system

Abstract

The system according to this embodiment aims to quickly and accurately collect and analyze information in the event of a disaster and propose optimal evacuation routes and rescue plans. [Solution] The system according to the embodiment comprises an information collection unit, a data analysis unit, an evacuation route calculation unit, a rescue plan proposal unit, and an information sharing unit. The information collection unit collects information from satellite images and the ground. The data analysis unit analyzes the data collected by the information collection unit in real time. The evacuation route calculation unit calculates a safe evacuation route based on the data analyzed by the data analysis unit. The rescue plan proposal unit proposes an optimal rescue plan based on the evacuation route calculated by the evacuation route calculation unit. The information sharing unit supports information sharing between rescue teams and disaster victims.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to collect and analyze information quickly and accurately when a disaster occurs, and there is a problem that it takes time to formulate an optimal evacuation route or rescue plan.

[0005] The system according to the embodiment aims to quickly and accurately collect and analyze information when a disaster occurs, and propose an optimal evacuation route or rescue plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an information gathering unit, a data analysis unit, an evacuation route calculation unit, a rescue plan proposal unit, and an information sharing unit. The information gathering unit collects information from satellite images and ground sources. The data analysis unit analyzes the data collected by the information gathering unit in real time. The evacuation route calculation unit calculates safe evacuation routes based on the data analyzed by the data analysis unit. The rescue plan proposal unit proposes an optimal rescue plan based on the evacuation routes calculated by the evacuation route calculation unit. The information sharing unit supports information sharing between rescue teams and disaster victims. [Effects of the Invention]

[0007] The system according to this embodiment can quickly and accurately collect and analyze information in the event of a disaster and propose optimal evacuation routes and rescue plans. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards 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 disaster relief support system according to an embodiment of the present invention is a system that rapidly collects and analyzes information when a disaster occurs and proposes the optimal evacuation route and rescue plan. This disaster relief support system uses AI to rapidly collect and analyze information when a disaster occurs and proposes the optimal evacuation route and rescue plan. For example, the disaster relief support system's information collection unit collects information from satellite images and the ground. Next, the data analysis unit analyzes the collected data in real time, and the evacuation route calculation unit calculates a safe evacuation route. Furthermore, the rescue plan proposal unit proposes the optimal rescue plan, and the information sharing unit supports information sharing between rescue teams and disaster victims. This system aims to accelerate and streamline disaster response by integrating AI and drone technology, improving pattern recognition through machine learning, and utilizing a cloud-based information sharing system. This results in shorter rescue times, improved rescue success rates, and enhanced safety for disaster victims. As a result, the disaster relief support system can rapidly collect and analyze information when a disaster occurs and propose the optimal evacuation route and rescue plan.

[0029] The disaster relief support system according to this embodiment comprises an information gathering unit, a data analysis unit, an evacuation route calculation unit, a rescue plan proposal unit, and an information sharing unit. The information gathering unit collects satellite images and information from the ground. For example, the information gathering unit can acquire satellite images at high resolution and collect sensor data and visual information as information from the ground. The data analysis unit analyzes the data collected by the information gathering unit in real time. For example, the data analysis unit can process the collected data immediately to grasp the progress of the disaster and the extent of the damage. The evacuation route calculation unit calculates a safe evacuation route based on the data analyzed by the data analysis unit. For example, the evacuation route calculation unit can calculate the optimal route by considering conditions such as the width of the evacuation route, the presence or absence of obstacles, and the evacuation time. The rescue plan proposal unit proposes an optimal rescue plan based on the evacuation route calculated by the evacuation route calculation unit. For example, the rescue plan proposal unit can formulate a rescue plan considering the speed of rescue and the efficient allocation of resources. The information sharing unit supports information sharing between rescue teams and disaster victims. The information sharing unit can achieve rapid and accurate information sharing by considering, for example, the communication methods used and the types of information to be shared. As a result, the disaster relief support system according to this embodiment can rapidly collect and analyze information when a disaster occurs and propose optimal evacuation routes and rescue plans.

[0030] The Information Gathering Unit collects satellite imagery and ground-based information. Specifically, to acquire high-resolution satellite imagery, the Information Gathering Unit utilizes multiple satellites and collects images taken at different angles and times. This allows for a detailed understanding of the progression of a disaster and the extent of the damage. Ground-based information includes sensor data and visual observation data. Sensor data includes seismometers, water level gauges, temperature sensors, and humidity sensors, and these sensors are appropriately positioned according to the type and circumstances of the disaster. For example, data from seismometers is collected during earthquakes, and data from water level gauges is collected during floods. Visual observation data is collected using drones and ground-based cameras, allowing for a real-time understanding of the detailed situation in the affected area. The Information Gathering Unit centrally manages this data and stores it in a database. Furthermore, the Information Gathering Unit evaluates the quality of the collected data and supplements or corrects it as needed. This enables the Information Gathering Unit to provide accurate and reliable data and improve the overall performance of the system.

[0031] The Data Analysis Department analyzes data collected by the Information Gathering Department in real time. Specifically, the Data Analysis Department immediately processes collected satellite imagery and sensor data to understand the progression of disasters and the extent of damage. For example, it uses image analysis technology to identify the extent of building damage and flooding in disaster-stricken areas from satellite imagery, and analyzes sensor data to determine the earthquake epicenter and the rate of flood water level rise. The Data Analysis Department integrates this data to grasp the overall picture of the disaster in real time. Furthermore, the Data Analysis Department can utilize past disaster data and statistical information to predict disaster progression patterns and damage. For example, it can evaluate the flood risk in specific areas based on past flood data and formulate future countermeasures. In addition, the Data Analysis Department can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the Data Analysis Department can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The evacuation route calculation unit calculates safe evacuation routes based on data analyzed by the data analysis unit. Specifically, the evacuation route calculation unit calculates the optimal route by considering conditions such as the width of the evacuation route, the presence or absence of obstacles, and the evacuation time. For example, it identifies the safest and fastest evacuation route by considering factors such as road width and gradient, traffic conditions, and the location of evacuation shelters. The evacuation route calculation unit uses AI to analyze these conditions and calculates the optimal evacuation route by simulating multiple scenarios. Furthermore, the evacuation route calculation unit can continuously modify evacuation routes based on data updated in real time, enabling it to respond to the latest situations. For example, if a road is newly closed or a new obstacle appears, the evacuation route calculation unit immediately incorporates the new data and updates the evacuation route. In addition, the evacuation route calculation unit can calculate more accurate evacuation routes by considering the characteristics of each region and past evacuation history. As a result, the evacuation route calculation unit can always provide highly accurate evacuation routes based on the latest information, supporting quick and appropriate evacuations.

[0033] The Rescue Plan Proposal Department proposes the optimal rescue plan based on the evacuation routes calculated by the Evacuation Route Calculation Department. Specifically, the Rescue Plan Proposal Department formulates rescue plans considering the speed of rescue and the efficient allocation of resources. For example, it optimizes the deployment and movement routes of rescue teams and the allocation of rescue materials to realize rapid and efficient rescue operations. The Rescue Plan Proposal Department uses AI to analyze these conditions and proposes the optimal rescue plan by simulating multiple scenarios. Furthermore, the Rescue Plan Proposal Department can continuously revise rescue plans based on data updated in real time to respond to the latest situations. For example, if new damage occurs or the status of rescue teams changes, the Rescue Plan Proposal Department immediately incorporates the new data and updates the rescue plan. In addition, the Rescue Plan Proposal Department can formulate more accurate rescue plans by considering the characteristics of each region and past rescue history. As a result, the Rescue Plan Proposal Department can always provide highly accurate rescue plans based on the latest information and support rapid and appropriate rescue operations.

[0034] The Information Sharing Department supports information sharing between rescue teams and disaster victims. Specifically, the Information Sharing Department ensures rapid and accurate information sharing by considering the communication methods used and the types of information to be shared. For example, it shares information with rescue teams in real time using a dedicated communication network, and provides evacuation information and rescue status to disaster victims via smartphones, radio, television, etc. The Information Sharing Department improves the speed and accuracy of information transmission by integrating these communication methods and centrally managing information. Furthermore, the Information Sharing Department enables two-way information sharing and can collect feedback from disaster victims. For example, disaster victims can report evacuation status and rescue requests using their smartphones, allowing rescue teams to grasp the situation of disaster victims in real time and respond quickly. In addition, the Information Sharing Department conducts information sharing training and system testing not only during disasters but also during normal times to maintain a consistently high level of information sharing. As a result, the Information Sharing Department can achieve rapid and accurate information sharing and maximize the efficiency and effectiveness of rescue operations.

[0035] The drone integration unit integrates drones to collect information. For example, the drone integration unit can collect information over a wide area by coordinating multiple drones. The drone integration unit considers the type of drone and the integration protocol to achieve efficient information collection. For example, the drone integration unit can combine fixed-wing drones and multi-rotor drones to simultaneously collect information over a wide area and detailed information. The drone integration unit can also optimize the flight routes of drones to collect information efficiently. For example, the drone integration unit sets the optimal flight route considering the terrain and weather information of the disaster area. This improves the range and accuracy of information collection by using drones. Some or all of the above processing in the drone integration unit may be performed using AI, for example, or without AI. For example, the drone integration unit can input the flight routes of drones into a generation AI and have the generation AI generate the optimal flight route.

[0036] The pattern recognition unit performs pattern recognition using machine learning. For example, the pattern recognition unit uses a machine learning algorithm to recognize patterns in disasters. The pattern recognition unit achieves highly accurate pattern recognition by considering the type of algorithm used and the content of the training data. For example, the pattern recognition unit can recognize patterns in disasters using deep learning. Furthermore, the pattern recognition unit can dynamically change the machine learning algorithm to grasp the progress and damage of a disaster in real time. For example, the pattern recognition unit performs rapid pattern recognition in the initial stages of a disaster and performs detailed pattern recognition as the disaster progresses. This improves the accuracy of disaster pattern recognition by using machine learning. Some or all of the above processing in the pattern recognition unit may be performed using AI, for example, or without using AI. For example, the pattern recognition unit can input disaster data into a generating AI and have the generating AI perform the pattern recognition.

[0037] The Cloud Sharing Unit performs cloud-based information sharing. For example, the Cloud Sharing Unit can use a cloud provider to store and share disaster information on the cloud. The Cloud Sharing Unit considers data storage methods and the cloud services used to achieve efficient information sharing. For example, the Cloud Sharing Unit uploads disaster information to the cloud in real time, allowing relevant parties to access it quickly. Furthermore, the Cloud Sharing Unit can synchronize and back up data on the cloud to ensure information reliability. For example, the Cloud Sharing Unit uses multiple cloud providers to ensure data redundancy. This enables rapid information sharing and access through cloud-based information sharing. Some or all of the above-described processes in the Cloud Sharing Unit may be performed using AI, or not. For example, the Cloud Sharing Unit can input the upload of disaster information into a generating AI and have the generating AI execute the upload to the cloud.

[0038] The information gathering unit dynamically changes the priority of information gathering according to the type and scale of the disaster. For example, in the event of an earthquake, the information gathering unit prioritizes collecting information from the area around the epicenter. In the event of a flood, the information gathering unit can prioritize collecting river water level information and the condition of embankments. Furthermore, when a typhoon is approaching, the information gathering unit can prioritize collecting information on wind speed and rainfall. By changing the priority of information gathering according to the type and scale of the disaster, more effective information gathering becomes possible. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input data on the type and scale of the disaster into a generating AI and have the generating AI determine the priority of information gathering.

[0039] The information gathering unit optimizes the collection range by referring to past disaster data during information gathering. For example, the information gathering unit can prioritize information gathering from areas that suffered significant damage based on past earthquake data. The information gathering unit can also prioritize information gathering from areas prone to flooding based on past flood data. Furthermore, the information gathering unit can prioritize information gathering from areas where damage is expected based on past typhoon data. By referring to past disaster data, the collection range can be optimized, enabling efficient information gathering. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input past disaster data into a generating AI and have the generating AI perform the optimization of the collection range.

[0040] The information gathering unit adjusts its information gathering method by considering geographical conditions and weather information. For example, in mountainous areas, the information gathering unit may use drones to gather information while considering topographic information. In urban areas, the information gathering unit may use satellite imagery to gather information while considering the impact of buildings. Furthermore, in the event of bad weather, the information gathering unit may prioritize information gathering from the ground. This allows for more effective information gathering by considering geographical conditions and weather information. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit may input geographical conditions and weather information data into a generating AI and have the generating AI adjust the collection method.

[0041] The information gathering unit integrates real-time information from social media during information gathering. For example, the information gathering unit can collect disaster-related posts from a first social media platform and analyze them in real time. The information gathering unit can also collect posts from a disaster information group on a second social media platform and extract important information. Furthermore, the information gathering unit can use hashtags on a third social media platform to collect photos and videos of disaster sites. By integrating real-time information from social media, the latest information can be collected quickly. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input social media data into a generating AI and have the generating AI perform the integration of real-time information.

[0042] The data analysis unit dynamically changes its analysis algorithm according to the progression of the disaster during data analysis. For example, in the initial stages of an earthquake, the data analysis unit prioritizes analyzing information on the epicenter and seismic intensity. The data analysis unit can analyze changes in water levels in real time according to the progression of a flood. Furthermore, the data analysis unit can analyze changes in wind speed and rainfall in real time as a typhoon approaches. This allows for more effective data analysis by changing the analysis algorithm according to the progression of the disaster. Some or all of the above processes in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input data on the progression of the disaster into a generating AI and have the generating AI execute changes to the analysis algorithm.

[0043] The data analysis unit improves the accuracy of its analysis by referring to past disaster data during data analysis. For example, the data analysis unit can improve the accuracy of identifying epicenters based on past earthquake data. The data analysis unit can improve the accuracy of water level predictions based on past flood data. Furthermore, the data analysis unit can improve the accuracy of wind speed and rainfall predictions based on past typhoon data. In this way, the accuracy of the analysis is improved by referring to past disaster data. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input past disaster data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0044] The data analysis department adjusts its analysis methods when analyzing data, taking into account geographical conditions and weather information. For example, in mountainous areas, the data analysis department considers topographic information during analysis. In urban areas, the data analysis department can consider the impact of buildings during analysis. Furthermore, during adverse weather conditions, the data analysis department can consider weather information during analysis. This allows for more effective data analysis by considering geographical conditions and weather information. Some or all of the above processes in the data analysis department may be performed using AI, for example, or without AI. For example, the data analysis department can input geographical conditions and weather information into a generating AI and have the generating AI perform the adjustment of the analysis method.

[0045] The data analysis department integrates real-time information from social media during data analysis. For example, the data analysis department can analyze disaster-related posts from a first social media platform to grasp the situation in real time. It can also analyze posts from a disaster information group on a second social media platform to extract important information. Furthermore, the data analysis department can use hashtags on a third social media platform to analyze photos and videos from the disaster site. This allows for rapid analysis of the latest information by integrating real-time information from social media. Some or all of the above processing in the data analysis department may be performed using AI, for example, or not. For example, the data analysis department can input social media data into a generating AI and have the generating AI perform the integration of real-time information.

[0046] The evacuation route calculation unit dynamically changes the route calculation algorithm according to the progress of the disaster when calculating evacuation routes. For example, in the initial stages of an earthquake, the evacuation route calculation unit prioritizes calculating routes away from the epicenter. Depending on the progress of a flood, the evacuation route calculation unit can prioritize calculating routes with lower water levels. Also, as a typhoon approaches, the evacuation route calculation unit can prioritize calculating routes with lower wind speeds. By changing the route calculation algorithm according to the progress of the disaster, it becomes possible to calculate more effective evacuation routes. Some or all of the above processing in the evacuation route calculation unit may be performed using AI, for example, or without AI. For example, the evacuation route calculation unit can input data on the progress of the disaster into a generating AI and have the generating AI execute the changes to the route calculation algorithm.

[0047] The evacuation route calculation unit improves the accuracy of route calculation by referring to past disaster data when calculating evacuation routes. For example, the evacuation route calculation unit can improve the accuracy of evacuation routes from the epicenter based on past earthquake data. The evacuation route calculation unit can improve the accuracy of routes with low water levels based on past flood data. In addition, the evacuation route calculation unit can improve the accuracy of routes with low wind speeds based on past typhoon data. In this way, the accuracy of route calculation is improved by referring to past disaster data. Some or all of the above processing in the evacuation route calculation unit may be performed using AI, for example, or without using AI. For example, the evacuation route calculation unit can input past disaster data into a generating AI and have the generating AI perform the improvement of route calculation accuracy.

[0048] The evacuation route calculation unit adjusts the route calculation method by considering geographical conditions and weather information when calculating evacuation routes. For example, in mountainous areas, the evacuation route calculation unit calculates routes by considering topographic information. In urban areas, the evacuation route calculation unit can calculate routes by considering the influence of buildings. Furthermore, in the event of bad weather, the evacuation route calculation unit can calculate routes by considering weather information. This makes it possible to calculate more effective evacuation routes by considering geographical conditions and weather information. Some or all of the above processing in the evacuation route calculation unit may be performed using AI, for example, or without using AI. For example, the evacuation route calculation unit can input geographical conditions and weather information data into a generating AI and have the generating AI perform adjustments to the route calculation method.

[0049] The evacuation route calculation unit integrates real-time information from social media when calculating evacuation routes. For example, the evacuation route calculation unit analyzes disaster-related posts from a first social media platform and calculates evacuation routes in real time. The evacuation route calculation unit can also analyze posts from a disaster information group on a second social media platform and calculate evacuation routes based on important information. Furthermore, the evacuation route calculation unit can use hashtags from a third social media platform to calculate evacuation routes based on photos and videos of the disaster site. By integrating real-time information from social media, it becomes possible to calculate evacuation routes that quickly reflect the latest information. Some or all of the above processing in the evacuation route calculation unit may be performed using AI, for example, or without AI. For example, the evacuation route calculation unit can input social media data into a generating AI and have the generating AI perform the integration of real-time information.

[0050] The rescue plan proposal unit dynamically changes its proposal algorithm according to the progress of the disaster when proposing a rescue plan. For example, in the initial stages of an earthquake, the rescue plan proposal unit prioritizes proposing rescue plans for areas around the epicenter. Depending on the progress of a flood, the rescue plan proposal unit can prioritize proposing rescue plans for areas with high water levels. Furthermore, as a typhoon approaches, the rescue plan proposal unit can prioritize proposing rescue plans for areas with high wind speeds. By changing the proposal algorithm according to the progress of the disaster, it becomes possible to propose more effective rescue plans. Some or all of the above processing in the rescue plan proposal unit may be performed using AI, for example, or without AI. For example, the rescue plan proposal unit can input data on the progress of the disaster into a generating AI and have the generating AI execute the changes to the proposal algorithm.

[0051] The rescue plan proposal unit improves the accuracy of its proposals by referring to past disaster data when proposing rescue plans. For example, the rescue plan proposal unit can improve the accuracy of rescue plans around epicenters based on past earthquake data. The rescue plan proposal unit can improve the accuracy of rescue plans for high-water-level areas based on past flood data. Furthermore, the rescue plan proposal unit can improve the accuracy of rescue plans for high-wind-speed areas based on past typhoon data. In this way, the accuracy of proposals is improved by referring to past disaster data. Some or all of the above processing in the rescue plan proposal unit may be performed using AI, for example, or without AI. For example, the rescue plan proposal unit can input past disaster data into a generating AI and have the generating AI perform the improvement of proposal accuracy.

[0052] The rescue plan proposal unit adjusts its proposal method when proposing a rescue plan, taking into account geographical conditions and weather information. For example, in mountainous areas, the rescue plan proposal unit can propose a rescue plan that takes topographic information into account. In urban areas, the rescue plan proposal unit can propose a rescue plan that takes into account the impact of buildings. Furthermore, in the event of severe weather, the rescue plan proposal unit can propose a rescue plan that takes weather information into account. This makes it possible to propose a more effective rescue plan by taking geographical conditions and weather information into account. Some or all of the above processing in the rescue plan proposal unit may be performed using AI, for example, or not using AI. For example, the rescue plan proposal unit can input geographical conditions and weather information data into a generating AI and have the generating AI perform the adjustment of the proposal method.

[0053] The rescue plan proposal unit integrates real-time information from social media when proposing a rescue plan. For example, the rescue plan proposal unit can analyze disaster-related posts from a first social media platform and propose a rescue plan in real time. The rescue plan proposal unit can also analyze posts from a second social media platform's disaster information group and propose a rescue plan based on important information. Furthermore, the rescue plan proposal unit can use hashtags from a third social media platform to propose a rescue plan based on photos and videos of the disaster site. By integrating real-time information from social media, it becomes possible to propose a rescue plan that quickly reflects the latest information. Some or all of the above processing in the rescue plan proposal unit may be performed using AI, for example, or not. For example, the rescue plan proposal unit can input social media data into a generating AI and have the generating AI perform the integration of real-time information.

[0054] The information sharing unit dynamically changes the sharing algorithm according to the progress of the disaster when sharing information. For example, in the initial stages of an earthquake, the information sharing unit prioritizes sharing information around the epicenter. Depending on the progress of a flood, the information sharing unit can prioritize sharing information about areas with high water levels. Also, as a typhoon approaches, the information sharing unit can prioritize sharing information about areas with high wind speeds. By changing the sharing algorithm according to the progress of the disaster, more effective information sharing becomes possible. Some or all of the above processing in the information sharing unit may be performed using AI, for example, or without AI. For example, the information sharing unit can input data on the progress of the disaster into a generating AI and have the generating AI execute the change in the sharing algorithm.

[0055] The information sharing unit improves the accuracy of information sharing by referring to past disaster data during information sharing. For example, the information sharing unit can improve the accuracy of information sharing around the epicenter based on past earthquake data. The information sharing unit can improve the accuracy of information sharing in areas with high water levels based on past flood data. Furthermore, the information sharing unit can improve the accuracy of information sharing in areas with high wind speeds based on past typhoon data. In this way, the accuracy of sharing is improved by referring to past disaster data. Some or all of the above processing in the information sharing unit may be performed using AI, for example, or without using AI. For example, the information sharing unit can input past disaster data into a generating AI and have the generating AI perform the improvement of sharing accuracy.

[0056] The information sharing unit adjusts the sharing method when sharing information, taking into account geographical conditions and weather information. For example, in mountainous areas, the information sharing unit considers topographic information when sharing information. In urban areas, the information sharing unit can consider the impact of buildings when sharing information. Furthermore, during bad weather, the information sharing unit can consider weather information when sharing information. This makes it possible to share information more effectively by taking geographical conditions and weather information into account. Some or all of the above processing in the information sharing unit may be performed using AI, for example, or without using AI. For example, the information sharing unit can input geographical conditions and weather information data into a generating AI and have the generating AI perform the adjustment of the sharing method.

[0057] The information sharing unit integrates real-time information from social media when sharing information. For example, the information sharing unit can collect disaster-related posts from a first social media platform and share the information in real time. The information sharing unit can also collect posts from a disaster information group on a second social media platform and share important information. Furthermore, the information sharing unit can use hashtags on a third social media platform to share photos and videos of the disaster site. This allows for the rapid sharing of the latest information by integrating real-time information from social media. Some or all of the above processing in the information sharing unit may be performed using AI, for example, or without AI. For example, the information sharing unit can input social media data into a generating AI and have the generating AI perform the integration of real-time information.

[0058] The drone integration unit dynamically changes the flight route according to the progression of the disaster during drone integration. For example, in the initial stages of an earthquake, the drone integration unit sets a flight route to collect information around the epicenter. Depending on the progression of a flood, the drone integration unit can set a flight route to collect information from areas with high water levels. Furthermore, as a typhoon approaches, the drone integration unit can set a flight route to collect information from areas with high wind speeds. By changing the flight route according to the progression of the disaster, more effective information collection becomes possible. Some or all of the above processing in the drone integration unit may be performed using AI, for example, or without AI. For example, the drone integration unit can input data on the progression of the disaster into a generating AI and have the generating AI execute changes to the flight route.

[0059] The drone integration unit adjusts the flight route considering geographical conditions and weather information during drone integration. For example, in mountainous areas, the drone integration unit sets the flight route considering terrain information. In urban areas, the drone integration unit can set the flight route considering the impact of buildings. Furthermore, in adverse weather conditions, the drone integration unit can set the flight route considering weather information. This makes it possible to collect information more effectively by considering geographical conditions and weather information. Some or all of the above processing in the drone integration unit may be performed using AI, for example, or without AI. For example, the drone integration unit can input geographical conditions and weather information data into a generating AI and have the generating AI perform the adjustment of the flight route.

[0060] The pattern recognition unit dynamically changes its recognition algorithm according to the progression of the disaster during pattern recognition. For example, in the initial stages of an earthquake, the pattern recognition unit prioritizes recognizing patterns around the epicenter. Depending on the progression of a flood, the pattern recognition unit can prioritize recognizing patterns in areas with high water levels. Furthermore, as a typhoon approaches, the pattern recognition unit can prioritize recognizing patterns in areas with high wind speeds. By changing the recognition algorithm according to the progression of the disaster, more effective pattern recognition becomes possible. Some or all of the above processing in the pattern recognition unit may be performed using AI, for example, or without AI. For example, the pattern recognition unit can input data on the progression of the disaster into a generating AI and have the generating AI execute the change in the recognition algorithm.

[0061] The pattern recognition unit adjusts its recognition method by considering geographical conditions and weather information during pattern recognition. For example, in mountainous areas, the pattern recognition unit can perform pattern recognition while considering topographic information. In urban areas, the pattern recognition unit can perform pattern recognition while considering the influence of buildings. Furthermore, in adverse weather conditions, the pattern recognition unit can perform pattern recognition while considering weather information. This makes it possible to perform more effective pattern recognition by considering geographical conditions and weather information. Some or all of the above processing in the pattern recognition unit may be performed using AI, for example, or without using AI. For example, the pattern recognition unit can input geographical conditions and weather information data into a generating AI and have the generating AI perform the adjustment of the recognition method.

[0062] The cloud sharing unit dynamically changes the sharing algorithm according to the progress of the disaster when sharing information to the cloud. For example, in the initial stages of an earthquake, the cloud sharing unit prioritizes sharing information around the epicenter to the cloud. Depending on the progress of a flood, the cloud sharing unit can prioritize sharing information from areas with high water levels to the cloud. Also, as a typhoon approaches, the cloud sharing unit can prioritize sharing information from areas with high wind speeds to the cloud. By changing the sharing algorithm according to the progress of the disaster, more effective information sharing becomes possible. Some or all of the above processing in the cloud sharing unit may be performed using AI, for example, or without AI. For example, the cloud sharing unit can input data on the progress of the disaster into a generating AI and have the generating AI execute the change in the sharing algorithm.

[0063] The cloud sharing unit improves the accuracy of sharing by referencing past disaster data during cloud sharing. For example, the cloud sharing unit can improve the accuracy of information sharing around the epicenter based on past earthquake data. The cloud sharing unit can improve the accuracy of information sharing in areas with high water levels based on past flood data. Furthermore, the cloud sharing unit can improve the accuracy of information sharing in areas with high wind speeds based on past typhoon data. In this way, the accuracy of sharing is improved by referencing past disaster data. Some or all of the above processing in the cloud sharing unit may be performed using AI, for example, or without using AI. For example, the cloud sharing unit can input past disaster data into a generating AI and have the generating AI perform the improvement of sharing accuracy.

[0064] The cloud sharing unit adjusts the sharing method when sharing data to the cloud, taking into account geographical conditions and weather information. For example, in mountainous areas, the cloud sharing unit considers topographic information when sharing data to the cloud. In urban areas, the cloud sharing unit can consider the impact of buildings when sharing data to the cloud. Furthermore, during bad weather, the cloud sharing unit can consider weather information when sharing data to the cloud. This makes it possible to share information more effectively by considering geographical conditions and weather information. Some or all of the above processing in the cloud sharing unit may be performed using AI, for example, or without AI. For example, the cloud sharing unit can input geographical conditions and weather information data into a generating AI and have the generating AI perform the adjustment of the sharing method.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The information gathering unit can acquire real-time location information of disaster victims during a disaster. For example, if a victim has a smartphone, its location can be determined using its GPS information. Furthermore, if a victim is wearing a wearable device, location information can be obtained from that device. It is also possible to collect location information posted by victims on social media. This allows for real-time location information of disaster victims, enabling rapid rescue operations.

[0067] The drone integration unit can acquire weather information in real time and dynamically change the flight route to optimize it. For example, in areas where strong winds are expected, it can change the drone's flight route to select a safer path. In addition, during rainy weather, it can adjust the drone's flight altitude to improve the accuracy of information gathering. Furthermore, the drone integration unit can also adjust the drone's flight schedule based on weather information. This allows for flexible responses to weather conditions.

[0068] The pattern recognition unit can analyze image data of disaster-stricken areas to quickly grasp the extent of damage during a disaster. For example, it can analyze satellite imagery and image data acquired from drones to understand the extent of building collapses and road closures. It can also analyze video data from disaster-stricken areas to grasp the progression of damage in real time. Furthermore, the pattern recognition unit can identify the extent of the damage based on the image data and determine the priority of rescue operations. This enables swift and accurate rescue operations.

[0069] The cloud sharing function can automatically classify data on the cloud to improve the efficiency of information sharing during disasters. For example, it can automatically classify disaster information by category, allowing stakeholders to quickly access the information they need. Furthermore, the cloud sharing function can select information to be shared preferentially based on its importance. In addition, the cloud sharing function can adjust the data update frequency, ensuring that the latest information is always shared. This enables more efficient and faster information sharing.

[0070] The information gathering department dynamically changes the priority of information collection depending on the type and scale of the disaster. For example, in the event of an earthquake, it prioritizes collecting information from the area around the epicenter. In the event of a flood, it can prioritize collecting information on river water levels and the condition of embankments. Also, when a typhoon is approaching, it can prioritize collecting information on wind speed and rainfall. By changing the priority of information collection according to the type and scale of the disaster, more effective information gathering becomes possible.

[0071] The information gathering department optimizes its collection scope by referring to past disaster data during information gathering. For example, based on past earthquake data, it prioritizes information gathering from areas that suffered significant damage. Based on past flood data, it can prioritize information gathering from areas prone to flooding. Furthermore, based on past typhoon data, it can prioritize information gathering from areas where damage is expected. In this way, by referring to past disaster data, the collection scope can be optimized, enabling efficient information gathering.

[0072] The information gathering unit adjusts its information gathering methods by considering geographical conditions and weather information. For example, in mountainous areas, information is gathered using drones, taking topographic information into consideration. In urban areas, information can be gathered using satellite imagery, taking into account the influence of buildings. Furthermore, during inclement weather, ground-based information gathering can be prioritized. By considering geographical conditions and weather information, more effective information gathering becomes possible.

[0073] The information gathering unit integrates real-time information from social media during the information gathering process. For example, it collects disaster-related posts from a first social media platform and analyzes them in real time. It can also collect posts from disaster information groups on a second social media platform and extract important information. Furthermore, it can use hashtags on a third social media platform to collect photos and videos from disaster sites. By integrating real-time information from social media, it can quickly gather the latest information.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The information gathering unit collects satellite images and information from the ground. For example, it can acquire high-resolution satellite images and collect sensor data and visual information from the ground. Step 2: The data analysis department analyzes the data collected by the information gathering department in real time. For example, it can process the collected data immediately to understand the progress of the disaster and the extent of the damage. Step 3: The evacuation route calculation unit calculates a safe evacuation route based on the data analyzed by the data analysis unit. For example, it can calculate the optimal route by considering conditions such as the width of the evacuation route, the presence or absence of obstacles, and the evacuation time. Step 4: The rescue plan proposal unit proposes the optimal rescue plan based on the evacuation routes calculated by the evacuation route calculation unit. For example, the rescue plan can be formulated considering the speed of rescue and the efficient allocation of resources. Step 5: The information sharing unit supports information sharing between rescue teams and disaster victims. For example, by considering the means of communication used and the types of information to be shared, it can enable rapid and accurate information sharing.

[0076] (Example of form 2) The disaster relief support system according to an embodiment of the present invention is a system that rapidly collects and analyzes information when a disaster occurs and proposes the optimal evacuation route and rescue plan. This disaster relief support system uses AI to rapidly collect and analyze information when a disaster occurs and proposes the optimal evacuation route and rescue plan. For example, the disaster relief support system's information collection unit collects information from satellite images and the ground. Next, the data analysis unit analyzes the collected data in real time, and the evacuation route calculation unit calculates a safe evacuation route. Furthermore, the rescue plan proposal unit proposes the optimal rescue plan, and the information sharing unit supports information sharing between rescue teams and disaster victims. This system aims to accelerate and streamline disaster response by integrating AI and drone technology, improving pattern recognition through machine learning, and utilizing a cloud-based information sharing system. This results in shorter rescue times, improved rescue success rates, and enhanced safety for disaster victims. As a result, the disaster relief support system can rapidly collect and analyze information when a disaster occurs and propose the optimal evacuation route and rescue plan.

[0077] The disaster relief support system according to this embodiment comprises an information gathering unit, a data analysis unit, an evacuation route calculation unit, a rescue plan proposal unit, and an information sharing unit. The information gathering unit collects satellite images and information from the ground. For example, the information gathering unit can acquire satellite images at high resolution and collect sensor data and visual information as information from the ground. The data analysis unit analyzes the data collected by the information gathering unit in real time. For example, the data analysis unit can process the collected data immediately to grasp the progress of the disaster and the extent of the damage. The evacuation route calculation unit calculates a safe evacuation route based on the data analyzed by the data analysis unit. For example, the evacuation route calculation unit can calculate the optimal route by considering conditions such as the width of the evacuation route, the presence or absence of obstacles, and the evacuation time. The rescue plan proposal unit proposes an optimal rescue plan based on the evacuation route calculated by the evacuation route calculation unit. For example, the rescue plan proposal unit can formulate a rescue plan considering the speed of rescue and the efficient allocation of resources. The information sharing unit supports information sharing between rescue teams and disaster victims. The information sharing unit can achieve rapid and accurate information sharing by considering, for example, the communication methods used and the types of information to be shared. As a result, the disaster relief support system according to this embodiment can rapidly collect and analyze information when a disaster occurs and propose optimal evacuation routes and rescue plans.

[0078] The Information Gathering Unit collects satellite imagery and ground-based information. Specifically, to acquire high-resolution satellite imagery, the Information Gathering Unit utilizes multiple satellites and collects images taken at different angles and times. This allows for a detailed understanding of the progression of a disaster and the extent of the damage. Ground-based information includes sensor data and visual observation data. Sensor data includes seismometers, water level gauges, temperature sensors, and humidity sensors, and these sensors are appropriately positioned according to the type and circumstances of the disaster. For example, data from seismometers is collected during earthquakes, and data from water level gauges is collected during floods. Visual observation data is collected using drones and ground-based cameras, allowing for a real-time understanding of the detailed situation in the affected area. The Information Gathering Unit centrally manages this data and stores it in a database. Furthermore, the Information Gathering Unit evaluates the quality of the collected data and supplements or corrects it as needed. This enables the Information Gathering Unit to provide accurate and reliable data and improve the overall performance of the system.

[0079] The Data Analysis Department analyzes data collected by the Information Gathering Department in real time. Specifically, the Data Analysis Department immediately processes collected satellite imagery and sensor data to understand the progression of disasters and the extent of damage. For example, it uses image analysis technology to identify the extent of building damage and flooding in disaster-stricken areas from satellite imagery, and analyzes sensor data to determine the earthquake epicenter and the rate of flood water level rise. The Data Analysis Department integrates this data to grasp the overall picture of the disaster in real time. Furthermore, the Data Analysis Department can utilize past disaster data and statistical information to predict disaster progression patterns and damage. For example, it can evaluate the flood risk in specific areas based on past flood data and formulate future countermeasures. In addition, the Data Analysis Department can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the Data Analysis Department can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0080] The evacuation route calculation unit calculates safe evacuation routes based on data analyzed by the data analysis unit. Specifically, the evacuation route calculation unit calculates the optimal route by considering conditions such as the width of the evacuation route, the presence or absence of obstacles, and the evacuation time. For example, it identifies the safest and fastest evacuation route by considering factors such as road width and gradient, traffic conditions, and the location of evacuation shelters. The evacuation route calculation unit uses AI to analyze these conditions and calculates the optimal evacuation route by simulating multiple scenarios. Furthermore, the evacuation route calculation unit can continuously modify evacuation routes based on data updated in real time, enabling it to respond to the latest situations. For example, if a road is newly closed or a new obstacle appears, the evacuation route calculation unit immediately incorporates the new data and updates the evacuation route. In addition, the evacuation route calculation unit can calculate more accurate evacuation routes by considering the characteristics of each region and past evacuation history. As a result, the evacuation route calculation unit can always provide highly accurate evacuation routes based on the latest information, supporting quick and appropriate evacuations.

[0081] The Rescue Plan Proposal Department proposes the optimal rescue plan based on the evacuation routes calculated by the Evacuation Route Calculation Department. Specifically, the Rescue Plan Proposal Department formulates rescue plans considering the speed of rescue and the efficient allocation of resources. For example, it optimizes the deployment and movement routes of rescue teams and the allocation of rescue materials to realize rapid and efficient rescue operations. The Rescue Plan Proposal Department uses AI to analyze these conditions and proposes the optimal rescue plan by simulating multiple scenarios. Furthermore, the Rescue Plan Proposal Department can continuously revise rescue plans based on data updated in real time to respond to the latest situations. For example, if new damage occurs or the status of rescue teams changes, the Rescue Plan Proposal Department immediately incorporates the new data and updates the rescue plan. In addition, the Rescue Plan Proposal Department can formulate more accurate rescue plans by considering the characteristics of each region and past rescue history. As a result, the Rescue Plan Proposal Department can always provide highly accurate rescue plans based on the latest information and support rapid and appropriate rescue operations.

[0082] The Information Sharing Department supports information sharing between rescue teams and disaster victims. Specifically, the Information Sharing Department ensures rapid and accurate information sharing by considering the communication methods used and the types of information to be shared. For example, it shares information with rescue teams in real time using a dedicated communication network, and provides evacuation information and rescue status to disaster victims via smartphones, radio, television, etc. The Information Sharing Department improves the speed and accuracy of information transmission by integrating these communication methods and centrally managing information. Furthermore, the Information Sharing Department enables two-way information sharing and can collect feedback from disaster victims. For example, disaster victims can report evacuation status and rescue requests using their smartphones, allowing rescue teams to grasp the situation of disaster victims in real time and respond quickly. In addition, the Information Sharing Department conducts information sharing training and system testing not only during disasters but also during normal times to maintain a consistently high level of information sharing. As a result, the Information Sharing Department can achieve rapid and accurate information sharing and maximize the efficiency and effectiveness of rescue operations.

[0083] The drone integration unit integrates drones to collect information. For example, the drone integration unit can collect information over a wide area by coordinating multiple drones. The drone integration unit considers the type of drone and the integration protocol to achieve efficient information collection. For example, the drone integration unit can combine fixed-wing drones and multi-rotor drones to simultaneously collect information over a wide area and detailed information. The drone integration unit can also optimize the flight routes of drones to collect information efficiently. For example, the drone integration unit sets the optimal flight route considering the terrain and weather information of the disaster area. This improves the range and accuracy of information collection by using drones. Some or all of the above processing in the drone integration unit may be performed using AI, for example, or without AI. For example, the drone integration unit can input the flight routes of drones into a generation AI and have the generation AI generate the optimal flight route.

[0084] The pattern recognition unit performs pattern recognition using machine learning. For example, the pattern recognition unit uses a machine learning algorithm to recognize patterns in disasters. The pattern recognition unit achieves highly accurate pattern recognition by considering the type of algorithm used and the content of the training data. For example, the pattern recognition unit can recognize patterns in disasters using deep learning. Furthermore, the pattern recognition unit can dynamically change the machine learning algorithm to grasp the progress and damage of a disaster in real time. For example, the pattern recognition unit performs rapid pattern recognition in the initial stages of a disaster and performs detailed pattern recognition as the disaster progresses. This improves the accuracy of disaster pattern recognition by using machine learning. Some or all of the above processing in the pattern recognition unit may be performed using AI, for example, or without using AI. For example, the pattern recognition unit can input disaster data into a generating AI and have the generating AI perform the pattern recognition.

[0085] The Cloud Sharing Unit performs cloud-based information sharing. For example, the Cloud Sharing Unit can use a cloud provider to store and share disaster information on the cloud. The Cloud Sharing Unit considers data storage methods and the cloud services used to achieve efficient information sharing. For example, the Cloud Sharing Unit uploads disaster information to the cloud in real time, allowing relevant parties to access it quickly. Furthermore, the Cloud Sharing Unit can synchronize and back up data on the cloud to ensure information reliability. For example, the Cloud Sharing Unit uses multiple cloud providers to ensure data redundancy. This enables rapid information sharing and access through cloud-based information sharing. Some or all of the above-described processes in the Cloud Sharing Unit may be performed using AI, or not. For example, the Cloud Sharing Unit can input the upload of disaster information into a generating AI and have the generating AI execute the upload to the cloud.

[0086] The information gathering unit estimates the user's emotions and adjusts the timing of information gathering based on the estimated emotions. For example, if the user is feeling anxious, the information gathering unit increases the frequency of information gathering and provides real-time updates. If the user is calm, the information gathering unit can maintain the normal frequency of information gathering and collect only the necessary information. Furthermore, if the user is in a state of panic, the information gathering unit can prioritize the collection of the most important information and provide it quickly. This allows for more appropriate information gathering by adjusting the timing of information gathering 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information gathering unit may be performed using AI, or not using AI. For example, the information gathering unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The information gathering unit dynamically changes the priority of information gathering according to the type and scale of the disaster. For example, in the event of an earthquake, the information gathering unit prioritizes collecting information from the area around the epicenter. In the event of a flood, the information gathering unit can prioritize collecting river water level information and the condition of embankments. Furthermore, when a typhoon is approaching, the information gathering unit can prioritize collecting information on wind speed and rainfall. By changing the priority of information gathering according to the type and scale of the disaster, more effective information gathering becomes possible. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input data on the type and scale of the disaster into a generating AI and have the generating AI determine the priority of information gathering.

[0088] The information gathering unit optimizes the collection range by referring to past disaster data during information gathering. For example, the information gathering unit can prioritize information gathering from areas that suffered significant damage based on past earthquake data. The information gathering unit can also prioritize information gathering from areas prone to flooding based on past flood data. Furthermore, the information gathering unit can prioritize information gathering from areas where damage is expected based on past typhoon data. By referring to past disaster data, the collection range can be optimized, enabling efficient information gathering. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input past disaster data into a generating AI and have the generating AI perform the optimization of the collection range.

[0089] The information gathering unit estimates the user's emotions and determines the priority of information to collect based on the estimated emotions. For example, if the user is feeling anxious, the information gathering unit will prioritize collecting information that provides reassurance. If the user is calm, the information gathering unit can prioritize collecting detailed information. Furthermore, if the user is in a state of panic, the information gathering unit can prioritize collecting the most important information. This allows for more appropriate information gathering by determining the priority of information to collect 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information gathering unit may be performed using AI, or not using AI. For example, the information gathering unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0090] The information gathering unit adjusts its information gathering method by considering geographical conditions and weather information. For example, in mountainous areas, the information gathering unit may use drones to gather information while considering topographic information. In urban areas, the information gathering unit may use satellite imagery to gather information while considering the impact of buildings. Furthermore, in the event of bad weather, the information gathering unit may prioritize information gathering from the ground. This allows for more effective information gathering by considering geographical conditions and weather information. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit may input geographical conditions and weather information data into a generating AI and have the generating AI adjust the collection method.

[0091] The information gathering unit integrates real-time information from social media during information gathering. For example, the information gathering unit can collect disaster-related posts from a first social media platform and analyze them in real time. The information gathering unit can also collect posts from a disaster information group on a second social media platform and extract important information. Furthermore, the information gathering unit can use hashtags on a third social media platform to collect photos and videos of disaster sites. By integrating real-time information from social media, the latest information can be collected quickly. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input social media data into a generating AI and have the generating AI perform the integration of real-time information.

[0092] The data analysis department estimates the user's emotions and adjusts the data analysis method based on the estimated emotions. For example, if the user is feeling anxious, the data analysis department can provide analysis results quickly. If the user is calm, the data analysis department can perform a detailed analysis and provide highly accurate results. Furthermore, if the user is in a state of panic, the data analysis department can prioritize the analysis of the most important information. In this way, by adjusting the data analysis method according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data analysis department may be performed using AI, for example, or not using AI. For example, the data analysis department can input user emotion data into a generative AI and have the generative AI adjust the data analysis method.

[0093] The data analysis unit dynamically changes its analysis algorithm according to the progression of the disaster during data analysis. For example, in the initial stages of an earthquake, the data analysis unit prioritizes analyzing information on the epicenter and seismic intensity. The data analysis unit can analyze changes in water levels in real time according to the progression of a flood. Furthermore, the data analysis unit can analyze changes in wind speed and rainfall in real time as a typhoon approaches. This allows for more effective data analysis by changing the analysis algorithm according to the progression of the disaster. Some or all of the above processes in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input data on the progression of the disaster into a generating AI and have the generating AI execute changes to the analysis algorithm.

[0094] The data analysis unit improves the accuracy of its analysis by referring to past disaster data during data analysis. For example, the data analysis unit can improve the accuracy of identifying epicenters based on past earthquake data. The data analysis unit can improve the accuracy of water level predictions based on past flood data. Furthermore, the data analysis unit can improve the accuracy of wind speed and rainfall predictions based on past typhoon data. In this way, the accuracy of the analysis is improved by referring to past disaster data. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input past disaster data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0095] The data analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the data analysis unit can provide a simple and highly visible display method. If the user is calm, the data analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a state of panic, the data analysis unit can highlight and display the most important information. In this way, by adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or not using AI. For example, the data analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0096] The data analysis department adjusts its analysis methods when analyzing data, taking into account geographical conditions and weather information. For example, in mountainous areas, the data analysis department considers topographic information during analysis. In urban areas, the data analysis department can consider the impact of buildings during analysis. Furthermore, during adverse weather conditions, the data analysis department can consider weather information during analysis. This allows for more effective data analysis by considering geographical conditions and weather information. Some or all of the above processes in the data analysis department may be performed using AI, for example, or without AI. For example, the data analysis department can input geographical conditions and weather information into a generating AI and have the generating AI perform the adjustment of the analysis method.

[0097] The data analysis department integrates real-time information from social media during data analysis. For example, the data analysis department can analyze disaster-related posts from a first social media platform to grasp the situation in real time. It can also analyze posts from a disaster information group on a second social media platform to extract important information. Furthermore, the data analysis department can use hashtags on a third social media platform to analyze photos and videos from the disaster site. This allows for rapid analysis of the latest information by integrating real-time information from social media. Some or all of the above processing in the data analysis department may be performed using AI, for example, or not. For example, the data analysis department can input social media data into a generating AI and have the generating AI perform the integration of real-time information.

[0098] The evacuation route calculation unit estimates the user's emotions and adjusts the evacuation route calculation method based on the estimated user emotions. For example, if the user is feeling anxious, the evacuation route calculation unit prioritizes calculating the safest route. If the user is calm, the evacuation route calculation unit can prioritize calculating the shortest route. Furthermore, if the user is in a state of panic, the evacuation route calculation unit can quickly calculate the route to the evacuation shelter. In this way, by adjusting the evacuation route calculation method according to the user's emotions, a more appropriate evacuation route can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the evacuation route calculation unit may be performed using AI, for example, or without using AI. For example, the evacuation route calculation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the evacuation route calculation method.

[0099] The evacuation route calculation unit dynamically changes the route calculation algorithm according to the progress of the disaster when calculating evacuation routes. For example, in the initial stages of an earthquake, the evacuation route calculation unit prioritizes calculating routes away from the epicenter. Depending on the progress of a flood, the evacuation route calculation unit can prioritize calculating routes with lower water levels. Also, as a typhoon approaches, the evacuation route calculation unit can prioritize calculating routes with lower wind speeds. By changing the route calculation algorithm according to the progress of the disaster, it becomes possible to calculate more effective evacuation routes. Some or all of the above processing in the evacuation route calculation unit may be performed using AI, for example, or without AI. For example, the evacuation route calculation unit can input data on the progress of the disaster into a generating AI and have the generating AI execute the changes to the route calculation algorithm.

[0100] The evacuation route calculation unit improves the accuracy of route calculation by referring to past disaster data when calculating evacuation routes. For example, the evacuation route calculation unit can improve the accuracy of evacuation routes from the epicenter based on past earthquake data. The evacuation route calculation unit can improve the accuracy of routes with low water levels based on past flood data. In addition, the evacuation route calculation unit can improve the accuracy of routes with low wind speeds based on past typhoon data. In this way, the accuracy of route calculation is improved by referring to past disaster data. Some or all of the above processing in the evacuation route calculation unit may be performed using AI, for example, or without using AI. For example, the evacuation route calculation unit can input past disaster data into a generating AI and have the generating AI perform the improvement of route calculation accuracy.

[0101] The evacuation route calculation unit estimates the user's emotions and adjusts the display method of the evacuation route based on the estimated user emotions. For example, if the user is feeling anxious, the evacuation route calculation unit provides a simple and highly visible display method. If the user is calm, the evacuation route calculation unit can provide a display method that includes detailed information. Furthermore, if the user is in a state of panic, the evacuation route calculation unit can highlight and display the most important information. This makes it possible to provide more appropriate information by adjusting the display method of the evacuation route according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the evacuation route calculation unit may be performed using AI, for example, or without AI. For example, the evacuation route calculation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0102] The evacuation route calculation unit adjusts the route calculation method by considering geographical conditions and weather information when calculating evacuation routes. For example, in mountainous areas, the evacuation route calculation unit calculates routes by considering topographic information. In urban areas, the evacuation route calculation unit can calculate routes by considering the influence of buildings. Furthermore, in the event of bad weather, the evacuation route calculation unit can calculate routes by considering weather information. This makes it possible to calculate more effective evacuation routes by considering geographical conditions and weather information. Some or all of the above processing in the evacuation route calculation unit may be performed using AI, for example, or without using AI. For example, the evacuation route calculation unit can input geographical conditions and weather information data into a generating AI and have the generating AI perform adjustments to the route calculation method.

[0103] The evacuation route calculation unit integrates real-time information from social media when calculating evacuation routes. For example, the evacuation route calculation unit analyzes disaster-related posts from a first social media platform and calculates evacuation routes in real time. The evacuation route calculation unit can also analyze posts from a disaster information group on a second social media platform and calculate evacuation routes based on important information. Furthermore, the evacuation route calculation unit can use hashtags from a third social media platform to calculate evacuation routes based on photos and videos of the disaster site. By integrating real-time information from social media, it becomes possible to calculate evacuation routes that quickly reflect the latest information. Some or all of the above processing in the evacuation route calculation unit may be performed using AI, for example, or without AI. For example, the evacuation route calculation unit can input social media data into a generating AI and have the generating AI perform the integration of real-time information.

[0104] The rescue plan proposal unit estimates the user's emotions and adjusts the rescue plan proposal method based on the estimated emotions. For example, if the user is feeling anxious, the rescue plan proposal unit will quickly propose a rescue plan. If the user is calm, the rescue plan proposal unit can propose a detailed rescue plan. Furthermore, if the user is in a state of panic, the rescue plan proposal unit can prioritize proposing the most important rescue plan. In this way, by adjusting the rescue plan proposal method according to the user's emotions, a more appropriate rescue plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the rescue plan proposal unit may be performed using AI, for example, or not using AI. For example, the rescue plan proposal unit can input the user's emotion data into a generative AI and have the generative AI perform the adjustment of the proposal method.

[0105] The rescue plan proposal unit dynamically changes its proposal algorithm according to the progress of the disaster when proposing a rescue plan. For example, in the initial stages of an earthquake, the rescue plan proposal unit prioritizes proposing rescue plans for areas around the epicenter. Depending on the progress of a flood, the rescue plan proposal unit can prioritize proposing rescue plans for areas with high water levels. Furthermore, as a typhoon approaches, the rescue plan proposal unit can prioritize proposing rescue plans for areas with high wind speeds. By changing the proposal algorithm according to the progress of the disaster, it becomes possible to propose more effective rescue plans. Some or all of the above processing in the rescue plan proposal unit may be performed using AI, for example, or without AI. For example, the rescue plan proposal unit can input data on the progress of the disaster into a generating AI and have the generating AI execute the changes to the proposal algorithm.

[0106] The rescue plan proposal unit improves the accuracy of its proposals by referring to past disaster data when proposing rescue plans. For example, the rescue plan proposal unit can improve the accuracy of rescue plans around epicenters based on past earthquake data. The rescue plan proposal unit can improve the accuracy of rescue plans for high-water-level areas based on past flood data. Furthermore, the rescue plan proposal unit can improve the accuracy of rescue plans for high-wind-speed areas based on past typhoon data. In this way, the accuracy of proposals is improved by referring to past disaster data. Some or all of the above processing in the rescue plan proposal unit may be performed using AI, for example, or without AI. For example, the rescue plan proposal unit can input past disaster data into a generating AI and have the generating AI perform the improvement of proposal accuracy.

[0107] The rescue plan proposal unit estimates the user's emotions and adjusts the display method of the rescue plan based on the estimated emotions. For example, if the user is feeling anxious, the rescue plan proposal unit provides a simple and highly visible display method. If the user is calm, the rescue plan proposal unit can provide a display method that includes detailed information. Furthermore, if the user is in a state of panic, the rescue plan proposal unit can highlight and display the most important information. This allows for more appropriate information to be provided by adjusting the display method of the rescue plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the rescue plan proposal unit may be performed using AI, for example, or not using AI. For example, the rescue plan proposal unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0108] The rescue plan proposal unit adjusts its proposal method when proposing a rescue plan, taking into account geographical conditions and weather information. For example, in mountainous areas, the rescue plan proposal unit can propose a rescue plan that takes topographic information into account. In urban areas, the rescue plan proposal unit can propose a rescue plan that takes into account the impact of buildings. Furthermore, in the event of severe weather, the rescue plan proposal unit can propose a rescue plan that takes weather information into account. This makes it possible to propose a more effective rescue plan by taking geographical conditions and weather information into account. Some or all of the above processing in the rescue plan proposal unit may be performed using AI, for example, or not using AI. For example, the rescue plan proposal unit can input geographical conditions and weather information data into a generating AI and have the generating AI perform the adjustment of the proposal method.

[0109] The rescue plan proposal unit integrates real-time information from social media when proposing a rescue plan. For example, the rescue plan proposal unit can analyze disaster-related posts from a first social media platform and propose a rescue plan in real time. The rescue plan proposal unit can also analyze posts from a second social media platform's disaster information group and propose a rescue plan based on important information. Furthermore, the rescue plan proposal unit can use hashtags from a third social media platform to propose a rescue plan based on photos and videos of the disaster site. By integrating real-time information from social media, it becomes possible to propose a rescue plan that quickly reflects the latest information. Some or all of the above processing in the rescue plan proposal unit may be performed using AI, for example, or not. For example, the rescue plan proposal unit can input social media data into a generating AI and have the generating AI perform the integration of real-time information.

[0110] The information sharing unit estimates the user's emotions and adjusts the method of information sharing based on the estimated emotions. For example, if the user is feeling anxious, the information sharing unit will quickly share information. If the user is calm, the information sharing unit can share detailed information. Furthermore, if the user is in a state of panic, the information sharing unit can prioritize sharing the most important information. This allows for more appropriate information sharing by adjusting the method of information sharing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the information sharing unit may be performed using AI, or not using AI. For example, the information sharing unit can input user emotion data into the generative AI and have the generative AI adjust the method of information sharing.

[0111] The information sharing unit dynamically changes the sharing algorithm according to the progress of the disaster when sharing information. For example, in the initial stages of an earthquake, the information sharing unit prioritizes sharing information around the epicenter. Depending on the progress of a flood, the information sharing unit can prioritize sharing information about areas with high water levels. Also, as a typhoon approaches, the information sharing unit can prioritize sharing information about areas with high wind speeds. By changing the sharing algorithm according to the progress of the disaster, more effective information sharing becomes possible. Some or all of the above processing in the information sharing unit may be performed using AI, for example, or without AI. For example, the information sharing unit can input data on the progress of the disaster into a generating AI and have the generating AI execute the change in the sharing algorithm.

[0112] The information sharing unit improves the accuracy of information sharing by referring to past disaster data during information sharing. For example, the information sharing unit can improve the accuracy of information sharing around the epicenter based on past earthquake data. The information sharing unit can improve the accuracy of information sharing in areas with high water levels based on past flood data. Furthermore, the information sharing unit can improve the accuracy of information sharing in areas with high wind speeds based on past typhoon data. In this way, the accuracy of sharing is improved by referring to past disaster data. Some or all of the above processing in the information sharing unit may be performed using AI, for example, or without using AI. For example, the information sharing unit can input past disaster data into a generating AI and have the generating AI perform the improvement of sharing accuracy.

[0113] The information sharing unit estimates the user's emotions and determines the priority of information sharing based on the estimated emotions. For example, if the user is feeling anxious, the information sharing unit prioritizes sharing information that provides reassurance. If the user is calm, the information sharing unit can prioritize sharing detailed information. Furthermore, if the user is in a state of panic, the information sharing unit can prioritize sharing the most important information. This allows for more appropriate information sharing by determining the priority of information sharing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information sharing unit may be performed using AI, or not using AI. For example, the information sharing unit can input user emotion data into the generative AI and have the generative AI determine the priority of information sharing.

[0114] The information sharing unit adjusts the sharing method when sharing information, taking into account geographical conditions and weather information. For example, in mountainous areas, the information sharing unit considers topographic information when sharing information. In urban areas, the information sharing unit can consider the impact of buildings when sharing information. Furthermore, during bad weather, the information sharing unit can consider weather information when sharing information. This makes it possible to share information more effectively by taking geographical conditions and weather information into account. Some or all of the above processing in the information sharing unit may be performed using AI, for example, or without using AI. For example, the information sharing unit can input geographical conditions and weather information data into a generating AI and have the generating AI perform the adjustment of the sharing method.

[0115] The information sharing unit integrates real-time information from social media when sharing information. For example, the information sharing unit can collect disaster-related posts from a first social media platform and share the information in real time. The information sharing unit can also collect posts from a disaster information group on a second social media platform and share important information. Furthermore, the information sharing unit can use hashtags on a third social media platform to share photos and videos of the disaster site. This allows for the rapid sharing of the latest information by integrating real-time information from social media. Some or all of the above processing in the information sharing unit may be performed using AI, for example, or without AI. For example, the information sharing unit can input social media data into a generating AI and have the generating AI perform the integration of real-time information.

[0116] The drone integration unit estimates the user's emotions and adjusts the drone's flight path based on the estimated emotions. For example, if the user is feeling anxious, the drone integration unit can set a flight path to quickly gather information. If the user is calm, the drone integration unit can set a flight path to gather detailed information. Furthermore, if the user is in a state of panic, the drone integration unit can set a flight path to gather the most important information. This allows for more appropriate information gathering by adjusting the drone's flight path according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the drone integration unit may be performed using AI, for example, or without AI. For example, the drone integration unit can input user emotion data into a generative AI and have the generative AI adjust the flight path.

[0117] The drone integration unit dynamically changes the flight route according to the progression of the disaster during drone integration. For example, in the initial stages of an earthquake, the drone integration unit sets a flight route to collect information around the epicenter. Depending on the progression of a flood, the drone integration unit can set a flight route to collect information from areas with high water levels. Furthermore, as a typhoon approaches, the drone integration unit can set a flight route to collect information from areas with high wind speeds. By changing the flight route according to the progression of the disaster, more effective information collection becomes possible. Some or all of the above processing in the drone integration unit may be performed using AI, for example, or without AI. For example, the drone integration unit can input data on the progression of the disaster into a generating AI and have the generating AI execute changes to the flight route.

[0118] The drone integration unit estimates the user's emotions and determines the drone's flight priority based on the estimated emotions. For example, if the user is feeling anxious, the drone integration unit may set a flight priority to collect reassuring information. If the user is calm, the drone integration unit may set a flight priority to collect detailed information. Furthermore, if the user is in a state of panic, the drone integration unit may set a flight priority to collect the most important information. This allows for more appropriate information collection by determining the drone's flight priority 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the drone integration unit may be performed using AI, for example, or without AI. For example, the drone integration unit can input user emotion data into a generative AI and have the generative AI determine the flight priority.

[0119] The drone integration unit adjusts the flight route considering geographical conditions and weather information during drone integration. For example, in mountainous areas, the drone integration unit sets the flight route considering terrain information. In urban areas, the drone integration unit can set the flight route considering the impact of buildings. Furthermore, in adverse weather conditions, the drone integration unit can set the flight route considering weather information. This makes it possible to collect information more effectively by considering geographical conditions and weather information. Some or all of the above processing in the drone integration unit may be performed using AI, for example, or without AI. For example, the drone integration unit can input geographical conditions and weather information data into a generating AI and have the generating AI perform the adjustment of the flight route.

[0120] The pattern recognition unit estimates the user's emotions and adjusts the pattern recognition method based on the estimated emotions. For example, if the user is feeling anxious, the pattern recognition unit performs pattern recognition quickly. If the user is calm, the pattern recognition unit can perform detailed pattern recognition. Furthermore, if the user is in a state of panic, the pattern recognition unit can prioritize recognizing the most important patterns. This allows for more appropriate pattern recognition by adjusting the pattern recognition method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the pattern recognition unit may be performed using AI, or not using AI. For example, the pattern recognition unit can input user emotion data into the generative AI and have the generative AI adjust the pattern recognition method.

[0121] The pattern recognition unit dynamically changes its recognition algorithm according to the progression of the disaster during pattern recognition. For example, in the initial stages of an earthquake, the pattern recognition unit prioritizes recognizing patterns around the epicenter. Depending on the progression of a flood, the pattern recognition unit can prioritize recognizing patterns in areas with high water levels. Furthermore, as a typhoon approaches, the pattern recognition unit can prioritize recognizing patterns in areas with high wind speeds. By changing the recognition algorithm according to the progression of the disaster, more effective pattern recognition becomes possible. Some or all of the above processing in the pattern recognition unit may be performed using AI, for example, or without AI. For example, the pattern recognition unit can input data on the progression of the disaster into a generating AI and have the generating AI execute the change in the recognition algorithm.

[0122] The pattern recognition unit estimates the user's emotions and determines the priority of pattern recognition based on the estimated emotions. For example, if the user is feeling anxious, the pattern recognition unit prioritizes reassuring patterns. If the user is calm, the pattern recognition unit can prioritize recognizing detailed patterns. Furthermore, if the user is in a state of panic, the pattern recognition unit can prioritize recognizing the most important patterns. This allows for more appropriate pattern recognition by determining the priority of pattern recognition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the pattern recognition unit may be performed using AI, or not using AI. For example, the pattern recognition unit can input user emotion data into the generative AI and have the generative AI determine the priority of pattern recognition.

[0123] The pattern recognition unit adjusts its recognition method by considering geographical conditions and weather information during pattern recognition. For example, in mountainous areas, the pattern recognition unit can perform pattern recognition while considering topographic information. In urban areas, the pattern recognition unit can perform pattern recognition while considering the influence of buildings. Furthermore, in adverse weather conditions, the pattern recognition unit can perform pattern recognition while considering weather information. This makes it possible to perform more effective pattern recognition by considering geographical conditions and weather information. Some or all of the above processing in the pattern recognition unit may be performed using AI, for example, or without using AI. For example, the pattern recognition unit can input geographical conditions and weather information data into a generating AI and have the generating AI perform the adjustment of the recognition method.

[0124] The cloud sharing unit estimates the user's emotions and adjusts the cloud sharing method based on the estimated emotions. For example, if the user is feeling anxious, the cloud sharing unit will quickly share information to the cloud. If the user is calm, the cloud sharing unit can share detailed information to the cloud. Furthermore, if the user is in a state of panic, the cloud sharing unit can prioritize sharing the most important information to the cloud. This allows for more appropriate information sharing by adjusting the cloud sharing method 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the cloud sharing unit may be performed using AI, or not using AI. For example, the cloud sharing unit can input user emotion data into a generative AI and have the generative AI adjust the cloud sharing method.

[0125] The cloud sharing unit dynamically changes the sharing algorithm according to the progress of the disaster when sharing information to the cloud. For example, in the initial stages of an earthquake, the cloud sharing unit prioritizes sharing information around the epicenter to the cloud. Depending on the progress of a flood, the cloud sharing unit can prioritize sharing information from areas with high water levels to the cloud. Also, as a typhoon approaches, the cloud sharing unit can prioritize sharing information from areas with high wind speeds to the cloud. By changing the sharing algorithm according to the progress of the disaster, more effective information sharing becomes possible. Some or all of the above processing in the cloud sharing unit may be performed using AI, for example, or without AI. For example, the cloud sharing unit can input data on the progress of the disaster into a generating AI and have the generating AI execute the change in the sharing algorithm.

[0126] The cloud sharing unit improves the accuracy of sharing by referencing past disaster data during cloud sharing. For example, the cloud sharing unit can improve the accuracy of information sharing around the epicenter based on past earthquake data. The cloud sharing unit can improve the accuracy of information sharing in areas with high water levels based on past flood data. Furthermore, the cloud sharing unit can improve the accuracy of information sharing in areas with high wind speeds based on past typhoon data. In this way, the accuracy of sharing is improved by referencing past disaster data. Some or all of the above processing in the cloud sharing unit may be performed using AI, for example, or without using AI. For example, the cloud sharing unit can input past disaster data into a generating AI and have the generating AI perform the improvement of sharing accuracy.

[0127] The cloud sharing unit estimates the user's emotions and determines the priority of cloud sharing based on the estimated emotions. For example, if the user is feeling anxious, the cloud sharing unit prioritizes sharing information that provides reassurance to the cloud. If the user is calm, the cloud sharing unit can prioritize sharing detailed information to the cloud. Furthermore, if the user is in a state of panic, the cloud sharing unit can prioritize sharing the most important information to the cloud. This allows for more appropriate information sharing by determining the priority of cloud sharing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the cloud sharing unit may be performed using AI, or not using AI. For example, the cloud sharing unit can input user emotion data into a generative AI and have the generative AI determine the priority of cloud sharing.

[0128] The cloud sharing unit adjusts the sharing method when sharing data to the cloud, taking into account geographical conditions and weather information. For example, in mountainous areas, the cloud sharing unit considers topographic information when sharing data to the cloud. In urban areas, the cloud sharing unit can consider the impact of buildings when sharing data to the cloud. Furthermore, during bad weather, the cloud sharing unit can consider weather information when sharing data to the cloud. This makes it possible to share information more effectively by considering geographical conditions and weather information. Some or all of the above processing in the cloud sharing unit may be performed using AI, for example, or without AI. For example, the cloud sharing unit can input geographical conditions and weather information data into a generating AI and have the generating AI perform the adjustment of the sharing method.

[0129] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0130] The information gathering unit can acquire real-time location information of disaster victims during a disaster. For example, if a victim has a smartphone, its location can be determined using its GPS information. Furthermore, if a victim is wearing a wearable device, location information can be obtained from that device. It is also possible to collect location information posted by victims on social media. This allows for real-time location information of disaster victims, enabling rapid rescue operations.

[0131] The drone integration unit can acquire weather information in real time and dynamically change the flight route to optimize it. For example, in areas where strong winds are expected, it can change the drone's flight route to select a safer path. In addition, during rainy weather, it can adjust the drone's flight altitude to improve the accuracy of information gathering. Furthermore, the drone integration unit can also adjust the drone's flight schedule based on weather information. This allows for flexible responses to weather conditions.

[0132] The pattern recognition unit can analyze image data of disaster-stricken areas to quickly grasp the extent of damage during a disaster. For example, it can analyze satellite imagery and image data acquired from drones to understand the extent of building collapses and road closures. It can also analyze video data from disaster-stricken areas to grasp the progression of damage in real time. Furthermore, the pattern recognition unit can identify the extent of the damage based on the image data and determine the priority of rescue operations. This enables swift and accurate rescue operations.

[0133] The cloud sharing function can automatically classify data on the cloud to improve the efficiency of information sharing during disasters. For example, it can automatically classify disaster information by category, allowing stakeholders to quickly access the information they need. Furthermore, the cloud sharing function can select information to be shared preferentially based on its importance. In addition, the cloud sharing function can adjust the data update frequency, ensuring that the latest information is always shared. This enables more efficient and faster information sharing.

[0134] The information gathering unit estimates the user's emotions and adjusts the timing of information gathering based on those estimates. For example, if the user is feeling anxious, the frequency of information gathering is increased to provide real-time updates. If the user is calm, the frequency of information gathering is kept at normal, and only necessary information is collected. Furthermore, if the user is in a state of panic, the most important information can be prioritized and provided quickly. In this way, by adjusting the timing of information gathering according to the user's emotions, more appropriate information gathering becomes possible.

[0135] The information gathering department dynamically changes the priority of information collection depending on the type and scale of the disaster. For example, in the event of an earthquake, it prioritizes collecting information from the area around the epicenter. In the event of a flood, it can prioritize collecting information on river water levels and the condition of embankments. Also, when a typhoon is approaching, it can prioritize collecting information on wind speed and rainfall. By changing the priority of information collection according to the type and scale of the disaster, more effective information gathering becomes possible.

[0136] The information gathering department optimizes its collection scope by referring to past disaster data during information gathering. For example, based on past earthquake data, it prioritizes information gathering from areas that suffered significant damage. Based on past flood data, it can prioritize information gathering from areas prone to flooding. Furthermore, based on past typhoon data, it can prioritize information gathering from areas where damage is expected. In this way, by referring to past disaster data, the collection scope can be optimized, enabling efficient information gathering.

[0137] The information gathering unit estimates the user's emotions and determines the priority of information to collect based on those estimated emotions. For example, if the user is feeling anxious, it prioritizes collecting information that provides reassurance. If the user is calm, it can prioritize collecting detailed information. Furthermore, if the user is in a state of panic, it can prioritize collecting the most important information. By prioritizing the information to collect according to the user's emotions, more appropriate information gathering becomes possible.

[0138] The information gathering unit adjusts its information gathering methods by considering geographical conditions and weather information. For example, in mountainous areas, information is gathered using drones, taking topographic information into consideration. In urban areas, information can be gathered using satellite imagery, taking into account the influence of buildings. Furthermore, during inclement weather, ground-based information gathering can be prioritized. By considering geographical conditions and weather information, more effective information gathering becomes possible.

[0139] The information gathering unit integrates real-time information from social media during the information gathering process. For example, it collects disaster-related posts from a first social media platform and analyzes them in real time. It can also collect posts from disaster information groups on a second social media platform and extract important information. Furthermore, it can use hashtags on a third social media platform to collect photos and videos from disaster sites. By integrating real-time information from social media, it can quickly gather the latest information.

[0140] The following briefly describes the processing flow for example form 2.

[0141] Step 1: The information gathering unit collects satellite images and information from the ground. For example, it can acquire high-resolution satellite images and collect sensor data and visual information from the ground. Step 2: The data analysis department analyzes the data collected by the information gathering department in real time. For example, it can process the collected data immediately to understand the progress of the disaster and the extent of the damage. Step 3: The evacuation route calculation unit calculates a safe evacuation route based on the data analyzed by the data analysis unit. For example, it can calculate the optimal route by considering conditions such as the width of the evacuation route, the presence or absence of obstacles, and the evacuation time. Step 4: The rescue plan proposal unit proposes the optimal rescue plan based on the evacuation routes calculated by the evacuation route calculation unit. For example, the rescue plan can be formulated considering the speed of rescue and the efficient allocation of resources. Step 5: The information sharing unit supports information sharing between rescue teams and disaster victims. For example, by considering the means of communication used and the types of information to be shared, it can enable rapid and accurate information sharing.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the information gathering unit, data analysis unit, evacuation route calculation unit, rescue plan proposal unit, and information sharing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the information gathering unit collects information using the camera 42 and sensor data of the smart device 14, and the specific processing unit 290 of the data processing unit 12 analyzes the collected data in real time. The evacuation route calculation unit calculates a safe evacuation route using the specific processing unit 290 of the data processing unit 12, and the rescue plan proposal unit proposes an optimal rescue plan based on the calculated evacuation route. The information sharing unit supports information sharing between the rescue team and the victims via the communication I / F 44 of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0146] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the information gathering unit, data analysis unit, evacuation route calculation unit, rescue plan proposal unit, and information sharing unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the information gathering unit collects information using the camera 42 and sensor data of the smart glasses 214 and analyzes the collected data in real time using the identification processing unit 290 of the data processing unit 12. The evacuation route calculation unit calculates a safe evacuation route using the identification processing unit 290 of the data processing unit 12, and the rescue plan proposal unit proposes an optimal rescue plan based on the calculated evacuation route. The information sharing unit supports information sharing between the rescue team and the victims via the communication I / F 44 of the smart glasses 214. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0162] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the information gathering unit, data analysis unit, evacuation route calculation unit, rescue plan proposal unit, and information sharing unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the information gathering unit collects information using the camera 42 and sensor data of the headset terminal 314, and the specific processing unit 290 of the data processing unit 12 analyzes the collected data in real time. The evacuation route calculation unit calculates a safe evacuation route using the specific processing unit 290 of the data processing unit 12, and the rescue plan proposal unit proposes an optimal rescue plan based on the calculated evacuation route. The information sharing unit supports information sharing between the rescue team and the victims via the communication I / F 44 of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0178] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] Each of the multiple elements described above, including the information gathering unit, data analysis unit, evacuation route calculation unit, rescue plan proposal unit, and information sharing unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the information gathering unit collects information using the camera 42 and sensor data of the robot 414, and the specific processing unit 290 of the data processing unit 12 analyzes the collected data in real time. The evacuation route calculation unit calculates a safe evacuation route using the specific processing unit 290 of the data processing unit 12, and the rescue plan proposal unit proposes an optimal rescue plan based on the calculated evacuation route. The information sharing unit supports information sharing between the rescue team and the victims via the communication I / F 44 of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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."

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] (Note 1) The information gathering unit collects satellite images and information from the ground, A data analysis unit analyzes the data collected by the aforementioned information collection unit in real time, An evacuation route calculation unit calculates a safe evacuation route based on the data analyzed by the aforementioned data analysis unit, A rescue plan proposal unit proposes an optimal rescue plan based on the evacuation route calculated by the aforementioned evacuation route calculation unit, It includes an information sharing unit that supports information sharing between rescue teams and disaster victims. A system characterized by the following features. (Note 2) It is equipped with a drone integration unit that integrates drones to collect information. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a pattern recognition unit that performs pattern recognition using machine learning. The system described in Appendix 1, characterized by the features described herein. (Note 4) It features a cloud sharing section for cloud-based information sharing. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned information gathering unit, It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned information gathering unit, The priority of information gathering is dynamically changed according to the type and scale of the disaster. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned information gathering unit, When collecting information, refer to past disaster data to optimize the collection scope. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned information gathering unit, It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned information gathering unit, When gathering information, adjust the collection method considering geographical conditions and weather information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned information gathering unit, When gathering information, integrate real-time information from social media. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned data analysis unit, We estimate user sentiment and adjust the data analysis method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned data analysis unit, During data analysis, the analysis algorithm is dynamically changed according to the progression of the disaster. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned data analysis unit, When analyzing data, referencing past disaster data improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned data analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned data analysis unit, When analyzing data, adjust the analysis method to take geographical conditions and weather information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned data analysis unit, Integrate real-time information from social media during data analysis. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned evacuation route calculation unit, The system estimates the user's emotions and adjusts the evacuation route calculation method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned evacuation route calculation unit, When calculating evacuation routes, the route calculation algorithm is dynamically changed according to the progress of the disaster. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned evacuation route calculation unit, When calculating evacuation routes, we improve the accuracy of route calculation by referring to past disaster data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned evacuation route calculation unit, The system estimates the user's emotions and adjusts how evacuation routes are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned evacuation route calculation unit, When calculating evacuation routes, the route calculation method is adjusted to take into account geographical conditions and weather information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned evacuation route calculation unit, When calculating evacuation routes, real-time information from social media will be integrated. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned rescue plan proposal department, The system estimates the user's emotions and adjusts the rescue plan proposal method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned rescue plan proposal department, When proposing a rescue plan, the proposed algorithm is dynamically changed according to the progress of the disaster. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned rescue plan proposal department, When proposing rescue plans, we improve the accuracy of the proposals by referring to past disaster data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned rescue plan proposal department, The system estimates the user's emotions and adjusts how the rescue plan is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned rescue plan proposal department, When proposing rescue plans, adjust the proposal method considering geographical conditions and weather information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned rescue plan proposal department, Integrate real-time information from social media when proposing rescue plans. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned information sharing unit is: It estimates user emotions and adjusts the way information is shared based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned information sharing unit is: When sharing information, the sharing algorithm is dynamically changed according to the progress of the disaster. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned information sharing unit is: When sharing information, refer to past disaster data to improve the accuracy of the sharing process. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned information sharing unit is: It estimates user sentiment and determines the priority of information sharing based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned information sharing unit is: When sharing information, adjust the sharing method considering geographical conditions and weather information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned information sharing unit is: When sharing information, integrate real-time information from social media. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned drone integration unit is It estimates the user's emotions and adjusts the drone's flight path based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned drone integration unit is When integrating drones, the flight path is dynamically changed according to the progression of the disaster. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned drone integration unit is The system estimates the user's emotions and determines the drone's flight priority based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned drone integration unit is When integrating drones, the flight route is adjusted considering geographical conditions and weather information. The system described in Appendix 2, characterized by the features described herein. (Note 39) The pattern recognition unit, It estimates the user's emotions and adjusts the pattern recognition method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The pattern recognition unit, During pattern recognition, the recognition algorithm is dynamically changed according to the progression of the disaster. The system described in Appendix 3, characterized by the features described herein. (Note 41) The pattern recognition unit, The system estimates the user's emotions and determines the priority of pattern recognition based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 42) The pattern recognition unit, When recognizing patterns, the recognition method is adjusted to take into account geographical conditions and weather information. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned cloud sharing section is It estimates the user's emotions and adjusts the cloud sharing method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned cloud sharing section is When sharing data to the cloud, the sharing algorithm is dynamically changed according to the progress of the disaster. The system described in Appendix 4, characterized by the features described herein. (Note 45) The aforementioned cloud sharing section is When sharing data to the cloud, referencing past disaster data improves the accuracy of the sharing process. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned cloud sharing section is It estimates user sentiment and prioritizes cloud sharing based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 47) The aforementioned cloud sharing section is When sharing to the cloud, the sharing method is adjusted to take geographical conditions and weather information into consideration. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0214] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The information gathering unit collects satellite images and information from the ground, A data analysis unit analyzes the data collected by the aforementioned information collection unit in real time, An evacuation route calculation unit calculates a safe evacuation route based on the data analyzed by the aforementioned data analysis unit, A rescue plan proposal unit proposes an optimal rescue plan based on the evacuation route calculated by the aforementioned evacuation route calculation unit, It includes an information sharing unit that supports information sharing between rescue teams and disaster victims. A system characterized by the following features.

2. It is equipped with a drone integration unit that integrates drones to collect information. The system according to feature 1.

3. It includes a pattern recognition unit that performs pattern recognition using machine learning. The system according to feature 1.

4. It features a cloud sharing section for cloud-based information sharing. The system according to feature 1.

5. The aforementioned information gathering unit, It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

6. The aforementioned information gathering unit, The priority of information gathering is dynamically changed according to the type and scale of the disaster. The system according to feature 1.

7. The aforementioned information gathering unit, When collecting information, refer to past disaster data to optimize the collection scope. The system according to feature 1.

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

9. The aforementioned information gathering unit, When gathering information, adjust the collection method considering geographical conditions and weather information. The system according to feature 1.

10. The aforementioned information gathering unit, When gathering information, integrate real-time information from social media. The system according to feature 1.

11. The aforementioned data analysis unit, We estimate user sentiment and adjust the data analysis method based on the estimated user sentiment. The system according to feature 1.

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