyze socioeconomic data from the affected areas and propose the most effective support methods. This ensures that support is tailored to the socioeconomic needs of the disaster-stricken areas.

[0059] The triage AI agent system can collect weather data from disaster-stricken areas in real time and incorporate it into relief plans. For example, if a typhoon or heavy rain is expected, evacuation plans can be developed in advance. It can also adjust the delivery routes of relief supplies based on weather data. Furthermore, it can analyze weather data to assess the risks in disaster-stricken areas. This enables rapid and appropriate assistance tailored to weather conditions.

[0060] The triage AI agent system can monitor the infrastructure status in disaster-stricken areas in real time and incorporate this information into support plans. For example, if roads or bridges are damaged, it can calculate alternative routes and deliver relief supplies accordingly. It can also provide necessary support until infrastructure such as electricity and water is restored. Furthermore, it can analyze infrastructure data and propose the most effective support methods. This enables efficient support tailored to the infrastructure status of the disaster-stricken area.

[0061] The triage AI agent system can monitor traffic conditions in disaster-stricken areas in real time and incorporate this information into support plans. For example, if traffic congestion occurs, it can calculate the optimal delivery route to deliver relief supplies. It can also prioritize support based on traffic conditions. Furthermore, it can analyze traffic data to assess the overall traffic risk in the disaster area. This enables rapid and appropriate support tailored to traffic conditions.

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

[0063] Step 1: The collection unit collects real-time data gathered during a disaster. The collection unit can collect sensor data, video data, audio data, etc. For example, it can collect aerial footage of the disaster area using a drone, collect environmental data such as temperature and humidity using sensors, and collect audio data of disaster victims to understand their situation. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data using generative AI. For example, it can analyze data using deep learning technology, analyze audio data using natural language processing technology, or analyze video data using image recognition technology. Step 3: The priority setting unit sets priorities according to urgency based on the data analyzed by the analysis unit. The priority setting unit includes an algorithm that sets priorities based on the scale of the damage, the number of victims, the situation in the affected area, and the health status of the victims. Step 4: The resource management unit efficiently allocates personnel and supplies based on the priorities set by the priority setting unit. The resource management unit has protocols in place to optimize the deployment of rescue teams and the delivery routes of relief supplies in cooperation with the resource management system.

[0064] (Example of form 2) The triage AI agent system according to an embodiment of the present invention is a system designed to support a rapid and accurate response during disasters. The triage AI agent system analyzes real-time data collected during a disaster and automatically sets priorities for rescue and distribution of relief supplies based on urgency. Furthermore, it works in conjunction with a resource management system to efficiently allocate personnel and supplies. This ensures that appropriate support reaches the most affected areas and regions quickly. For example, the triage AI agent system analyzes real-time data collected during a disaster. This data includes the situation in the affected area, the number of victims, and the types of relief supplies needed. Next, based on the analyzed data, it sets priorities according to urgency. For example, it prioritizes the rescue of seriously injured people, followed by the distribution of food and medicine. Furthermore, the triage AI agent system works in conjunction with a resource management system to efficiently allocate personnel and supplies. For example, optimizing the deployment of rescue teams and the delivery routes of relief supplies enables rapid and efficient support. In addition, AI-based data analysis improves the efficiency of support planning and contributes to cost reduction. This system automates decision-making based on urgency, enabling rapid and appropriate rescue operations and resource management. This significantly reduces the risk of delays in providing assistance to disaster victims and improves social trust and satisfaction. Furthermore, the triage AI agent system targets emergency rescue agencies, NGOs and aid organizations, and disaster management personnel, making it extremely useful in today's world where the demand for disaster response technology is increasing. The vision of the triage AI agent system is to save many lives through the provision of rapid and accurate information in emergency response during disasters. This is expected to lead to more efficient disaster response and improved reliability. As a result, the triage AI agent system enables rapid and accurate rescue operations and resource management.

[0065] The triage AI agent system according to this embodiment comprises a collection unit, an analysis unit, a priority setting unit, and a resource management unit. The collection unit collects real-time data collected during a disaster. The collection unit can collect, for example, sensor data, video data, and audio data. The collection unit can also collect information on the situation in the disaster area using drones and sensors. For example, the collection unit can collect aerial footage of the disaster area using a drone. The collection unit can also collect environmental data such as temperature and humidity using sensors. Furthermore, the collection unit can collect audio data of disaster victims to understand their situation. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data using generative AI. For example, the analysis unit analyzes the data using deep learning technology. The analysis unit can also analyze audio data using natural language processing technology. Furthermore, the analysis unit can also analyze video data using image recognition technology. The priority setting unit sets priorities according to the urgency based on the data analyzed by the analysis unit. The priority setting unit includes an algorithm that sets priorities according to the urgency based on the analysis results. For example, the priority setting unit sets priorities based on the scale of the damage and the number of victims. The priority setting unit can also set priorities based on the situation in the affected area. Furthermore, the priority setting unit can also set priorities based on the health status of the victims. The resource management unit efficiently allocates personnel and supplies based on the priorities set by the priority setting unit. The resource management unit includes protocols for efficiently allocating personnel and supplies in cooperation with the resource management system. For example, the resource management unit optimizes the deployment of rescue teams and the delivery routes of relief supplies. Furthermore, the resource management unit can optimize the deployment of rescue teams and the delivery routes of relief supplies in cooperation with the resource management system. As a result, the triage AI agent system according to this embodiment can achieve rapid and accurate rescue operations and resource management.

[0066] The data collection unit collects real-time data during disasters. This unit can collect, for example, sensor data, video data, and audio data. Specifically, it uses drones and sensors to collect information about the situation in disaster-stricken areas. Drones fly over the disaster area, collecting wide-area aerial footage in real time. This allows for a rapid assessment of the overall situation in the affected area. Drones are also equipped with infrared and thermal imaging cameras, enabling the identification of victims' locations even at night or in poor visibility conditions. Sensors are installed on the ground to collect environmental data such as temperature, humidity, gas concentration, and vibration. This allows for early detection of dangers such as fires and hazardous gas leaks. Furthermore, the data collection unit uses speech recognition technology to collect audio data from victims. It collects real-time calls for help and descriptions of the situation from victims and transmits them to the analysis unit. This allows for a rapid assessment of the victims' locations and conditions. The data collection unit has a communication protocol for centrally managing and transmitting this data to the analysis unit. This ensures that collected data is transmitted quickly and accurately to the analysis unit, enabling real-time situation assessment.

[0067] The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data using generative AI. Specifically, it analyzes the data using deep learning technology. Deep learning technology can learn from large amounts of data and perform advanced pattern recognition. For example, it uses image recognition technology to analyze video data taken by drones to understand the situation in the disaster area in detail. It can identify the extent of building collapses, road closures, and fire outbreaks. It also uses natural language processing technology to analyze the voice data of disaster victims. It converts the voices of victims calling for help and describing their situation into text data to understand their location and condition. Furthermore, it analyzes sensor data and can detect dangerous situations early based on environmental data such as temperature, humidity, and gas concentration. The analysis unit integrates this data to understand the situation of the entire disaster area in real time. The analysis results are transmitted to the priority setting unit and used to set priorities according to the urgency.

[0068] The priority setting unit sets priorities according to urgency based on the data analyzed by the analysis unit. The priority setting unit is equipped with an algorithm that sets priorities according to urgency based on the analysis results. Specifically, priorities are set based on the scale of the damage and the number of victims. For example, situations that endanger lives, such as building collapses or fires, are given the highest priority. Priorities can also be set based on the situation in the affected area. For example, situations that hinder rescue operations, such as road closures or bridge collapses, are given priority. Furthermore, priorities can also be set based on the health status of the victims. For example, seriously injured people, the elderly, and children, who require special rescue, are given priority. The priority setting unit makes a comprehensive judgment based on this information and sets the optimal priorities. The set priorities are transmitted to the resource management unit and used for the efficient allocation of personnel and supplies.

[0069] The Resource Management Department efficiently allocates personnel and supplies based on priorities set by the Prioritization Department. The Resource Management Department has protocols for efficiently allocating personnel and supplies in conjunction with the Resource Management System. Specifically, it optimizes the deployment of rescue teams and the delivery routes of relief supplies. For example, it quickly dispatches rescue teams to high-priority areas and prioritizes the delivery of necessary relief supplies. Furthermore, the Resource Management Department continuously optimizes the deployment of rescue teams and the delivery routes of relief supplies based on real-time updated data. This enables rapid response to changing situations and efficient resource allocation. In addition, the Resource Management Department has protocols for optimizing the deployment of rescue teams and the delivery routes of relief supplies in conjunction with the Resource Management System. This allows the Resource Management Department to achieve rapid and accurate rescue operations and resource management.

[0070] The data collection unit can collect information about the situation in disaster-stricken areas using drones and sensors. For example, the unit can collect aerial footage of the disaster area using drones. The unit can optimize the drone's flight path to efficiently cover the entire disaster area. The unit can also collect environmental data such as temperature and humidity using sensors. The unit can adjust the placement of sensors to prioritize data collection in critical areas. Furthermore, the unit can collect audio data of disaster victims to understand their situation. The unit can enhance the coordination between drones and sensors to grasp detailed situations in real time. This allows for detailed collection of information about the situation in disaster-stricken areas.

[0071] The analysis unit can analyze data collected using generative AI. For example, the analysis unit can analyze data using deep learning technology. By using deep learning technology, the analysis unit can improve the accuracy of data analysis. Furthermore, the analysis unit can analyze audio data using natural language processing technology. By using natural language processing technology, the analysis unit can improve the accuracy of audio data analysis. In addition, the analysis unit can analyze video data using image recognition technology. By using image recognition technology, the analysis unit can improve the accuracy of video data analysis. Thus, using generative AI improves the accuracy of data analysis.

[0072] The priority setting unit includes an algorithm that sets priorities according to urgency based on the analysis results. For example, the priority setting unit sets priorities based on the scale of the damage and the number of affected people. The priority setting unit can set higher priorities the larger the scale of the damage. It can also set higher priorities the larger the number of affected people. Furthermore, the priority setting unit can also set priorities based on the situation in the affected area. The priority setting unit can set higher priorities the worse the situation in the affected area. This allows for the automatic setting of priorities according to urgency.

[0073] The Resource Management Department has protocols for efficiently allocating personnel and supplies in conjunction with the resource management system. For example, the Resource Management Department optimizes the deployment of rescue teams and the delivery routes for relief supplies. By optimizing the deployment of rescue teams, the Resource Management Department enables rapid and efficient assistance. Furthermore, by optimizing the delivery routes for relief supplies, the Resource Management Department enables rapid and efficient assistance. In addition, the Resource Management Department can optimize the deployment of rescue teams and the delivery routes for relief supplies in conjunction with the resource management system. This allows for the efficient allocation of personnel and supplies.

[0074] The Resource Management Department can optimize the deployment of rescue teams and the delivery routes of relief supplies. For example, the Resource Management Department can optimize the deployment of rescue teams. By optimizing the deployment of rescue teams, the Resource Management Department can provide rapid and efficient assistance. The Resource Management Department can also optimize the delivery routes of relief supplies. By optimizing the delivery routes of relief supplies, the Resource Management Department can provide rapid and efficient assistance. Furthermore, the Resource Management Department has protocols for optimizing the deployment of rescue teams and the delivery routes of relief supplies. This enables rapid and efficient assistance by optimizing the deployment of rescue teams and the delivery routes of relief supplies.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. The data collection unit can also increase the frequency of data collection to collect more detailed information if the user is relaxed. Furthermore, if the user is facing an emergency, the data collection unit can immediately begin data collection to enable a rapid response. This allows for a reduction in the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0076] The data collection unit can optimize the placement of drones and sensors to collect more detailed information about the disaster area. For example, the data collection unit can optimize drone flight routes to efficiently cover the entire disaster area. The data collection unit can also adjust sensor placement to prioritize data collection from critical areas. Furthermore, the data collection unit can enhance the coordination between drones and sensors to gain a real-time, detailed understanding of the situation. This allows for more detailed data collection of the disaster area by optimizing the placement of drones and sensors. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input drone and sensor placement data into a generating AI and have the generating AI perform the optimization of the placement.

[0077] The data collection unit can dynamically change the type of data it collects according to the situation in the disaster area. For example, if the situation in the disaster area changes, the data collection unit can automatically change the type of data it collects. By automatically changing the type of data it collects when the situation in the disaster area changes, the data collection unit can prioritize the collection of necessary data. The data collection unit can also prioritize the collection of necessary data according to the situation in the disaster area. The data collection unit can prioritize the collection of necessary data according to the situation in the disaster area. Furthermore, the data collection unit can adjust the type of data it collects in real time based on the situation in the disaster area. By adjusting the type of data it collects in real time based on the situation in the disaster area, the data collection unit can prioritize the collection of necessary data. This allows for the priority collection of necessary data by dynamically changing the type of data collected according to the situation in the disaster area. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input disaster area situation data into a generating AI and have the generating AI change the type of data to collect.

[0078] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important data. By prioritizing the collection of important data when the user is stressed, the data collection unit can reduce the user's burden. The data collection unit can also prioritize the collection of detailed data when the user is relaxed. By prioritizing the collection of detailed data when the user is relaxed, the data collection unit can collect detailed information. Furthermore, if the user is facing an emergency, the data collection unit can prioritize the collection of high-priority data. By prioritizing the collection of high-priority data when the user is facing an emergency, the data collection unit can enable a rapid response. In this way, by determining the priority of data to collect according to the user's emotions, important data can be collected preferentially. 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, allowing the generating AI to perform emotion estimation.

[0079] The data collection unit can adjust its data collection method while considering the geographical characteristics of the disaster area. For example, the data collection unit can adjust the drone's flight route according to the terrain of the disaster area. By adjusting the drone's flight route according to the terrain of the disaster area, the data collection unit can efficiently collect data. The data collection unit can also optimize the placement of sensors based on the geographical characteristics of the disaster area. By optimizing the placement of sensors based on the geographical characteristics of the disaster area, the data collection unit can efficiently collect data. Furthermore, the data collection unit can change the data collection method while considering the geographical characteristics of the disaster area. By changing the data collection method while considering the geographical characteristics of the disaster area, the data collection unit can efficiently collect data. Thus, by adjusting the data collection method while considering the geographical characteristics of the disaster area, efficient data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input geographical characteristic data of the disaster area into a generating AI and have the generating AI perform the adjustment of the data collection method.

[0080] The data collection unit can select data collection methods while considering the infrastructure conditions of the disaster-stricken area. For example, the data collection unit can decide whether to use drones or sensors depending on the infrastructure conditions of the disaster-stricken area. By deciding whether to use drones or sensors depending on the infrastructure conditions of the disaster-stricken area, the data collection unit can achieve optimal data collection. The data collection unit can also select data collection methods while considering the infrastructure conditions of the disaster-stricken area. By selecting data collection methods while considering the infrastructure conditions of the disaster-stricken area, the data collection unit can achieve optimal data collection. Furthermore, the data collection unit can select the optimal data collection method based on the infrastructure conditions of the disaster-stricken area. By selecting the optimal data collection method based on the infrastructure conditions of the disaster-stricken area, the data collection unit can achieve optimal data collection. Thus, by selecting data collection methods while considering the infrastructure conditions of the disaster-stricken area, optimal data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input disaster-stricken area infrastructure data into a generating AI and have the generating AI select the data collection method.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. By providing a simple and highly visible display method when the user is nervous, the analysis unit can make the display easy for the user to read. The analysis unit can also provide a display method that includes detailed information when the user is relaxed. By providing a display method that includes detailed information when the user is relaxed, the analysis unit can make the display easy for the user to read. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By providing a display method that gets straight to the point when the user is in a hurry, the analysis unit can make the display easy for the user to read. In this way, by adjusting the display method of the analysis results according to the user's emotions, a display that is easy for the user to read can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0082] The analysis unit can optimize its analysis algorithm by referring to past disaster data during analysis. For example, the analysis unit adjusts the analysis algorithm based on past disaster data. By adjusting the analysis algorithm based on past disaster data, the analysis unit can improve the accuracy of the analysis. The analysis unit can also improve the accuracy of the analysis by referring to past disaster data. By improving the accuracy of the analysis by referring to past disaster data, the analysis unit can provide more accurate analysis results. Furthermore, the analysis unit can optimize the analysis algorithm by utilizing past disaster data. By optimizing the analysis algorithm by utilizing past disaster data, the analysis unit can improve the accuracy of the analysis. As a result, the accuracy of the analysis is improved by optimizing the analysis algorithm by referring to past disaster data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past disaster data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0083] The analysis unit can evaluate the reliability of the collected data during analysis and prioritize the analysis of highly reliable data. For example, the analysis unit evaluates the reliability of the collected data and prioritizes the analysis of highly reliable data. By evaluating the reliability of the collected data and prioritizing the analysis of highly reliable data, the analysis unit can provide highly accurate results. Furthermore, the analysis unit can also determine the priority of analysis based on the reliability of the data. By determining the priority of analysis based on the reliability of the data, the analysis unit can provide highly accurate results. In addition, the analysis unit can prioritize the analysis of highly reliable data and provide highly accurate results. By prioritizing the analysis of highly reliable data and providing highly accurate results, the analysis unit can provide highly accurate results. This allows for the evaluation of the reliability of the collected data and the prioritization of the analysis of highly reliable data, thereby providing highly accurate results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the reliability of the collected data into a generating AI and have the generating AI perform the reliability evaluation.

[0084] The analysis unit can estimate the user's emotions and adjust the importance of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can highlight important information. By highlighting important information when the user is nervous, the analysis unit can make important information easier for the user to see. The analysis unit can also provide analysis results that include detailed information when the user is relaxed. By providing analysis results that include detailed information when the user is relaxed, the analysis unit can make important information easier for the user to see. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that summarize the key points. By providing analysis results that summarize the key points when the user is in a hurry, the analysis unit can make important information easier for the user to see. This allows for the highlighting of important information by adjusting the importance of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0085] The analysis unit can select an analysis method while considering the socioeconomic conditions of the disaster-stricken area. For example, the analysis unit selects an analysis method based on the socioeconomic conditions of the disaster-stricken area. By selecting an analysis method based on the socioeconomic conditions of the disaster-stricken area, the analysis unit can perform the optimal analysis. The analysis unit can also select the optimal analysis method while considering the socioeconomic conditions of the disaster-stricken area. By selecting the optimal analysis method while considering the socioeconomic conditions of the disaster-stricken area, the analysis unit can perform the optimal analysis. Furthermore, the analysis unit can adjust the analysis method according to the socioeconomic conditions of the disaster-stricken area. By adjusting the analysis method according to the socioeconomic conditions of the disaster-stricken area, the analysis unit can perform the optimal analysis. Thus, by selecting an analysis method while considering the socioeconomic conditions of the disaster-stricken area, the optimal analysis can be achieved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input socioeconomic data of the disaster-stricken area into a generating AI and have the generating AI perform the selection of the analysis method.

[0086] The analysis unit can improve the accuracy of its analysis by referring to weather data from the disaster-stricken area during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to weather data from the disaster-stricken area. By improving the accuracy of the analysis by referring to weather data from the disaster-stricken area, the analysis unit can provide more accurate analysis results. Furthermore, the analysis unit can adjust its analysis method based on the weather data. By adjusting its analysis method based on the weather data, the analysis unit can provide more accurate analysis results. In addition, the analysis unit can improve the accuracy of its analysis by utilizing weather data from the disaster-stricken area. By improving the accuracy of the analysis by utilizing weather data from the disaster-stricken area, the analysis unit can provide more accurate analysis results. Thus, by improving the accuracy of the analysis by referring to weather data from the disaster-stricken area, more accurate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input weather data from the disaster-stricken area into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0087] The prioritization unit can estimate the user's emotions and adjust the prioritization criteria based on the estimated emotions. For example, if the user is stressed, the prioritization unit can prioritize important tasks. By prioritizing important tasks when the user is stressed, the prioritization unit can ensure that tasks important to the user are processed preferentially. The prioritization unit can also prioritize detailed tasks when the user is relaxed. By prioritizing detailed tasks when the user is relaxed, the prioritization unit can ensure that tasks important to the user are processed preferentially. Furthermore, if the user is in a hurry, the prioritization unit can prioritize tasks that require immediate attention. By prioritizing tasks that require immediate attention when the user is in a hurry, the prioritization unit can ensure that tasks important to the user are processed preferentially. In this way, by adjusting the prioritization criteria according to the user's emotions, more appropriate priorities can be set. 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 processing described above in the priority setting unit may be performed using AI, for example, or without using AI. For example, the priority setting unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0088] The priority setting unit can set priorities while considering the health status of the victims. For example, the priority setting unit sets priorities based on the health status of the victims. By setting priorities based on the health status of the victims, the priority setting unit can prioritize the rescue of seriously injured victims. The priority setting unit can also prioritize the rescue of victims whose health status is deteriorating. By prioritizing the rescue of victims whose health status is deteriorating, the priority setting unit can prioritize the rescue of seriously injured victims. Furthermore, the priority setting unit can also adjust priorities while considering the health status of the victims. By adjusting priorities while considering the health status of the victims, the priority setting unit can prioritize the rescue of seriously injured victims. As a result, by setting priorities while considering the health status of the victims, it is possible to prioritize the rescue of seriously injured victims. Some or all of the above-described processes in the priority setting unit may be performed using AI, for example, or without using AI. For example, the priority setting unit can input victim health status data into a generating AI and have the generating AI perform the priority setting.

[0089] The priority setting unit can set priorities while considering the infrastructure situation in the disaster-stricken area. For example, the priority setting unit sets priorities based on the infrastructure situation in the disaster-stricken area. By setting priorities based on the infrastructure situation in the disaster-stricken area, the priority setting unit can prioritize support to areas with damaged infrastructure. The priority setting unit can also prioritize support to areas with damaged infrastructure. By prioritizing support to areas with damaged infrastructure, the priority setting unit can prioritize support to areas with damaged infrastructure. Furthermore, the priority setting unit can adjust priorities while considering the infrastructure situation in the disaster-stricken area. By adjusting priorities while considering the infrastructure situation in the disaster-stricken area, the priority setting unit can prioritize support to areas with damaged infrastructure. As a result, by setting priorities while considering the infrastructure situation in the disaster-stricken area, areas with damaged infrastructure can be prioritized for support. Some or all of the above processing in the priority setting unit may be performed using AI, for example, or without using AI. For example, the priority setting unit can input infrastructure situation data of the disaster-stricken area into a generating AI and have the generating AI perform the priority setting.

[0090] The priority setting unit can estimate the user's emotions and adjust the display method of priorities based on the estimated emotions. For example, if the user is nervous, the priority setting unit can provide a simple and highly visible display method. By providing a simple and highly visible display method when the user is nervous, the priority setting unit can make the display easy for the user to see. The priority setting unit can also provide a display method that includes detailed information when the user is relaxed. By providing a display method that includes detailed information when the user is relaxed, the priority setting unit can make the display easy for the user to see. Furthermore, if the user is in a hurry, the priority setting unit can provide a display method that gets to the point. By providing a display method that gets to the point when the user is in a hurry, the priority setting unit can make the display easy for the user to see. In this way, by adjusting the display method of priorities according to the user's emotions, a display that is easy for the user to see can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the processing described above in the priority setting unit may be performed using AI, for example, or without using AI. For example, the priority setting unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0091] The priority setting unit can set priorities while considering the geographical characteristics of the disaster-stricken area. For example, the priority setting unit sets priorities based on the geographical characteristics of the disaster-stricken area. By setting priorities based on the geographical characteristics of the disaster-stricken area, the priority setting unit can prioritize support for geographically isolated areas. The priority setting unit can also prioritize support for geographically isolated areas. By prioritizing support for geographically isolated areas, the priority setting unit can prioritize support for geographically isolated areas. Furthermore, the priority setting unit can adjust priorities while considering the geographical characteristics of the disaster-stricken area. By adjusting priorities while considering the geographical characteristics of the disaster-stricken area, the priority setting unit can prioritize support for geographically isolated areas. As a result, by setting priorities while considering the geographical characteristics of the disaster-stricken area, geographically isolated areas can be prioritized. Some or all of the above processing in the priority setting unit may be performed using AI, for example, or without using AI. For example, the priority setting unit can input geographical characteristic data of the disaster-stricken area into a generating AI and have the generating AI perform the priority setting.

[0092] The priority setting unit can set priorities while considering the population density of the disaster-stricken area. For example, the priority setting unit sets priorities based on the population density of the disaster-stricken area. By setting priorities based on the population density of the disaster-stricken area, the priority setting unit can prioritize support for areas with high population density. The priority setting unit can also prioritize support for areas with high population density. By prioritizing support for areas with high population density, the priority setting unit can prioritize support for areas with high population density. Furthermore, the priority setting unit can adjust priorities while considering the population density of the disaster-stricken area. By adjusting priorities while considering the population density of the disaster-stricken area, the priority setting unit can prioritize support for areas with high population density. As a result, by setting priorities while considering the population density of the disaster-stricken area, areas with high population density can be prioritized for support. Some or all of the above processing in the priority setting unit may be performed using AI, for example, or without using AI. For example, the priority setting unit can input population density data of the disaster-stricken area into a generating AI and have the generating AI perform the priority setting.

[0093] The resource management unit can estimate the user's emotions and adjust the resource allocation method based on the estimated emotions. For example, if the user is stressed, the resource management unit can prioritize the allocation of important resources. The resource management unit can also prioritize the allocation of important resources to the user by prioritizing the allocation of important resources when the user is stressed. Furthermore, if the user is in a hurry, the resource management unit can prioritize the allocation of resources that require a quick response. The resource management unit can prioritize the allocation of important resources to the user by prioritizing the allocation of resources that require a quick response when the user is in a hurry. In this way, by adjusting the resource allocation method according to the user's emotions, important resources can be prioritized. 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 processes in the resource management unit may be performed using AI, for example, or without AI. For example, the resource management unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0094] The resource management unit can optimize the allocation route when allocating resources, taking into account the traffic conditions in the disaster-stricken area. For example, the resource management unit optimizes the allocation route based on the traffic conditions in the disaster-stricken area. By optimizing the allocation route based on the traffic conditions in the disaster-stricken area, the resource management unit enables efficient resource allocation. The resource management unit can also prioritize selecting routes that avoid traffic congestion. By prioritizing the selection of routes that avoid traffic congestion, the resource management unit enables efficient resource allocation. Furthermore, the resource management unit can determine the optimal allocation route by taking into account the traffic conditions in the disaster-stricken area. By determining the optimal allocation route by taking into account the traffic conditions in the disaster-stricken area, the resource management unit enables efficient resource allocation. Thus, by optimizing the allocation route by taking into account the traffic conditions in the disaster-stricken area, efficient resource allocation becomes possible. Some or all of the above processing in the resource management unit may be performed using AI, for example, or without using AI. For example, the resource management unit can input traffic condition data from the disaster-stricken area into a generating AI and have the generating AI perform the optimization of the allocation route.

[0095] The resource management unit can select a resource allocation method considering the infrastructure situation in the disaster-stricken area when allocating resources. For example, the resource management unit can select the optimal allocation method based on the infrastructure situation in the disaster-stricken area. By selecting the optimal allocation method based on the infrastructure situation in the disaster-stricken area, the resource management unit can achieve optimal resource allocation. The resource management unit can also use drones or helicopters in areas where infrastructure is damaged. By using drones or helicopters in areas where infrastructure is damaged, the resource management unit can achieve optimal resource allocation. Furthermore, the resource management unit can select an efficient allocation method considering the infrastructure situation in the disaster-stricken area. By selecting an efficient allocation method considering the infrastructure situation in the disaster-stricken area, the resource management unit can achieve optimal resource allocation. Thus, by selecting an allocation method considering the infrastructure situation in the disaster-stricken area, optimal resource allocation becomes possible. Some or all of the above processing in the resource management unit may be performed using AI, for example, or without AI. For example, the resource management unit can input infrastructure situation data of the disaster-stricken area into a generating AI and have the generating AI select the allocation method.

[0096] The resource management unit can estimate the user's emotions and determine resource allocation priorities based on the estimated emotions. For example, if the user is stressed, the resource management unit will prioritize the allocation of important resources. The resource management unit can prioritize the allocation of resources that are important to the user by prioritizing the allocation of important resources when the user is stressed. The resource management unit can also perform detailed resource allocation when the user is relaxed. The resource management unit can prioritize the allocation of resources that are important to the user by performing detailed resource allocation when the user is relaxed. Furthermore, if the resource management unit is in a hurry, it can prioritize the allocation of resources that require a quick response. The resource management unit can prioritize the allocation of resources that require a quick response when the user is in a hurry, thereby prioritizing the allocation of resources that are important to the user. In this way, by determining resource allocation priorities according to the user's emotions, important resources can be prioritized. 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 processes in the resource management unit may be performed using AI, for example, or without AI. For example, the resource management unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0097] The resource management unit can adjust the resource allocation method when allocating resources, taking into account the geographical characteristics of the disaster-stricken area. For example, the resource management unit adjusts the resource allocation method based on the geographical characteristics of the disaster-stricken area. By adjusting the resource allocation method based on the geographical characteristics of the disaster-stricken area, the resource management unit can enable efficient resource allocation. The resource management unit can also use drones or helicopters in geographically isolated areas. By using drones or helicopters in geographically isolated areas, the resource management unit can enable efficient resource allocation. Furthermore, the resource management unit can select an efficient resource allocation method, taking into account the geographical characteristics of the disaster-stricken area. By selecting an efficient resource allocation method, taking into account the geographical characteristics of the disaster-stricken area, the resource management unit can enable efficient resource allocation. Thus, by adjusting the allocation method, taking into account the geographical characteristics of the disaster-stricken area, efficient resource allocation becomes possible. Some or all of the above processing in the resource management unit may be performed using AI, for example, or without AI. For example, the resource management unit can input geographical characteristic data of the disaster-stricken area into a generating AI and have the generating AI perform the adjustment of the allocation method.

[0098] The resource management unit can select a resource allocation method considering the population density of the disaster-stricken area when allocating resources. For example, the resource management unit can select the optimal allocation method based on the population density of the disaster-stricken area. By selecting the optimal allocation method based on the population density of the disaster-stricken area, the resource management unit can achieve optimal resource allocation. The resource management unit can also select a method for rapidly distributing large quantities of supplies to areas with high population density. By selecting a method for rapidly distributing large quantities of supplies to areas with high population density, the resource management unit can achieve optimal resource allocation. Furthermore, the resource management unit can also select an efficient allocation method considering the population density of the disaster-stricken area. By selecting an efficient allocation method considering the population density of the disaster-stricken area, the resource management unit can achieve optimal resource allocation. Thus, by selecting an allocation method considering the population density of the disaster-stricken area, optimal resource allocation becomes possible. Some or all of the above processing in the resource management unit may be performed using AI, for example, or without AI. For example, the resource management unit can input population density data of the disaster-stricken area into a generating AI and have the generating AI select the allocation method.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The triage AI agent system can adjust support plans based on the psychological state of disaster victims. For example, if a victim is experiencing strong anxiety or fear, psychological support can be prioritized. Conversely, if a victim is calm, physical assistance can be prioritized. Furthermore, the timing and method of support can be adjusted based on the victim's psychological state. This reduces the psychological burden on victims and allows for more effective support.

[0101] The triage AI agent system can optimize support plans by considering the geographical characteristics of disaster-stricken areas. For example, it can use drones and helicopters to deliver supplies to areas that are difficult to access, such as mountainous regions and remote islands. In urban areas, it can also calculate the optimal delivery route to avoid traffic congestion. Furthermore, it can set support priorities based on the terrain and infrastructure conditions of the disaster-stricken area. This enables efficient support tailored to the geographical conditions of the disaster-stricken area.

[0102] The triage AI agent system can monitor the health status of disaster victims in real time and provide necessary support quickly. For example, it can measure the body temperature and heart rate of disaster victims using sensors and immediately dispatch medical assistance if an abnormality is detected. It can also prioritize support based on the health status of the disaster victims. Furthermore, it can analyze health data to assess the health risks of the entire disaster area. This enables a swift and appropriate response to protect the health of disaster victims.

[0103] The triage AI agent system can adjust support plans to take into account the socioeconomic conditions of disaster-stricken areas. For example, it can provide special support to low-income areas or areas with a large elderly population. It can also prioritize infrastructure restoration to quickly restart economic activity in the affected areas. Furthermore, it can analyze socioeconomic data from the affected areas and propose the most effective support methods. This ensures that support is tailored to the socioeconomic needs of the disaster-stricken areas.

[0104] The triage AI agent system can collect weather data from disaster-stricken areas in real time and incorporate it into relief plans. For example, if a typhoon or heavy rain is expected, evacuation plans can be developed in advance. It can also adjust the delivery routes of relief supplies based on weather data. Furthermore, it can analyze weather data to assess the risks in disaster-stricken areas. This enables rapid and appropriate assistance tailored to weather conditions.

[0105] The triage AI agent system can estimate the emotions of disaster victims and prioritize support based on those emotions. For example, if a victim is experiencing strong anxiety or fear, psychological support can be prioritized. Conversely, if a victim is calm, physical support can be prioritized. Furthermore, the system can adjust the timing and method of support based on the victim's emotions. This reduces the psychological burden on victims and allows for more effective support.

[0106] The triage AI agent system can monitor the infrastructure status in disaster-stricken areas in real time and incorporate this information into support plans. For example, if roads or bridges are damaged, it can calculate alternative routes and deliver relief supplies accordingly. It can also provide necessary support until infrastructure such as electricity and water is restored. Furthermore, it can analyze infrastructure data and propose the most effective support methods. This enables efficient support tailored to the infrastructure status of the disaster-stricken area.

[0107] The triage AI agent system can estimate the emotions of disaster victims and determine the type of relief supplies to provide based on those estimates. For example, if a victim is feeling stressed, it can prioritize providing items that have a relaxing effect. Conversely, if a victim is feeling at ease, it can prioritize providing basic necessities. Furthermore, it can adjust the distribution method of relief supplies based on the emotions of the victims. This reduces the psychological burden on victims and allows for more effective assistance.

[0108] The triage AI agent system can monitor traffic conditions in disaster-stricken areas in real time and incorporate this information into support plans. For example, if traffic congestion occurs, it can calculate the optimal delivery route to deliver relief supplies. It can also prioritize support based on traffic conditions. Furthermore, it can analyze traffic data to assess the overall traffic risk in the disaster area. This enables rapid and appropriate support tailored to traffic conditions.

[0109] The triage AI agent system can estimate the emotions of disaster victims and adjust the method of support based on those estimates. For example, if a victim is experiencing strong anxiety or fear, psychological support can be prioritized. Conversely, if a victim is calm, physical support can be prioritized. Furthermore, the timing and method of support can be adjusted based on the victim's emotions. This reduces the psychological burden on victims and allows for more effective support.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The collection unit collects real-time data gathered during a disaster. The collection unit can collect sensor data, video data, audio data, etc. For example, it can collect aerial footage of the disaster area using a drone, collect environmental data such as temperature and humidity using sensors, and collect audio data of disaster victims to understand their situation. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data using generative AI. For example, it can analyze data using deep learning technology, analyze audio data using natural language processing technology, or analyze video data using image recognition technology. Step 3: The priority setting unit sets priorities according to urgency based on the data analyzed by the analysis unit. The priority setting unit includes an algorithm that sets priorities based on the scale of the damage, the number of victims, the situation in the affected area, and the health status of the victims. Step 4: The resource management unit efficiently allocates personnel and supplies based on the priorities set by the priority setting unit. The resource management unit has protocols in place to optimize the deployment of rescue teams and the delivery routes of relief supplies in cooperation with the resource management system.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the data collection unit, analysis unit, priority setting unit, and resource management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects information on the situation in the disaster area using the camera 42 and sensors of the smart device 14, and the control unit 46A collects the data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the data using deep learning technology and natural language processing technology. The priority setting unit is implemented in the specific processing unit 290 of the data processing unit 12, and sets priorities according to the urgency based on the analysis results. The resource management unit is implemented in the specific processing unit 290 of the data processing unit 12, and efficiently allocates personnel and supplies in cooperation with the resource management system. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the data collection unit, analysis unit, priority setting unit, and resource management unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects information on the situation in the disaster area using the camera 42 and sensors of the smart glasses 214, and the control unit 46A collects the data. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the data using deep learning technology and natural language processing technology. The priority setting unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and sets priorities according to the urgency based on the analysis results. The resource management unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and efficiently allocates personnel and supplies in cooperation with the resource management system. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the data collection unit, analysis unit, priority setting unit, and resource management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects information on the situation in the disaster area using the camera 42 and sensors of the headset terminal 314, and the control unit 46A collects the data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the data using deep learning technology and natural language processing technology. The priority setting unit is implemented in the specific processing unit 290 of the data processing unit 12, and sets priorities according to the urgency based on the analysis results. The resource management unit is implemented in the specific processing unit 290 of the data processing unit 12, and efficiently allocates personnel and supplies in cooperation with the resource management system. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, priority setting unit, and resource management unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects information on the situation in the disaster area using the camera 42 and sensors of the robot 414, and the control unit 46A collects the data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the data using deep learning technology and natural language processing technology. The priority setting unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and sets priorities according to the urgency based on the analysis results. The resource management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and efficiently allocates personnel and supplies in cooperation with the resource management system. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A data collection unit that collects real-time data during disasters, An analysis unit analyzes the data collected by the aforementioned collection unit, A priority setting unit sets priorities according to urgency based on the data analyzed by the aforementioned analysis unit, The system includes a resource management unit that efficiently allocates personnel and supplies based on the priorities set by the priority setting unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Use drones and sensors to collect information about the situation in the disaster area. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze the collected data using generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned priority setting unit, It features an algorithm that sets priorities according to urgency based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned resource management unit, It features protocols that work in conjunction with resource management systems to efficiently allocate personnel and supplies. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned resource management unit, Optimize the deployment of rescue teams and the delivery routes of relief supplies. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Optimize the placement of drones and sensors to collect more detailed information about the situation in disaster-stricken areas. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The type of data collected is dynamically changed according to the situation in the disaster-stricken area. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the collection method is adjusted to take into account the geographical characteristics of the disaster-stricken area. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, the collection method should be selected considering the infrastructure situation in the disaster-stricken area. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past disaster data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the reliability of the collected data is evaluated, and the most reliable data is prioritized for analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the importance of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the analytical method will be selected taking into account the socioeconomic conditions of the disaster-stricken area. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to weather data from the affected area to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned priority setting unit, It estimates the user's emotions and adjusts the priority setting criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned priority setting unit, When setting priorities, the health status of the disaster victims should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned priority setting unit, When setting priorities, consider the infrastructure situation in the affected areas. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned priority setting unit, It estimates the user's emotions and adjusts the display priority based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned priority setting unit, When setting priorities, consider the geographical characteristics of the affected areas. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned priority setting unit, When setting priorities, consider the population density of the affected areas. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned resource management unit, It estimates user sentiment and adjusts resource allocation based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned resource management unit, When allocating resources, optimize the allocation route while considering the traffic conditions in the disaster-stricken area. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned resource management unit, When allocating resources, the allocation method should be selected considering the infrastructure situation in the disaster-stricken areas. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned resource management unit, It estimates user sentiment and determines resource allocation priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned resource management unit, When allocating resources, adjust the allocation method considering the geographical characteristics of the affected areas. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned resource management unit, When allocating resources, the allocation method should be selected considering the population density of the disaster-stricken area. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects real-time data collected during disasters, An analysis unit analyzes the data collected by the aforementioned collection unit, A priority setting unit sets priorities according to urgency based on the data analyzed by the aforementioned analysis unit, The system includes a resource management unit that efficiently allocates personnel and supplies based on the priorities set by the priority setting unit. A system characterized by the following features.

2. The aforementioned collection unit is Use drones and sensors to collect information about the situation in the disaster area. The system according to feature 1.

3. The aforementioned analysis unit, We analyze the data collected using generative AI. The system according to feature 1.

4. The aforementioned priority setting unit, It features an algorithm that sets priorities according to urgency based on the analysis results. The system according to feature 1.

5. The aforementioned resource management unit, It features protocols that work in conjunction with resource management systems to efficiently allocate personnel and supplies. The system according to feature 1.

6. The aforementioned resource management unit, Optimize the deployment of rescue teams and the delivery routes of relief supplies. The system according to feature 1.

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

8. The aforementioned collection unit is Optimize the placement of drones and sensors to collect more detailed information about the situation in disaster-stricken areas. The system according to feature 1.

9. The aforementioned collection unit is The type of data collected is dynamically changed according to the situation in the disaster-stricken area. The system according to feature 1.

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

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