system

Abstract

The system according to this embodiment aims to provide appropriate support based on the needs of disaster victims. [Solution] The system according to the embodiment comprises a collection unit, a matching unit, a proposal unit, a distribution unit, and a priority proposal unit. The collection unit collects the needs of disaster victims. The matching unit matches appropriate support based on the needs collected by the collection unit. The proposal unit proposes volunteer activities based on the support matched by the matching unit. The distribution unit distributes relief supplies based on the support proposed by the proposal unit. The priority proposal unit proposes priorities for infrastructure development based on the support proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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, there have been problems such as a mismatch between the needs of disaster victims and the provided support, a mismatch in volunteer activities, insufficient information collection and sharing, and lack of efficiency in support.

[0005] The system according to the embodiment aims to provide appropriate support based on the needs of disaster victims.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a matching unit, a proposal unit, a distribution unit, and a priority proposal unit. The collection unit collects the needs of disaster victims. The matching unit matches appropriate support based on the needs collected by the collection unit. The proposal unit proposes volunteer activities based on the support matched by the matching unit. The distribution unit distributes relief supplies based on the support proposed by the proposal unit. The priority proposal unit proposes priorities for infrastructure development based on the support proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide appropriate support based on the needs of disaster victims. [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 system according to an embodiment of the present invention is a system for providing support tailored to the needs of disaster victims. This system collects the needs of disaster victims in real time and matches them with appropriate support. For example, the disaster relief system allows disaster victims to quickly receive the support they truly need. The disaster relief system also analyzes SNS and security camera footage to grasp the situation in the disaster area in real time, enabling the sharing of accurate information. Furthermore, the disaster relief system proposes optimal activities based on the skills and experience of volunteers, realizing the effective deployment of volunteers. This improves the efficiency of volunteer activities. Next, the disaster relief system appropriately distributes necessary relief supplies based on the needs of disaster victims. This prevents shortages or surpluses of supplies and realizes efficient support. The disaster relief system also analyzes the situation in the disaster area and proposes priorities for infrastructure development, supporting effective recovery work. Specifically, the first step is for the disaster relief system to collect the needs of disaster victims and match them with appropriate support. Next, the disaster relief system analyzes SNS and security camera footage to grasp the situation in the disaster area in real time. Furthermore, the disaster relief system proposes optimal activities based on the skills and experience of volunteers, realizing the effective deployment of volunteers. Finally, the disaster relief system appropriately distributes necessary relief supplies based on the needs of the victims and proposes priorities for infrastructure development. This mechanism enables support tailored to the needs of the victims, improving the efficiency of volunteer activities and the accuracy of information. Furthermore, effective support is achieved through the distribution of relief supplies and the proposal of priorities for infrastructure development. In this way, the disaster relief system can provide support tailored to the needs of the victims and improve the efficiency of volunteer activities and the accuracy of information.

[0029] The disaster relief system according to this embodiment comprises a collection unit, a matching unit, a proposal unit, a distribution unit, and a priority proposal unit. The collection unit collects the needs of disaster victims. For example, the collection unit collects needs such as food, water, medical assistance, and shelters that disaster victims require. The collection unit can also analyze SNS and security camera footage to grasp the situation in the disaster area in real time. For example, the collection unit analyzes SNS posts to extract the needs of disaster victims. The collection unit can also analyze security camera footage to grasp the situation in the disaster area. The collection unit can use AI to collect the needs of disaster victims in real time. The matching unit matches appropriate support based on the needs collected by the collection unit. For example, the matching unit quickly matches the support that disaster victims need. The matching unit can use AI to match appropriate support based on the needs of disaster victims. The proposal unit proposes volunteer activities based on the support matched by the matching unit. For example, the proposal unit proposes optimal activities based on the skills and experience of volunteers. The proposal unit can use AI to propose optimal activities based on the skills and experience of volunteers. The distribution unit distributes relief supplies based on the support proposed by the proposal unit. The distribution unit appropriately distributes necessary relief supplies based on the needs of disaster victims, for example. The distribution unit can use AI to appropriately distribute necessary relief supplies based on the needs of disaster victims. The priority proposal unit proposes priorities for infrastructure development based on the support proposed by the proposal unit. The priority proposal unit can analyze the situation in the disaster area and propose priorities for infrastructure development, for example. The priority proposal unit can use AI to analyze the situation in the disaster area and propose priorities for infrastructure development. As a result, the disaster relief system according to this embodiment can provide support that is tailored to the needs of disaster victims and improve the efficiency of volunteer activities and the accuracy of information.

[0030] The information collection unit collects the needs of disaster victims. For example, it collects information on the needs of disaster victims such as food, water, medical assistance, and shelters. Specifically, it not only collects information entered by disaster victims via smartphones and computers, but can also analyze social media and security camera footage to grasp the situation in the disaster area in real time. For example, the information collection unit analyzes social media posts to extract the needs of disaster victims. Social media posts often describe the current situation and the support that disaster victims need, and by analyzing this using natural language processing technology, specific needs can be extracted. The information collection unit can also analyze security camera footage to grasp the situation in the disaster area. Image recognition technology is used to analyze security camera footage to detect the situation in the disaster area and the movement of people. For example, it can grasp in real time the number of collapsed buildings, flooded roads, and people who have evacuated. The information collection unit can use AI to collect the needs of disaster victims in real time. The AI ​​quickly analyzes the collected data and classifies and organizes the needs of disaster victims. This allows the data collection unit to quickly and accurately understand the needs of disaster victims and use this information to facilitate the next step of matching them with appropriate support. Furthermore, the data collection unit can centrally manage the collected data and integrate it with other departments and systems. For example, the collected data can be stored on a cloud server and made accessible to the matching and proposal units. By adjusting the frequency and accuracy of data collection, flexible responses can be made to specific situations and conditions. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The matching department matches appropriate support based on the needs collected by the collection department. Specifically, it uses AI to analyze data and identify the optimal support in order to quickly match the support that disaster victims need. For example, if a disaster victim needs food, the matching department will work with local food suppliers and volunteer organizations to arrange for the rapid provision of food. If medical assistance is needed, it will work with nearby medical institutions and medical volunteers to arrange for the necessary medical supplies and medical staff. The matching department can use AI to match appropriate support based on the needs of disaster victims. The AI ​​analyzes the collected data and classifies and organizes the needs of disaster victims. For example, it identifies the optimal support by considering the location information of disaster victims and the urgency of their needs. In addition, the AI ​​can use past data and statistical information to predict the effectiveness of support and propose the optimal support method. This allows the matching department to respond quickly and accurately to the needs of disaster victims and provide appropriate support. Furthermore, the matching department can monitor the progress of support in real time and adjust the support content as needed. For example, it can understand the inventory status of support supplies and the activity status of volunteers and review the priorities of support. This allows the matching department to provide support efficiently and effectively, and to respond quickly to the needs of disaster victims.

[0032] The Proposal Department proposes volunteer activities based on the support matched by the Matching Department. Specifically, it uses AI to analyze data to propose the most suitable activities based on the volunteers' skills and experience. For example, it proposes medical support activities to volunteers with medical qualifications and the construction and repair of evacuation centers to volunteers with construction skills. The Proposal Department can use AI to propose the most suitable activities based on the volunteers' skills and experience. The AI ​​analyzes the volunteers' registration information and past activity history to identify the most suitable activities. For example, it proposes the most suitable activities considering the volunteers' skill sets, years of experience, and past activity results. In addition, the AI ​​can analyze the situation and needs of the disaster area in real time and adjust the volunteer activities accordingly. This allows the Proposal Department to maximize the use of volunteers' skills and experience and propose efficient and effective support activities. Furthermore, the Proposal Department can monitor the volunteers' activity status in real time and adjust the activities as needed. For example, it can grasp the progress of volunteer activities and the situation in the disaster area and review the priorities of activities. This allows the Proposal Department to efficiently manage volunteer activities and respond quickly to the needs of disaster victims.

[0033] The distribution department distributes relief supplies based on the support proposed by the proposal department. Specifically, it uses AI to analyze data in order to appropriately distribute necessary relief supplies based on the needs of disaster victims. For example, it distributes food, water, medical supplies, and materials for setting up shelters according to the needs of disaster victims. The distribution department can use AI to appropriately distribute necessary relief supplies based on the needs of disaster victims. The AI ​​analyzes the collected data and classifies and organizes the needs of disaster victims. For example, it identifies the most suitable relief supplies by considering the location information of disaster victims and the urgency of their needs. The AI ​​can also analyze the inventory status and supply routes of relief supplies and formulate an efficient distribution plan. This allows the distribution department to respond quickly and accurately to the needs of disaster victims and provide appropriate relief supplies. Furthermore, the distribution department can monitor the distribution status of relief supplies in real time and adjust the distribution plan as needed. For example, it can understand the inventory status of relief supplies and the situation in the disaster area and review the distribution priorities. This allows the distribution department to distribute relief supplies efficiently and effectively and respond quickly to the needs of disaster victims.

[0034] The Priority Proposal Department proposes priorities for infrastructure development based on the support proposed by the Proposal Department. Specifically, it analyzes data using AI to analyze the situation in disaster-stricken areas and propose priorities for infrastructure development. For example, it proposes priorities according to the situation in the disaster-stricken area, such as road and bridge repair, restoration of electricity and water supply, and development of communication infrastructure. The Priority Proposal Department can use AI to analyze the situation in disaster-stricken areas and propose priorities for infrastructure development. The AI ​​analyzes the collected data and classifies and organizes the situation in the disaster-stricken areas. For example, it identifies the optimal priority for infrastructure development by considering the extent of damage and the urgency of recovery in the disaster-stricken areas. In addition, the AI ​​can use historical data and statistical information to predict the effectiveness of infrastructure development and propose the optimal development plan. This allows the Priority Proposal Department to respond quickly and accurately to the situation in disaster-stricken areas and propose appropriate infrastructure development. Furthermore, the Priority Proposal Department can monitor the progress of infrastructure development in real time and adjust the development plan as needed. For example, it can grasp the progress of development and the situation in the disaster-stricken areas and review the development priorities. This allows the Priority Proposal Department to propose infrastructure development efficiently and effectively and support the rapid recovery of disaster-stricken areas.

[0035] The data collection unit can analyze social media and security camera footage to grasp the situation in disaster-stricken areas in real time. For example, the data collection unit can analyze social media posts and extract the needs of disaster victims. The data collection unit can use AI to analyze social media posts and extract the needs of disaster victims. The data collection unit can also analyze security camera footage to grasp the situation in disaster-stricken areas. The data collection unit can use AI to analyze security camera footage and grasp the situation in disaster-stricken areas. This allows for the provision of accurate information by grasping the situation in disaster-stricken areas in real time. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media posts into a generating AI and have the generating AI perform the extraction of the needs of disaster victims.

[0036] The proposal department can suggest the most suitable activities based on the volunteer's skills and experience. For example, the proposal department can suggest medical support activities based on the volunteer's medical qualifications. The proposal department can use AI to suggest medical support activities based on the volunteer's medical qualifications. The proposal department can also suggest infrastructure development activities based on the volunteer's construction experience. The proposal department can use AI to suggest infrastructure development activities based on the volunteer's construction experience. The proposal department can also suggest educational support activities based on the volunteer's educational experience. The proposal department can use AI to suggest educational support activities based on the volunteer's educational experience. This improves the efficiency of volunteer activities by suggesting the most suitable activities based on the volunteer's skills and experience. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input the volunteer's skills and experience into a generating AI and have the generating AI execute suggestions for the most suitable activities.

[0037] The distribution unit can appropriately distribute necessary relief supplies based on the needs of disaster victims. For example, the distribution unit can distribute food and water based on the needs of disaster victims. The distribution unit can use AI to distribute food and water based on the needs of disaster victims. The distribution unit can also distribute medical relief supplies based on the needs of disaster victims. The distribution unit can use AI to distribute medical relief supplies based on the needs of disaster victims. The distribution unit can also provide shelters based on the needs of disaster victims. The distribution unit can use AI to provide shelters based on the needs of disaster victims. This prevents shortages or surpluses of supplies by appropriately distributing relief supplies based on the needs of disaster victims. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input the needs of disaster victims into a generating AI and have the generating AI distribute the necessary relief supplies.

[0038] The priority proposal unit can analyze the situation in the disaster-stricken area and propose priorities for infrastructure development. For example, the priority proposal unit can analyze the damage to roads in the disaster-stricken area and propose road repair as a priority. The priority proposal unit can use AI to analyze the damage to roads in the disaster-stricken area and propose road repair as a priority. The priority proposal unit can also propose the restoration of power supply in the disaster-stricken area as a priority. The priority proposal unit can use AI to propose the restoration of power supply in the disaster-stricken area as a priority. The priority proposal unit can also propose the repair of water supply in the disaster-stricken area as a priority. The priority proposal unit can use AI to propose the repair of water supply in the disaster-stricken area as a priority. In this way, by analyzing the situation in the disaster-stricken area and proposing priorities for infrastructure development, it is possible to support effective recovery work. Some or all of the above processing in the priority proposal unit may be performed using AI or not. For example, the priority proposal unit can input the situation in the disaster-stricken area into a generating AI and have the generating AI execute the proposal of infrastructure development priorities.

[0039] The data collection unit can analyze the victim's past needs submission history and select the optimal collection method. For example, the data collection unit can automatically suggest similar needs based on the needs previously submitted by the victim. The data collection unit can use AI to automatically suggest similar needs based on the needs previously submitted by the victim. Furthermore, the data collection unit can prioritize the collection of frequently submitted needs from the victim's past needs submission history. The data collection unit can use AI to prioritize the collection of frequently submitted needs from the victim's past needs submission history. Furthermore, the data collection unit can analyze the victim's past needs submission history and select the optimal question format. The data collection unit can use AI to analyze the victim's past needs submission history and select the optimal question format. This allows for the selection of the optimal collection method by analyzing the victim's past needs submission history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the victim's past needs submission history into a generating AI and have the generating AI select the optimal collection method.

[0040] The data collection unit can filter the collected needs based on the disaster victim's current living situation and areas of interest. For example, the data collection unit can prioritize collecting necessary support, taking into account the disaster victim's current living situation. The data collection unit can use AI to prioritize collecting necessary support, taking into account the disaster victim's current living situation. The data collection unit can also collect relevant needs based on the disaster victim's areas of interest. The data collection unit can use AI to collect relevant needs based on the disaster victim's areas of interest. The data collection unit can also collect needs by omitting unnecessary questions based on the disaster victim's living situation and areas of interest. The data collection unit can use AI to collect needs by omitting unnecessary questions based on the disaster victim's living situation and areas of interest. This allows for the collection of needs by omitting unnecessary questions through filtering based on the disaster victim's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the disaster victim's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant needs by considering the geographical location information of disaster victims when collecting needs. For example, the data collection unit can prioritize the collection of support needs in the vicinity based on the current location of disaster victims. The data collection unit can use AI to prioritize the collection of support needs in the vicinity based on the current location of disaster victims. The data collection unit can also collect region-specific needs by considering the geographical location information of disaster victims. The data collection unit can use AI to collect region-specific needs by considering the geographical location information of disaster victims. The data collection unit can also prioritize the collection of needs at the nearest support base based on the location information of disaster victims. The data collection unit can use AI to prioritize the collection of needs at the nearest support base based on the location information of disaster victims. This allows for the priority collection of region-specific needs by considering the geographical location information of disaster victims. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the geographical location information of disaster victims into a generating AI and have the generating AI perform the collection of highly relevant needs.

[0042] The data collection unit can analyze the social media activities of disaster victims and collect relevant needs when collecting needs. For example, the data collection unit can analyze the social media posts of disaster victims and collect urgent needs. The data collection unit can use AI to analyze the social media posts of disaster victims and collect urgent needs. The data collection unit can also collect support that is of interest to disaster victims from their social media activities. The data collection unit can use AI to collect support that is of interest to disaster victims from their social media activities. The data collection unit can also collect relevant needs by referring to posts from the social media followers and friends of disaster victims. The data collection unit can use AI to refer to posts from the social media followers and friends of disaster victims and collect relevant needs. This allows for the efficient collection of relevant needs by analyzing the social media activities of disaster victims. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the social media activities of disaster victims into a generating AI and have the generating AI collect relevant needs.

[0043] The matching unit can adjust the level of detail in the matching process based on the importance of the support. For example, the matching unit can prioritize matching support with a high level of importance. The matching unit can use AI to prioritize matching support with a high level of importance. The matching unit can also provide detailed information when matching support with a low level of importance. The matching unit can use AI to provide detailed information when matching support with a low level of importance. The matching unit can also adjust the priority of the matching process according to the importance of the support. The matching unit can use AI to adjust the priority of the matching process according to the importance of the support. This allows important support to be provided preferentially by adjusting the level of detail in the matching process based on the importance of the support. Some or all of the above processes in the matching unit may be performed using AI or not. For example, the matching unit can input the importance of the support into a generating AI and have the generating AI perform the adjustment of the level of detail in the matching process.

[0044] The matching unit can apply different matching algorithms depending on the category of support during the matching process. For example, in the case of medical support, the matching unit can apply a specialized matching algorithm. The matching unit can use AI to apply a specialized matching algorithm in the case of medical support. The matching unit can also apply an efficient matching algorithm in the case of material support. The matching unit can use AI to apply an efficient matching algorithm in the case of material support. The matching unit can also apply a skill-based matching algorithm in the case of volunteer support. The matching unit can use AI to apply a skill-based matching algorithm in the case of volunteer support. By applying different matching algorithms depending on the category of support, more effective support can be provided. Some or all of the above processing in the matching unit may be performed using AI or not. For example, the matching unit can input the category of support into a generating AI and have the generating AI execute the application of different matching algorithms.

[0045] The matching unit can determine the priority of matching based on the timing of support provision during the matching process. For example, the matching unit can prioritize matching support that is urgently needed. The matching unit can use AI to prioritize matching support that is urgently needed. The matching unit can also postpone support that can be provided later. The matching unit can use AI to postpone support that can be provided later. The matching unit can also adjust the priority of matching according to the timing of support provision. The matching unit can use AI to adjust the priority of matching according to the timing of support provision. This allows for the priority of matching based on the timing of support provision, thereby prioritizing the provision of support that is urgently needed. Some or all of the above-described processes in the matching unit may be performed using AI or not. For example, the matching unit can input the timing of support provision into a generating AI and have the generating AI determine the priority of matching.

[0046] The matching unit can adjust the order of matching based on the relevance of the support during the matching process. For example, the matching unit prioritizes matching the support most relevant to the needs of the disaster victim. The matching unit can use AI to prioritize matching the support most relevant to the needs of the disaster victim. The matching unit can also postpone less relevant support. The matching unit can use AI to postpone less relevant support. The matching unit can also adjust the order of matching according to the relevance of the support. The matching unit can use AI to adjust the order of matching according to the relevance of the support. By adjusting the order of matching based on the relevance of the support, the matching unit can prioritize providing the support most relevant to the needs of the disaster victim. Some or all of the above processing in the matching unit may be performed using AI or not. For example, the matching unit can input the relevance of the support into a generating AI and have the generating AI perform the adjustment of the matching order.

[0047] The proposal department can suggest the most suitable activities based on the volunteer's skills and experience. For example, the proposal department can suggest medical support activities based on the volunteer's medical skills. The proposal department can use AI to suggest medical support activities based on the volunteer's medical skills. The proposal department can also suggest infrastructure development activities based on the volunteer's construction experience. The proposal department can use AI to suggest infrastructure development activities based on the volunteer's construction experience. The proposal department can also suggest educational support activities based on the volunteer's educational experience. The proposal department can use AI to suggest educational support activities based on the volunteer's educational experience. This improves the efficiency of volunteer activities by suggesting the most suitable activities based on the volunteer's skills and experience. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input the volunteer's skills and experience into a generating AI and have the generating AI execute a proposal for the most suitable activity.

[0048] The proposal unit can make optimal suggestions by referring to the volunteer's past activity history when making a suggestion. For example, the proposal unit can suggest similar activities based on the volunteer's past activity history. The proposal unit can use AI to suggest similar activities based on the volunteer's past activity history. The proposal unit can also suggest the most effective activities from the volunteer's past activity history. The proposal unit can use AI to suggest the most effective activities from the volunteer's past activity history. The proposal unit can also analyze the volunteer's past activity history and suggest the optimal activities. The proposal unit can use AI to analyze the volunteer's past activity history and suggest the optimal activities. This allows the proposal unit to suggest the optimal activities by referring to the volunteer's past activity history. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the volunteer's past activity history into a generation AI and have the generation AI execute the optimal suggestion.

[0049] The proposal unit can propose the most suitable activities by considering the volunteer's geographical location information when making a proposal. For example, the proposal unit can propose nearby support activities based on the volunteer's current location. The proposal unit can use AI to propose nearby support activities based on the volunteer's current location. The proposal unit can also consider the volunteer's geographical location information and propose support activities specific to the region. The proposal unit can use AI to consider the volunteer's geographical location information and propose support activities specific to the region. The proposal unit can also propose activities at the nearest support base based on the volunteer's location information. The proposal unit can use AI to propose activities at the nearest support base based on the volunteer's location information. This allows for the proposal of region-specific support activities by considering the volunteer's geographical location information. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the volunteer's geographical location information into a generating AI and have the generating AI execute a proposal for the most suitable activities.

[0050] The proposal unit can analyze a volunteer's social media activity and suggest relevant activities when making a proposal. For example, the proposal unit can analyze a volunteer's social media posts and suggest relevant support activities. The proposal unit can use AI to analyze a volunteer's social media posts and suggest relevant support activities. The proposal unit can also suggest support activities of interest based on the volunteer's social media activity. The proposal unit can use AI to suggest support activities of interest based on the volunteer's social media activity. The proposal unit can also suggest relevant support activities by referring to posts from the volunteer's social media followers and friends. The proposal unit can use AI to refer to posts from the volunteer's social media followers and friends and suggest relevant support activities. In this way, relevant support activities can be suggested by analyzing the volunteer's social media activity. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the volunteer's social media activity into a generating AI and have the generating AI execute suggestions for relevant activities.

[0051] The distribution unit can select the optimal distribution method by referring to the disaster victim's past history of receiving relief supplies. For example, the distribution unit can prioritize the distribution of necessary supplies based on the disaster victim's past history of receiving relief supplies. The distribution unit can use AI to prioritize the distribution of necessary supplies based on the disaster victim's past history of receiving relief supplies. The distribution unit can also ensure a balanced distribution without surplus or shortage based on the disaster victim's past history of receiving relief supplies. The distribution unit can use AI to ensure a balanced distribution without surplus or shortage based on the disaster victim's past history of receiving relief supplies. The distribution unit can also analyze the disaster victim's past history of receiving relief supplies and select the optimal distribution method. The distribution unit can use AI to analyze the disaster victim's past history of receiving relief supplies and select the optimal distribution method. This allows for a balanced distribution without surplus or shortage by referring to the disaster victim's past history of receiving relief supplies. Some or all of the above processing in the distribution unit may be performed using AI or without AI. For example, the distribution unit can input the disaster victim's past history of receiving relief supplies into a generating AI, and have the AI ​​select the optimal distribution method.

[0052] The distribution unit can customize the distribution of relief supplies based on the current living conditions of disaster victims. For example, the distribution unit can consider the current living conditions of disaster victims and prioritize the distribution of necessary supplies. The distribution unit can use AI to consider the current living conditions of disaster victims and prioritize the distribution of necessary supplies. The distribution unit can also customize and distribute specific supplies based on the living conditions of disaster victims. The distribution unit can use AI to customize and distribute specific supplies based on the living conditions of disaster victims. The distribution unit can also omit unnecessary supplies based on the living conditions of disaster victims. The distribution unit can use AI to omit unnecessary supplies based on the living conditions of disaster victims. By customizing relief supplies based on the living conditions of disaster victims, more appropriate supplies can be provided. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input the living conditions of disaster victims into a generating AI and have the generating AI perform the customization of relief supplies.

[0053] The distribution unit can select the optimal distribution method by considering the geographical location information of the disaster victims. For example, the distribution unit can prioritize the distribution of nearby relief supplies based on the disaster victims' current location. The distribution unit can use AI to prioritize the distribution of nearby relief supplies based on the disaster victims' current location. The distribution unit can also distribute region-specific supplies by considering the geographical location information of the disaster victims. The distribution unit can use AI to distribute region-specific supplies by considering the geographical location information of the disaster victims. The distribution unit can also prioritize the distribution of supplies from the nearest support base based on the location information of the disaster victims. The distribution unit can use AI to prioritize the distribution of supplies from the nearest support base based on the location information of the disaster victims. This allows for the priority provision of region-specific supplies by considering the geographical location information of the disaster victims. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input the geographical location information of the disaster victims into a generating AI and have the generating AI select the optimal distribution method.

[0054] The distribution unit can analyze the social media activity of disaster victims and distribute relevant relief supplies at the time of distribution. For example, the distribution unit can analyze the social media posts of disaster victims and distribute supplies with high urgency. The distribution unit can use AI to analyze the social media posts of disaster victims and distribute supplies with high urgency. The distribution unit can also distribute supplies of interest to disaster victims based on their social media activity. The distribution unit can use AI to distribute supplies of interest to disaster victims based on their social media activity. The distribution unit can also distribute relevant supplies by referring to posts from the disaster victims' social media followers and friends. The distribution unit can use AI to refer to posts from the disaster victims' social media followers and friends and distribute relevant supplies. This allows for the efficient provision of relevant supplies by analyzing the social media activity of disaster victims. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input the social media activity of disaster victims into a generating AI and have the generating AI execute the distribution of relevant relief supplies.

[0055] The priority proposal unit can propose the optimal priority by referring to the past infrastructure development history of the disaster-stricken area when proposing priorities. For example, the priority proposal unit can propose necessary developments on a priority basis based on the past infrastructure development history of the disaster-stricken area. The priority proposal unit can use AI to propose necessary developments on a priority basis based on the past infrastructure development history of the disaster-stricken area. The priority proposal unit can also propose developments that are neither excessive nor insufficient based on the past infrastructure development history of the disaster-stricken area. The priority proposal unit can use AI to propose developments that are neither excessive nor insufficient based on the past infrastructure development history of the disaster-stricken area. The priority proposal unit can also analyze the past infrastructure development history of the disaster-stricken area and propose the optimal priority. The priority proposal unit can use AI to analyze the past infrastructure development history of the disaster-stricken area and propose the optimal priority. This allows for the proposal of developments that are neither excessive nor insufficient by referring to the past infrastructure development history of the disaster-stricken area. Some or all of the above processing in the priority proposal unit may be performed using AI or not. For example, the priority proposal unit can input the past infrastructure development history of the disaster-stricken area into a generating AI and have the generating AI execute the proposal of the optimal priority.

[0056] The priority proposal unit can customize the priority of infrastructure development based on the current situation in the disaster-stricken area when proposing priorities. For example, the priority proposal unit can consider the current situation in the disaster-stricken area and propose necessary developments on a priority basis. The priority proposal unit can use AI to consider the current situation in the disaster-stricken area and propose necessary developments on a priority basis. The priority proposal unit can also customize and propose specific developments based on the situation in the disaster-stricken area. The priority proposal unit can use AI to customize and propose specific developments based on the situation in the disaster-stricken area. The priority proposal unit can also omit unnecessary developments based on the situation in the disaster-stricken area. The priority proposal unit can use AI to omit unnecessary developments based on the situation in the disaster-stricken area. By customizing the priority of infrastructure development based on the current situation in the disaster-stricken area, more appropriate developments can be proposed. Some or all of the above processing in the priority proposal unit may be performed using AI or not. For example, the priority proposal unit can input the current situation in the disaster-stricken area into a generating AI and have the generating AI perform the customization of infrastructure development priorities.

[0057] The priority proposal unit can propose the optimal priority when proposing priorities, taking into account the geographical location information of the disaster-stricken area. For example, the priority proposal unit can propose infrastructure development in the vicinity of the disaster-stricken area as a priority based on the current location of the disaster-stricken area. The priority proposal unit can use AI to propose infrastructure development in the vicinity of the disaster-stricken area as a priority based on the current location of the disaster-stricken area. Furthermore, the priority proposal unit can also propose development specific to the region, taking into account the geographical location information of the disaster-stricken area. The priority proposal unit can use AI to propose development specific to the region, taking into account the geographical location information of the disaster-stricken area. Furthermore, the priority proposal unit can also propose development at the nearest development base as a priority based on the location information of the disaster-stricken area. The priority proposal unit can use AI to propose development at the nearest development base as a priority based on the location information of the disaster-stricken area. In this way, by taking into account the geographical location information of the disaster-stricken area, development specific to the region can be proposed as a priority. Some or all of the above processing in the priority proposal unit may be performed using AI or not. For example, the priority proposal unit can input the geographical location information of the disaster-stricken area into a generating AI and have the generating AI execute the proposal of the optimal priority.

[0058] The priority proposal unit can analyze social media activity in disaster-stricken areas and propose priorities for relevant infrastructure development when proposing priorities. For example, the priority proposal unit can analyze social media posts in disaster-stricken areas and propose developments with high urgency. The priority proposal unit can use AI to analyze social media posts in disaster-stricken areas and propose developments with high urgency. The priority proposal unit can also propose developments of interest based on social media activity in disaster-stricken areas. The priority proposal unit can use AI to propose developments of interest based on social media activity in disaster-stricken areas. The priority proposal unit can also propose relevant developments by referring to posts from followers and friends on social media in disaster-stricken areas. The priority proposal unit can use AI to refer to posts from followers and friends on social media in disaster-stricken areas and propose relevant developments. This allows for the efficient proposal of relevant developments by analyzing social media activity in disaster-stricken areas. Some or all of the above processing in the priority proposal unit may be performed using AI or not. For example, the priority proposal unit can input social media activity in disaster-stricken areas into a generating AI and have the generating AI execute a proposal for the priority of relevant infrastructure development.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] A disaster relief system can include a health monitoring unit in addition to a needs collection unit that gathers the needs of disaster victims. The health monitoring unit can monitor vital signs such as heart rate, body temperature, and blood pressure of disaster victims in real time, and can provide medical assistance quickly if abnormalities are detected. For example, while the needs collection unit gathers the needs of disaster victims, the health monitoring unit monitors the victims' heart rate and prioritizes matching them with medical assistance if abnormalities are found. The health monitoring unit also monitors the victims' body temperature and can quickly provide medical supplies if a fever is detected. Furthermore, the health monitoring unit monitors the victims' blood pressure and can arrange for a medical examination by a medical professional if high blood pressure is detected. This allows for real-time monitoring of the health status of disaster victims and the rapid provision of appropriate medical assistance.

[0061] A disaster relief system can include an environmental monitoring unit in addition to a data collection unit that gathers the needs of disaster victims, to monitor the living environment of those victims. The environmental monitoring unit can monitor environmental data such as temperature, humidity, and air quality in the disaster area in real time and provide support to improve the living environment of disaster victims. For example, while the data collection unit gathers the needs of disaster victims, the environmental monitoring unit monitors the temperature in the disaster area and provides appropriate shelters if extreme temperature changes are detected. The environmental monitoring unit also monitors the humidity in the disaster area and can provide dehumidifiers or desiccants if the humidity is high. Furthermore, the environmental monitoring unit monitors the air quality in the disaster area and can provide air purifiers if air pollution is detected. This allows for a real-time understanding of the living environment of disaster victims and the rapid provision of appropriate support.

[0062] A disaster relief system can include a data collection unit to gather the needs of disaster victims, as well as a movement tracking unit to track their movements. The movement tracking unit can track the location of disaster victims in real time and provide support as needed. For example, while the data collection unit gathers the needs of disaster victims, the movement tracking unit monitors their location and provides information about evacuation shelters if their movement to a shelter is confirmed. The movement tracking unit can also guide disaster victims to the nearest support center if they need assistance while moving. Furthermore, the movement tracking unit can analyze the movement history of disaster victims and suggest optimized evacuation routes. This allows for real-time monitoring of disaster victims' movements and the rapid provision of appropriate support.

[0063] A disaster relief system can include a communication support unit in addition to a data collection unit that gathers the needs of disaster victims. The communication support unit can provide support to facilitate communication between disaster victims and aid workers, and among disaster victims themselves. For example, while the data collection unit gathers the needs of disaster victims, the communication support unit supports information sharing between disaster victims and aid workers. The communication support unit can also provide means of communication among disaster victims, enabling them to help each other. Furthermore, the communication support unit can take into account the language and cultural differences of disaster victims and provide multilingual communication tools. This can facilitate communication among disaster victims and enhance the effectiveness of the support.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The collection unit gathers information on the needs of disaster victims. For example, it collects information on the food, water, medical assistance, and shelters that victims need. The collection unit can also analyze social media and security camera footage to understand the situation in the disaster area in real time. AI can be used to collect information on the needs of disaster victims in real time. Step 2: The matching unit matches appropriate support based on the needs collected by the collection unit. For example, it quickly matches the support that disaster victims need. Using AI, it is possible to match appropriate support based on the needs of disaster victims. Step 3: The proposal department proposes volunteer activities based on the support matched by the matching department. For example, it proposes the most suitable activities based on the volunteer's skills and experience. AI can be used to propose the most suitable activities based on the volunteer's skills and experience. Step 4: The distribution unit distributes relief supplies based on the support proposed by the proposal unit. For example, it appropriately distributes necessary relief supplies based on the needs of the disaster victims. Using AI, it is possible to appropriately distribute necessary relief supplies based on the needs of the disaster victims. Step 5: The Prioritization Proposal Department proposes priorities for infrastructure development based on the support proposed by the Proposal Department. For example, it analyzes the situation in disaster-stricken areas and proposes priorities for infrastructure development. AI can be used to analyze the situation in disaster-stricken areas and propose priorities for infrastructure development.

[0066] (Example of form 2) The disaster relief system according to an embodiment of the present invention is a system for providing support tailored to the needs of disaster victims. This system collects the needs of disaster victims in real time and matches them with appropriate support. For example, the disaster relief system allows disaster victims to quickly receive the support they truly need. The disaster relief system also analyzes SNS and security camera footage to grasp the situation in the disaster area in real time, enabling the sharing of accurate information. Furthermore, the disaster relief system proposes optimal activities based on the skills and experience of volunteers, realizing the effective deployment of volunteers. This improves the efficiency of volunteer activities. Next, the disaster relief system appropriately distributes necessary relief supplies based on the needs of disaster victims. This prevents shortages or surpluses of supplies and realizes efficient support. The disaster relief system also analyzes the situation in the disaster area and proposes priorities for infrastructure development, supporting effective recovery work. Specifically, the first step is for the disaster relief system to collect the needs of disaster victims and match them with appropriate support. Next, the disaster relief system analyzes SNS and security camera footage to grasp the situation in the disaster area in real time. Furthermore, the disaster relief system proposes optimal activities based on the skills and experience of volunteers, realizing the effective deployment of volunteers. Finally, the disaster relief system appropriately distributes necessary relief supplies based on the needs of the victims and proposes priorities for infrastructure development. This mechanism enables support tailored to the needs of the victims, improving the efficiency of volunteer activities and the accuracy of information. Furthermore, effective support is achieved through the distribution of relief supplies and the proposal of priorities for infrastructure development. In this way, the disaster relief system can provide support tailored to the needs of the victims and improve the efficiency of volunteer activities and the accuracy of information.

[0067] The disaster relief system according to this embodiment comprises a collection unit, a matching unit, a proposal unit, a distribution unit, and a priority proposal unit. The collection unit collects the needs of disaster victims. For example, the collection unit collects needs such as food, water, medical assistance, and shelters that disaster victims require. The collection unit can also analyze SNS and security camera footage to grasp the situation in the disaster area in real time. For example, the collection unit analyzes SNS posts to extract the needs of disaster victims. The collection unit can also analyze security camera footage to grasp the situation in the disaster area. The collection unit can use AI to collect the needs of disaster victims in real time. The matching unit matches appropriate support based on the needs collected by the collection unit. For example, the matching unit quickly matches the support that disaster victims need. The matching unit can use AI to match appropriate support based on the needs of disaster victims. The proposal unit proposes volunteer activities based on the support matched by the matching unit. For example, the proposal unit proposes optimal activities based on the skills and experience of volunteers. The proposal unit can use AI to propose optimal activities based on the skills and experience of volunteers. The distribution unit distributes relief supplies based on the support proposed by the proposal unit. The distribution unit appropriately distributes necessary relief supplies based on the needs of disaster victims, for example. The distribution unit can use AI to appropriately distribute necessary relief supplies based on the needs of disaster victims. The priority proposal unit proposes priorities for infrastructure development based on the support proposed by the proposal unit. The priority proposal unit can analyze the situation in the disaster area and propose priorities for infrastructure development, for example. The priority proposal unit can use AI to analyze the situation in the disaster area and propose priorities for infrastructure development. As a result, the disaster relief system according to this embodiment can provide support that is tailored to the needs of disaster victims and improve the efficiency of volunteer activities and the accuracy of information.

[0068] The information collection unit collects the needs of disaster victims. For example, it collects information on the needs of disaster victims such as food, water, medical assistance, and shelters. Specifically, it not only collects information entered by disaster victims via smartphones and computers, but can also analyze social media and security camera footage to grasp the situation in the disaster area in real time. For example, the information collection unit analyzes social media posts to extract the needs of disaster victims. Social media posts often describe the current situation and the support that disaster victims need, and by analyzing this using natural language processing technology, specific needs can be extracted. The information collection unit can also analyze security camera footage to grasp the situation in the disaster area. Image recognition technology is used to analyze security camera footage to detect the situation in the disaster area and the movement of people. For example, it can grasp in real time the number of collapsed buildings, flooded roads, and people who have evacuated. The information collection unit can use AI to collect the needs of disaster victims in real time. The AI ​​quickly analyzes the collected data and classifies and organizes the needs of disaster victims. This allows the data collection unit to quickly and accurately understand the needs of disaster victims and use this information to facilitate the next step of matching them with appropriate support. Furthermore, the data collection unit can centrally manage the collected data and integrate it with other departments and systems. For example, the collected data can be stored on a cloud server and made accessible to the matching and proposal units. By adjusting the frequency and accuracy of data collection, flexible responses can be made to specific situations and conditions. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0069] The matching department matches appropriate support based on the needs collected by the collection department. Specifically, it uses AI to analyze data and identify the optimal support in order to quickly match the support that disaster victims need. For example, if a disaster victim needs food, the matching department will work with local food suppliers and volunteer organizations to arrange for the rapid provision of food. If medical assistance is needed, it will work with nearby medical institutions and medical volunteers to arrange for the necessary medical supplies and medical staff. The matching department can use AI to match appropriate support based on the needs of disaster victims. The AI ​​analyzes the collected data and classifies and organizes the needs of disaster victims. For example, it identifies the optimal support by considering the location information of disaster victims and the urgency of their needs. In addition, the AI ​​can use past data and statistical information to predict the effectiveness of support and propose the optimal support method. This allows the matching department to respond quickly and accurately to the needs of disaster victims and provide appropriate support. Furthermore, the matching department can monitor the progress of support in real time and adjust the support content as needed. For example, it can understand the inventory status of support supplies and the activity status of volunteers and review the priorities of support. This allows the matching department to provide support efficiently and effectively, and to respond quickly to the needs of disaster victims.

[0070] The Proposal Department proposes volunteer activities based on the support matched by the Matching Department. Specifically, it uses AI to analyze data to propose the most suitable activities based on the volunteers' skills and experience. For example, it proposes medical support activities to volunteers with medical qualifications and the construction and repair of evacuation centers to volunteers with construction skills. The Proposal Department can use AI to propose the most suitable activities based on the volunteers' skills and experience. The AI ​​analyzes the volunteers' registration information and past activity history to identify the most suitable activities. For example, it proposes the most suitable activities considering the volunteers' skill sets, years of experience, and past activity results. In addition, the AI ​​can analyze the situation and needs of the disaster area in real time and adjust the volunteer activities accordingly. This allows the Proposal Department to maximize the use of volunteers' skills and experience and propose efficient and effective support activities. Furthermore, the Proposal Department can monitor the volunteers' activity status in real time and adjust the activities as needed. For example, it can grasp the progress of volunteer activities and the situation in the disaster area and review the priorities of activities. This allows the Proposal Department to efficiently manage volunteer activities and respond quickly to the needs of disaster victims.

[0071] The distribution department distributes relief supplies based on the support proposed by the proposal department. Specifically, it uses AI to analyze data in order to appropriately distribute necessary relief supplies based on the needs of disaster victims. For example, it distributes food, water, medical supplies, and materials for setting up shelters according to the needs of disaster victims. The distribution department can use AI to appropriately distribute necessary relief supplies based on the needs of disaster victims. The AI ​​analyzes the collected data and classifies and organizes the needs of disaster victims. For example, it identifies the most suitable relief supplies by considering the location information of disaster victims and the urgency of their needs. The AI ​​can also analyze the inventory status and supply routes of relief supplies and formulate an efficient distribution plan. This allows the distribution department to respond quickly and accurately to the needs of disaster victims and provide appropriate relief supplies. Furthermore, the distribution department can monitor the distribution status of relief supplies in real time and adjust the distribution plan as needed. For example, it can understand the inventory status of relief supplies and the situation in the disaster area and review the distribution priorities. This allows the distribution department to distribute relief supplies efficiently and effectively and respond quickly to the needs of disaster victims.

[0072] The Priority Proposal Department proposes priorities for infrastructure development based on the support proposed by the Proposal Department. Specifically, it analyzes data using AI to analyze the situation in disaster-stricken areas and propose priorities for infrastructure development. For example, it proposes priorities according to the situation in the disaster-stricken area, such as road and bridge repair, restoration of electricity and water supply, and development of communication infrastructure. The Priority Proposal Department can use AI to analyze the situation in disaster-stricken areas and propose priorities for infrastructure development. The AI ​​analyzes the collected data and classifies and organizes the situation in the disaster-stricken areas. For example, it identifies the optimal priority for infrastructure development by considering the extent of damage and the urgency of recovery in the disaster-stricken areas. In addition, the AI ​​can use historical data and statistical information to predict the effectiveness of infrastructure development and propose the optimal development plan. This allows the Priority Proposal Department to respond quickly and accurately to the situation in disaster-stricken areas and propose appropriate infrastructure development. Furthermore, the Priority Proposal Department can monitor the progress of infrastructure development in real time and adjust the development plan as needed. For example, it can grasp the progress of development and the situation in the disaster-stricken areas and review the development priorities. This allows the Priority Proposal Department to propose infrastructure development efficiently and effectively and support the rapid recovery of disaster-stricken areas.

[0073] The data collection unit can analyze social media and security camera footage to grasp the situation in disaster-stricken areas in real time. For example, the data collection unit can analyze social media posts and extract the needs of disaster victims. The data collection unit can use AI to analyze social media posts and extract the needs of disaster victims. The data collection unit can also analyze security camera footage to grasp the situation in disaster-stricken areas. The data collection unit can use AI to analyze security camera footage and grasp the situation in disaster-stricken areas. This allows for the provision of accurate information by grasping the situation in disaster-stricken areas in real time. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media posts into a generating AI and have the generating AI perform the extraction of the needs of disaster victims.

[0074] The proposal department can suggest the most suitable activities based on the volunteer's skills and experience. For example, the proposal department can suggest medical support activities based on the volunteer's medical qualifications. The proposal department can use AI to suggest medical support activities based on the volunteer's medical qualifications. The proposal department can also suggest infrastructure development activities based on the volunteer's construction experience. The proposal department can use AI to suggest infrastructure development activities based on the volunteer's construction experience. The proposal department can also suggest educational support activities based on the volunteer's educational experience. The proposal department can use AI to suggest educational support activities based on the volunteer's educational experience. This improves the efficiency of volunteer activities by suggesting the most suitable activities based on the volunteer's skills and experience. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input the volunteer's skills and experience into a generating AI and have the generating AI execute suggestions for the most suitable activities.

[0075] The distribution unit can appropriately distribute necessary relief supplies based on the needs of disaster victims. For example, the distribution unit can distribute food and water based on the needs of disaster victims. The distribution unit can use AI to distribute food and water based on the needs of disaster victims. The distribution unit can also distribute medical relief supplies based on the needs of disaster victims. The distribution unit can use AI to distribute medical relief supplies based on the needs of disaster victims. The distribution unit can also provide shelters based on the needs of disaster victims. The distribution unit can use AI to provide shelters based on the needs of disaster victims. This prevents shortages or surpluses of supplies by appropriately distributing relief supplies based on the needs of disaster victims. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input the needs of disaster victims into a generating AI and have the generating AI distribute the necessary relief supplies.

[0076] The priority proposal unit can analyze the situation in the disaster-stricken area and propose priorities for infrastructure development. For example, the priority proposal unit can analyze the damage to roads in the disaster-stricken area and propose road repair as a priority. The priority proposal unit can use AI to analyze the damage to roads in the disaster-stricken area and propose road repair as a priority. The priority proposal unit can also propose the restoration of power supply in the disaster-stricken area as a priority. The priority proposal unit can use AI to propose the restoration of power supply in the disaster-stricken area as a priority. The priority proposal unit can also propose the repair of water supply in the disaster-stricken area as a priority. The priority proposal unit can use AI to propose the repair of water supply in the disaster-stricken area as a priority. In this way, by analyzing the situation in the disaster-stricken area and proposing priorities for infrastructure development, it is possible to support effective recovery work. Some or all of the above processing in the priority proposal unit may be performed using AI or not. For example, the priority proposal unit can input the situation in the disaster-stricken area into a generating AI and have the generating AI execute the proposal of infrastructure development priorities.

[0077] The data collection unit can estimate the emotions of disaster victims and adjust the method of collecting needs based on the estimated emotions. For example, if a disaster victim is feeling anxious, the data collection unit can collect needs in the form of simple and easy-to-understand questions. The data collection unit can use AI to collect needs in the form of simple and easy-to-understand questions when a disaster victim is feeling anxious. The data collection unit can also ask detailed questions and collect specific needs when a disaster victim is calm. The data collection unit can use AI to ask detailed questions and collect specific needs when a disaster victim is calm. The data collection unit can also present options and collect needs when a disaster victim is confused. The data collection unit can use AI to present options and collect needs when a disaster victim is confused. This allows for the collection of more appropriate needs by adjusting the method of collecting needs according to the emotions of the disaster victims. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the collection unit may be performed using AI or not. For example, the collection unit can input the emotional data of disaster victims into a generating AI and have the generating AI adjust the method of collecting needs.

[0078] The data collection unit can analyze the victim's past needs submission history and select the optimal collection method. For example, the data collection unit can automatically suggest similar needs based on the needs previously submitted by the victim. The data collection unit can use AI to automatically suggest similar needs based on the needs previously submitted by the victim. Furthermore, the data collection unit can prioritize the collection of frequently submitted needs from the victim's past needs submission history. The data collection unit can use AI to prioritize the collection of frequently submitted needs from the victim's past needs submission history. Furthermore, the data collection unit can analyze the victim's past needs submission history and select the optimal question format. The data collection unit can use AI to analyze the victim's past needs submission history and select the optimal question format. This allows for the selection of the optimal collection method by analyzing the victim's past needs submission history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the victim's past needs submission history into a generating AI and have the generating AI select the optimal collection method.

[0079] The data collection unit can filter the collected needs based on the disaster victim's current living situation and areas of interest. For example, the data collection unit can prioritize collecting necessary support, taking into account the disaster victim's current living situation. The data collection unit can use AI to prioritize collecting necessary support, taking into account the disaster victim's current living situation. The data collection unit can also collect relevant needs based on the disaster victim's areas of interest. The data collection unit can use AI to collect relevant needs based on the disaster victim's areas of interest. The data collection unit can also collect needs by omitting unnecessary questions based on the disaster victim's living situation and areas of interest. The data collection unit can use AI to collect needs by omitting unnecessary questions based on the disaster victim's living situation and areas of interest. This allows for the collection of needs by omitting unnecessary questions through filtering based on the disaster victim's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the disaster victim's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0080] The data collection unit can estimate the emotions of disaster victims and determine the priority of needs to be collected based on the estimated emotions. For example, if a disaster victim is feeling anxious, the data collection unit will prioritize collecting urgent needs. The data collection unit can use AI to prioritize collecting urgent needs when a disaster victim is feeling anxious. The data collection unit can also prioritize collecting detailed needs when a disaster victim is calm. The data collection unit can use AI to prioritize collecting detailed needs when a disaster victim is calm. The data collection unit can also prioritize collecting basic needs when a disaster victim is confused. The data collection unit can use AI to prioritize collecting basic needs when a disaster victim is confused. This allows for the prioritization of needs according to the emotions of disaster victims, thereby prioritizing the collection of urgent needs. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the emotional data of disaster victims into a generating AI, which can then perform the task of determining the prioritization of their needs.

[0081] The data collection unit can prioritize the collection of highly relevant needs by considering the geographical location information of disaster victims when collecting needs. For example, the data collection unit can prioritize the collection of support needs in the vicinity based on the current location of disaster victims. The data collection unit can use AI to prioritize the collection of support needs in the vicinity based on the current location of disaster victims. The data collection unit can also collect region-specific needs by considering the geographical location information of disaster victims. The data collection unit can use AI to collect region-specific needs by considering the geographical location information of disaster victims. The data collection unit can also prioritize the collection of needs at the nearest support base based on the location information of disaster victims. The data collection unit can use AI to prioritize the collection of needs at the nearest support base based on the location information of disaster victims. This allows for the priority collection of region-specific needs by considering the geographical location information of disaster victims. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the geographical location information of disaster victims into a generating AI and have the generating AI perform the collection of highly relevant needs.

[0082] The data collection unit can analyze the social media activities of disaster victims and collect relevant needs when collecting needs. For example, the data collection unit can analyze the social media posts of disaster victims and collect urgent needs. The data collection unit can use AI to analyze the social media posts of disaster victims and collect urgent needs. The data collection unit can also collect support that is of interest to disaster victims from their social media activities. The data collection unit can use AI to collect support that is of interest to disaster victims from their social media activities. The data collection unit can also collect relevant needs by referring to posts from the social media followers and friends of disaster victims. The data collection unit can use AI to refer to posts from the social media followers and friends of disaster victims and collect relevant needs. This allows for the efficient collection of relevant needs by analyzing the social media activities of disaster victims. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the social media activities of disaster victims into a generating AI and have the generating AI collect relevant needs.

[0083] The matching unit can estimate the emotions of disaster victims and adjust the support matching method based on the estimated emotions. For example, if a disaster victim is feeling anxious, the matching unit can quickly match them with support. The matching unit can use AI to quickly match disaster victims with support when they are feeling anxious. The matching unit can also match disaster victims with more detailed support when they are calm. The matching unit can use AI to match disaster victims with more detailed support when they are calm. The matching unit can also prioritize matching basic support when disaster victims are confused. The matching unit can use AI to prioritize matching basic support when disaster victims are confused. By adjusting the support matching method according to the emotions of disaster victims, more appropriate support can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a 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 matching unit may be performed using AI or not. For example, the matching unit can input the emotional data of disaster victims into a generating AI, which can then adjust the method of matching support recipients.

[0084] The matching unit can adjust the level of detail in the matching process based on the importance of the support. For example, the matching unit can prioritize matching support with a high level of importance. The matching unit can use AI to prioritize matching support with a high level of importance. The matching unit can also provide detailed information when matching support with a low level of importance. The matching unit can use AI to provide detailed information when matching support with a low level of importance. The matching unit can also adjust the priority of the matching process according to the importance of the support. The matching unit can use AI to adjust the priority of the matching process according to the importance of the support. This allows important support to be provided preferentially by adjusting the level of detail in the matching process based on the importance of the support. Some or all of the above processes in the matching unit may be performed using AI or not. For example, the matching unit can input the importance of the support into a generating AI and have the generating AI perform the adjustment of the level of detail in the matching process.

[0085] The matching unit can apply different matching algorithms depending on the category of support during the matching process. For example, in the case of medical support, the matching unit can apply a specialized matching algorithm. The matching unit can use AI to apply a specialized matching algorithm in the case of medical support. The matching unit can also apply an efficient matching algorithm in the case of material support. The matching unit can use AI to apply an efficient matching algorithm in the case of material support. The matching unit can also apply a skill-based matching algorithm in the case of volunteer support. The matching unit can use AI to apply a skill-based matching algorithm in the case of volunteer support. By applying different matching algorithms depending on the category of support, more effective support can be provided. Some or all of the above processing in the matching unit may be performed using AI or not. For example, the matching unit can input the category of support into a generating AI and have the generating AI execute the application of different matching algorithms.

[0086] The matching unit can estimate the emotions of disaster victims and determine matching priorities based on the estimated emotions. For example, if a disaster victim is feeling anxious, the matching unit will prioritize matching with highly urgent support. The matching unit can use AI to prioritize matching with highly urgent support when a disaster victim is feeling anxious. The matching unit can also prioritize matching with detailed support when a disaster victim is calm. The matching unit can use AI to prioritize matching with detailed support when a disaster victim is calm. The matching unit can also prioritize matching with basic support when a disaster victim is confused. The matching unit can use AI to prioritize matching with basic support when a disaster victim is confused. By determining matching priorities according to the emotions of disaster victims, highly urgent support can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI or not. For example, the matching unit can input the emotional data of disaster victims into a generating AI, which can then perform the task of determining the matching priority.

[0087] The matching unit can determine the priority of matching based on the timing of support provision during the matching process. For example, the matching unit can prioritize matching support that is urgently needed. The matching unit can use AI to prioritize matching support that is urgently needed. The matching unit can also postpone support that can be provided later. The matching unit can use AI to postpone support that can be provided later. The matching unit can also adjust the priority of matching according to the timing of support provision. The matching unit can use AI to adjust the priority of matching according to the timing of support provision. This allows for the priority of matching based on the timing of support provision, thereby prioritizing the provision of support that is urgently needed. Some or all of the above-described processes in the matching unit may be performed using AI or not. For example, the matching unit can input the timing of support provision into a generating AI and have the generating AI determine the priority of matching.

[0088] The matching unit can adjust the order of matching based on the relevance of the support during the matching process. For example, the matching unit prioritizes matching the support most relevant to the needs of the disaster victim. The matching unit can use AI to prioritize matching the support most relevant to the needs of the disaster victim. The matching unit can also postpone less relevant support. The matching unit can use AI to postpone less relevant support. The matching unit can also adjust the order of matching according to the relevance of the support. The matching unit can use AI to adjust the order of matching according to the relevance of the support. By adjusting the order of matching based on the relevance of the support, the matching unit can prioritize providing the support most relevant to the needs of the disaster victim. Some or all of the above processing in the matching unit may be performed using AI or not. For example, the matching unit can input the relevance of the support into a generating AI and have the generating AI perform the adjustment of the matching order.

[0089] The suggestion unit can estimate the emotions of disaster victims and adjust the method of suggesting volunteer activities based on the estimated emotions. For example, if a disaster victim is feeling anxious, the suggestion unit can quickly suggest volunteer activities. The suggestion unit can use AI to quickly suggest volunteer activities if a disaster victim is feeling anxious. The suggestion unit can also suggest detailed volunteer activities if a disaster victim is calm. The suggestion unit can use AI to suggest detailed volunteer activities if a disaster victim is calm. The suggestion unit can also prioritize suggesting basic volunteer activities if a disaster victim is confused. The suggestion unit can use AI to prioritize suggesting basic volunteer activities if a disaster victim is confused. By adjusting the method of suggesting volunteer activities according to the emotions of disaster victims, more appropriate activities can be suggested. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the proposal department can input data on the emotional state of disaster victims into a generating AI and have the AI ​​adjust the methods for proposing volunteer activities.

[0090] The proposal department can suggest the most suitable activities based on the volunteer's skills and experience. For example, the proposal department can suggest medical support activities based on the volunteer's medical skills. The proposal department can use AI to suggest medical support activities based on the volunteer's medical skills. The proposal department can also suggest infrastructure development activities based on the volunteer's construction experience. The proposal department can use AI to suggest infrastructure development activities based on the volunteer's construction experience. The proposal department can also suggest educational support activities based on the volunteer's educational experience. The proposal department can use AI to suggest educational support activities based on the volunteer's educational experience. This improves the efficiency of volunteer activities by suggesting the most suitable activities based on the volunteer's skills and experience. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input the volunteer's skills and experience into a generating AI and have the generating AI execute a proposal for the most suitable activity.

[0091] The proposal unit can make optimal suggestions by referring to the volunteer's past activity history when making a suggestion. For example, the proposal unit can suggest similar activities based on the volunteer's past activity history. The proposal unit can use AI to suggest similar activities based on the volunteer's past activity history. The proposal unit can also suggest the most effective activities from the volunteer's past activity history. The proposal unit can use AI to suggest the most effective activities from the volunteer's past activity history. The proposal unit can also analyze the volunteer's past activity history and suggest the optimal activities. The proposal unit can use AI to analyze the volunteer's past activity history and suggest the optimal activities. This allows the proposal unit to suggest the optimal activities by referring to the volunteer's past activity history. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the volunteer's past activity history into a generation AI and have the generation AI execute the optimal suggestion.

[0092] The suggestion unit can estimate the emotions of disaster victims and determine the priority of volunteer activities based on the estimated emotions. For example, if a disaster victim is feeling anxious, the suggestion unit will prioritize suggesting highly urgent volunteer activities. The suggestion unit can use AI to prioritize suggesting highly urgent volunteer activities when a disaster victim is feeling anxious. The suggestion unit can also prioritize suggesting detailed volunteer activities when a disaster victim is calm. The suggestion unit can use AI to prioritize suggesting detailed volunteer activities when a disaster victim is calm. The suggestion unit can also prioritize suggesting basic volunteer activities when a disaster victim is confused. The suggestion unit can use AI to prioritize suggesting basic volunteer activities when a disaster victim is confused. By determining the priority of volunteer activities according to the emotions of disaster victims, it is possible to prioritize suggesting highly urgent activities. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the proposal department can input emotional data of disaster victims into a generating AI and have the AI ​​determine the priorities for volunteer activities.

[0093] The proposal unit can propose the most suitable activities by considering the volunteer's geographical location information when making a proposal. For example, the proposal unit can propose nearby support activities based on the volunteer's current location. The proposal unit can use AI to propose nearby support activities based on the volunteer's current location. The proposal unit can also consider the volunteer's geographical location information and propose support activities specific to the region. The proposal unit can use AI to consider the volunteer's geographical location information and propose support activities specific to the region. The proposal unit can also propose activities at the nearest support base based on the volunteer's location information. The proposal unit can use AI to propose activities at the nearest support base based on the volunteer's location information. This allows for the proposal of region-specific support activities by considering the volunteer's geographical location information. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the volunteer's geographical location information into a generating AI and have the generating AI execute a proposal for the most suitable activities.

[0094] The proposal unit can analyze a volunteer's social media activity and suggest relevant activities when making a proposal. For example, the proposal unit can analyze a volunteer's social media posts and suggest relevant support activities. The proposal unit can use AI to analyze a volunteer's social media posts and suggest relevant support activities. The proposal unit can also suggest support activities of interest based on the volunteer's social media activity. The proposal unit can use AI to suggest support activities of interest based on the volunteer's social media activity. The proposal unit can also suggest relevant support activities by referring to posts from the volunteer's social media followers and friends. The proposal unit can use AI to refer to posts from the volunteer's social media followers and friends and suggest relevant support activities. In this way, relevant support activities can be suggested by analyzing the volunteer's social media activity. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the volunteer's social media activity into a generating AI and have the generating AI execute suggestions for relevant activities.

[0095] The distribution unit can estimate the emotions of disaster victims and adjust the distribution method of relief supplies based on the estimated emotions. For example, if a disaster victim is feeling anxious, the distribution unit can quickly distribute relief supplies. The distribution unit can use AI to quickly distribute relief supplies if a disaster victim is feeling anxious. The distribution unit can also distribute detailed relief supplies if a disaster victim is calm. The distribution unit can use AI to distribute detailed relief supplies if a disaster victim is calm. The distribution unit can also prioritize the distribution of basic relief supplies if a disaster victim is confused. The distribution unit can use AI to prioritize the distribution of basic relief supplies if a disaster victim is confused. By adjusting the distribution method of relief supplies according to the emotions of disaster victims, more appropriate supplies can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the distribution unit may be performed using AI or not. For example, the distribution unit can input the emotional data of disaster victims into a generating AI and have the generating AI adjust the distribution method of relief supplies.

[0096] The distribution unit can select the optimal distribution method by referring to the disaster victim's past history of receiving relief supplies. For example, the distribution unit can prioritize the distribution of necessary supplies based on the disaster victim's past history of receiving relief supplies. The distribution unit can use AI to prioritize the distribution of necessary supplies based on the disaster victim's past history of receiving relief supplies. The distribution unit can also ensure a balanced distribution without surplus or shortage based on the disaster victim's past history of receiving relief supplies. The distribution unit can use AI to ensure a balanced distribution without surplus or shortage based on the disaster victim's past history of receiving relief supplies. The distribution unit can also analyze the disaster victim's past history of receiving relief supplies and select the optimal distribution method. The distribution unit can use AI to analyze the disaster victim's past history of receiving relief supplies and select the optimal distribution method. This allows for a balanced distribution without surplus or shortage by referring to the disaster victim's past history of receiving relief supplies. Some or all of the above processing in the distribution unit may be performed using AI or without AI. For example, the distribution unit can input the disaster victim's past history of receiving relief supplies into a generating AI, and have the AI ​​select the optimal distribution method.

[0097] The distribution unit can customize the distribution of relief supplies based on the current living conditions of disaster victims. For example, the distribution unit can consider the current living conditions of disaster victims and prioritize the distribution of necessary supplies. The distribution unit can use AI to consider the current living conditions of disaster victims and prioritize the distribution of necessary supplies. The distribution unit can also customize and distribute specific supplies based on the living conditions of disaster victims. The distribution unit can use AI to customize and distribute specific supplies based on the living conditions of disaster victims. The distribution unit can also omit unnecessary supplies based on the living conditions of disaster victims. The distribution unit can use AI to omit unnecessary supplies based on the living conditions of disaster victims. By customizing relief supplies based on the living conditions of disaster victims, more appropriate supplies can be provided. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input the living conditions of disaster victims into a generating AI and have the generating AI perform the customization of relief supplies.

[0098] The distribution unit can estimate the emotions of disaster victims and determine the priority of relief supplies based on the estimated emotions. For example, if a disaster victim is feeling anxious, the distribution unit will prioritize the distribution of urgent supplies. The distribution unit can use AI to prioritize the distribution of urgent supplies when a disaster victim is feeling anxious. The distribution unit can also prioritize the distribution of detailed supplies when a disaster victim is calm. The distribution unit can use AI to prioritize the distribution of detailed supplies when a disaster victim is calm. The distribution unit can also prioritize the distribution of basic supplies when a disaster victim is confused. The distribution unit can use AI to prioritize the distribution of basic supplies when a disaster victim is confused. By determining the priority of relief supplies according to the emotions of disaster victims, urgent supplies can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the distribution unit may be performed using AI or not. For example, the distribution unit can input the emotional data of disaster victims into a generating AI and have the generating AI determine the priority of relief supplies.

[0099] The distribution unit can select the optimal distribution method by considering the geographical location information of the disaster victims. For example, the distribution unit can prioritize the distribution of nearby relief supplies based on the disaster victims' current location. The distribution unit can use AI to prioritize the distribution of nearby relief supplies based on the disaster victims' current location. The distribution unit can also distribute region-specific supplies by considering the geographical location information of the disaster victims. The distribution unit can use AI to distribute region-specific supplies by considering the geographical location information of the disaster victims. The distribution unit can also prioritize the distribution of supplies from the nearest support base based on the location information of the disaster victims. The distribution unit can use AI to prioritize the distribution of supplies from the nearest support base based on the location information of the disaster victims. This allows for the priority provision of region-specific supplies by considering the geographical location information of the disaster victims. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input the geographical location information of the disaster victims into a generating AI and have the generating AI select the optimal distribution method.

[0100] The distribution unit can analyze the social media activity of disaster victims and distribute relevant relief supplies at the time of distribution. For example, the distribution unit can analyze the social media posts of disaster victims and distribute supplies with high urgency. The distribution unit can use AI to analyze the social media posts of disaster victims and distribute supplies with high urgency. The distribution unit can also distribute supplies of interest to disaster victims based on their social media activity. The distribution unit can use AI to distribute supplies of interest to disaster victims based on their social media activity. The distribution unit can also distribute relevant supplies by referring to posts from the disaster victims' social media followers and friends. The distribution unit can use AI to refer to posts from the disaster victims' social media followers and friends and distribute relevant supplies. This allows for the efficient provision of relevant supplies by analyzing the social media activity of disaster victims. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input the social media activity of disaster victims into a generating AI and have the generating AI execute the distribution of relevant relief supplies.

[0101] The priority suggestion unit can estimate the emotions of disaster victims and adjust the priority of infrastructure development based on the estimated emotions. For example, if a disaster victim is feeling anxious, the priority suggestion unit can quickly suggest infrastructure development priorities. The priority suggestion unit can use AI to quickly suggest infrastructure development priorities when a disaster victim is feeling anxious. Furthermore, if a disaster victim is calm, the priority suggestion unit can also suggest detailed infrastructure development priorities. The priority suggestion unit can use AI to suggest detailed infrastructure development priorities when a disaster victim is calm. Furthermore, if a disaster victim is confused, the priority suggestion unit can prioritize suggesting basic infrastructure development priorities. The priority suggestion unit can use AI to prioritize suggesting basic infrastructure development priorities when a disaster victim is confused. This allows for the suggestion of more appropriate development by adjusting infrastructure development priorities according to the emotions of disaster victims. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the priority proposal unit may be performed using AI or not. For example, the priority proposal unit can input emotional data of disaster victims into a generating AI and have the generating AI adjust the priority of infrastructure development.

[0102] The priority proposal unit can propose the optimal priority by referring to the past infrastructure development history of the disaster-stricken area when proposing priorities. For example, the priority proposal unit can propose necessary developments on a priority basis based on the past infrastructure development history of the disaster-stricken area. The priority proposal unit can use AI to propose necessary developments on a priority basis based on the past infrastructure development history of the disaster-stricken area. The priority proposal unit can also propose developments that are neither excessive nor insufficient based on the past infrastructure development history of the disaster-stricken area. The priority proposal unit can use AI to propose developments that are neither excessive nor insufficient based on the past infrastructure development history of the disaster-stricken area. The priority proposal unit can also analyze the past infrastructure development history of the disaster-stricken area and propose the optimal priority. The priority proposal unit can use AI to analyze the past infrastructure development history of the disaster-stricken area and propose the optimal priority. This allows for the proposal of developments that are neither excessive nor insufficient by referring to the past infrastructure development history of the disaster-stricken area. Some or all of the above processing in the priority proposal unit may be performed using AI or not. For example, the priority proposal unit can input the past infrastructure development history of the disaster-stricken area into a generating AI and have the generating AI execute the proposal of the optimal priority.

[0103] The priority proposal unit can customize the priority of infrastructure development based on the current situation in the disaster-stricken area when proposing priorities. For example, the priority proposal unit can consider the current situation in the disaster-stricken area and propose necessary developments on a priority basis. The priority proposal unit can use AI to consider the current situation in the disaster-stricken area and propose necessary developments on a priority basis. The priority proposal unit can also customize and propose specific developments based on the situation in the disaster-stricken area. The priority proposal unit can use AI to customize and propose specific developments based on the situation in the disaster-stricken area. The priority proposal unit can also omit unnecessary developments based on the situation in the disaster-stricken area. The priority proposal unit can use AI to omit unnecessary developments based on the situation in the disaster-stricken area. By customizing the priority of infrastructure development based on the current situation in the disaster-stricken area, more appropriate developments can be proposed. Some or all of the above processing in the priority proposal unit may be performed using AI or not. For example, the priority proposal unit can input the current situation in the disaster-stricken area into a generating AI and have the generating AI perform the customization of infrastructure development priorities.

[0104] The priority suggestion unit can estimate the emotions of disaster victims and determine the priority of infrastructure development based on those estimated emotions. For example, if a disaster victim is feeling anxious, the priority suggestion unit will prioritize highly urgent development. The priority suggestion unit can use AI to prioritize highly urgent development when a disaster victim is feeling anxious. The priority suggestion unit can also prioritize detailed development when a disaster victim is calm. The priority suggestion unit can use AI to prioritize detailed development when a disaster victim is calm. The priority suggestion unit can also prioritize basic development when a disaster victim is confused. The priority suggestion unit can use AI to prioritize basic development when a disaster victim is confused. By determining the priority of infrastructure development according to the emotions of disaster victims, it is possible to prioritize highly urgent development. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the priority proposal unit may be performed using AI or not. For example, the priority proposal unit can input emotional data of disaster victims into a generating AI and have the generating AI determine the priorities for infrastructure development.

[0105] The priority proposal unit can propose the optimal priority when proposing priorities, taking into account the geographical location information of the disaster-stricken area. For example, the priority proposal unit can propose infrastructure development in the vicinity of the disaster-stricken area as a priority based on the current location of the disaster-stricken area. The priority proposal unit can use AI to propose infrastructure development in the vicinity of the disaster-stricken area as a priority based on the current location of the disaster-stricken area. Furthermore, the priority proposal unit can also propose development specific to the region, taking into account the geographical location information of the disaster-stricken area. The priority proposal unit can use AI to propose development specific to the region, taking into account the geographical location information of the disaster-stricken area. Furthermore, the priority proposal unit can also propose development at the nearest development base as a priority based on the location information of the disaster-stricken area. The priority proposal unit can use AI to propose development at the nearest development base as a priority based on the location information of the disaster-stricken area. In this way, by taking into account the geographical location information of the disaster-stricken area, development specific to the region can be proposed as a priority. Some or all of the above processing in the priority proposal unit may be performed using AI or not. For example, the priority proposal unit can input the geographical location information of the disaster-stricken area into a generating AI and have the generating AI execute the proposal of the optimal priority.

[0106] The priority proposal unit can analyze social media activity in disaster-stricken areas and propose priorities for relevant infrastructure development when proposing priorities. For example, the priority proposal unit can analyze social media posts in disaster-stricken areas and propose developments with high urgency. The priority proposal unit can use AI to analyze social media posts in disaster-stricken areas and propose developments with high urgency. The priority proposal unit can also propose developments of interest based on social media activity in disaster-stricken areas. The priority proposal unit can use AI to propose developments of interest based on social media activity in disaster-stricken areas. The priority proposal unit can also propose relevant developments by referring to posts from followers and friends on social media in disaster-stricken areas. The priority proposal unit can use AI to refer to posts from followers and friends on social media in disaster-stricken areas and propose relevant developments. This allows for the efficient proposal of relevant developments by analyzing social media activity in disaster-stricken areas. Some or all of the above processing in the priority proposal unit may be performed using AI or not. For example, the priority proposal unit can input social media activity in disaster-stricken areas into a generating AI and have the generating AI execute a proposal for the priority of relevant infrastructure development.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] A disaster relief system can include a health monitoring unit in addition to a needs collection unit that gathers the needs of disaster victims. The health monitoring unit can monitor vital signs such as heart rate, body temperature, and blood pressure of disaster victims in real time, and can provide medical assistance quickly if abnormalities are detected. For example, while the needs collection unit gathers the needs of disaster victims, the health monitoring unit monitors the victims' heart rate and prioritizes matching them with medical assistance if abnormalities are found. The health monitoring unit also monitors the victims' body temperature and can quickly provide medical supplies if a fever is detected. Furthermore, the health monitoring unit monitors the victims' blood pressure and can arrange for a medical examination by a medical professional if high blood pressure is detected. This allows for real-time monitoring of the health status of disaster victims and the rapid provision of appropriate medical assistance.

[0109] A disaster relief system can include a data collection unit to gather the needs of disaster victims, as well as a psychological assessment unit to evaluate their psychological state. The psychological assessment unit can assess the stress levels and psychological burden of disaster victims and provide psychological counseling as needed. For example, when the data collection unit gathers the needs of disaster victims, the psychological assessment unit can assess their stress levels, and if a high-stress state is identified, it can prioritize matching them with psychological counseling. The psychological assessment unit can also assess the psychological burden of disaster victims and provide relaxation programs as needed. Furthermore, the psychological assessment unit can continuously monitor the psychological state of disaster victims and provide long-term psychological support. This allows for an appropriate assessment of the psychological state of disaster victims and the rapid provision of necessary psychological support.

[0110] A disaster relief system can include an environmental monitoring unit in addition to a data collection unit that gathers the needs of disaster victims, to monitor the living environment of those victims. The environmental monitoring unit can monitor environmental data such as temperature, humidity, and air quality in the disaster area in real time and provide support to improve the living environment of disaster victims. For example, while the data collection unit gathers the needs of disaster victims, the environmental monitoring unit monitors the temperature in the disaster area and provides appropriate shelters if extreme temperature changes are detected. The environmental monitoring unit also monitors the humidity in the disaster area and can provide dehumidifiers or desiccants if the humidity is high. Furthermore, the environmental monitoring unit monitors the air quality in the disaster area and can provide air purifiers if air pollution is detected. This allows for a real-time understanding of the living environment of disaster victims and the rapid provision of appropriate support.

[0111] A disaster relief system can include a data collection unit to gather the needs of disaster victims, as well as a movement tracking unit to track their movements. The movement tracking unit can track the location of disaster victims in real time and provide support as needed. For example, while the data collection unit gathers the needs of disaster victims, the movement tracking unit monitors their location and provides information about evacuation shelters if their movement to a shelter is confirmed. The movement tracking unit can also guide disaster victims to the nearest support center if they need assistance while moving. Furthermore, the movement tracking unit can analyze the movement history of disaster victims and suggest optimized evacuation routes. This allows for real-time monitoring of disaster victims' movements and the rapid provision of appropriate support.

[0112] A disaster relief system can include a communication support unit in addition to a data collection unit that gathers the needs of disaster victims. The communication support unit can provide support to facilitate communication between disaster victims and aid workers, and among disaster victims themselves. For example, while the data collection unit gathers the needs of disaster victims, the communication support unit supports information sharing between disaster victims and aid workers. The communication support unit can also provide means of communication among disaster victims, enabling them to help each other. Furthermore, the communication support unit can take into account the language and cultural differences of disaster victims and provide multilingual communication tools. This can facilitate communication among disaster victims and enhance the effectiveness of the support.

[0113] A disaster relief system can include, in addition to a data collection unit that gathers the needs of disaster victims, an emotion priority determination unit that estimates the emotions of disaster victims and determines the priority of support based on those estimated emotions. The emotion priority determination unit can analyze the emotional data of disaster victims and provide urgent support on a priority basis. For example, when the data collection unit gathers the needs of disaster victims, the emotion priority determination unit analyzes the emotions of the disaster victims and provides support quickly if they are feeling anxious or fearful. The emotion priority determination unit can also provide detailed support if the disaster victims are calm. Furthermore, if the disaster victims are confused, the emotion priority determination unit can prioritize basic support. This allows for the prioritization of support according to the emotions of disaster victims and the rapid provision of appropriate support.

[0114] A disaster relief system can include, in addition to a data collection unit that gathers the needs of disaster victims, an emotion adjustment unit that estimates the emotions of disaster victims and adjusts the content of support based on those estimated emotions. The emotion adjustment unit can analyze the emotional data of disaster victims and provide appropriate support. For example, when the data collection unit gathers the needs of disaster victims, the emotion adjustment unit analyzes the emotions of the disaster victims and provides support that provides reassurance if they are feeling anxious. Also, if the disaster victims are calm, the emotion adjustment unit can provide detailed information. Furthermore, if the disaster victims are confused, the emotion adjustment unit can provide concise and easy-to-understand support. In this way, support can be adjusted according to the emotions of disaster victims, and more effective support can be provided.

[0115] A disaster relief system may include a data collection unit that gathers the needs of disaster victims, as well as an emotional adjustment unit that estimates the emotions of disaster victims and adjusts the support methods based on those estimated emotions. The emotional adjustment unit can analyze the emotional data of disaster victims and provide appropriate support methods. For example, when the data collection unit gathers the needs of disaster victims, the emotional adjustment unit analyzes the emotions of the disaster victims and provides quick and concise support methods if they are feeling anxious. If the disaster victims are calm, the emotional adjustment unit can provide support methods with detailed explanations. Furthermore, if the disaster victims are confused, the emotional adjustment unit can offer support methods by presenting options. This allows for the adjustment of support methods according to the emotions of disaster victims, enabling more effective support.

[0116] A disaster relief system can include, in addition to a data collection unit that gathers the needs of disaster victims, an emotion timing adjustment unit that estimates the emotions of disaster victims and adjusts the timing of support based on those estimated emotions. The emotion timing adjustment unit can analyze the emotional data of disaster victims and provide appropriate support timing. For example, when the data collection unit gathers the needs of disaster victims, the emotion timing adjustment unit analyzes the emotions of the disaster victims and provides support quickly if they are feeling anxious. The emotion timing adjustment unit can also provide detailed support if the disaster victims are calm. Furthermore, if the disaster victims are confused, the emotion timing adjustment unit can prioritize providing basic support. This allows for adjusting the timing of support according to the emotions of disaster victims and providing appropriate support quickly.

[0117] A disaster relief system may include a collection unit that gathers the needs of disaster victims, as well as an emotion type determination unit that estimates the emotions of disaster victims and determines the type of support based on those estimated emotions. The emotion type determination unit can analyze the emotional data of disaster victims and provide appropriate support. For example, when the collection unit gathers the needs of disaster victims, the emotion type determination unit analyzes the emotions of the disaster victims and provides psychological support if they are feeling anxious. Furthermore, if the disaster victims are calm, the emotion type determination unit can provide physical support. In addition, if the disaster victims are confused, the emotion type determination unit can prioritize providing basic support. This allows for the determination of support types according to the emotions of disaster victims and the rapid provision of appropriate support.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The collection unit gathers information on the needs of disaster victims. For example, it collects information on the food, water, medical assistance, and shelters that victims need. The collection unit can also analyze social media and security camera footage to understand the situation in the disaster area in real time. AI can be used to collect information on the needs of disaster victims in real time. Step 2: The matching unit matches appropriate support based on the needs collected by the collection unit. For example, it quickly matches the support that disaster victims need. Using AI, it is possible to match appropriate support based on the needs of disaster victims. Step 3: The proposal department proposes volunteer activities based on the support matched by the matching department. For example, it proposes the most suitable activities based on the volunteer's skills and experience. AI can be used to propose the most suitable activities based on the volunteer's skills and experience. Step 4: The distribution unit distributes relief supplies based on the support proposed by the proposal unit. For example, it appropriately distributes necessary relief supplies based on the needs of the disaster victims. Using AI, it is possible to appropriately distribute necessary relief supplies based on the needs of the disaster victims. Step 5: The Prioritization Proposal Department proposes priorities for infrastructure development based on the support proposed by the Proposal Department. For example, it analyzes the situation in disaster-stricken areas and proposes priorities for infrastructure development. AI can be used to analyze the situation in disaster-stricken areas and propose priorities for infrastructure development.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] Each of the multiple elements described above, including the collection unit, matching unit, proposal unit, distribution unit, and priority proposal unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the needs of disaster victims using the camera 42 and microphone 38B of the smart device 14 and analyzes them in real time by the control unit 46A. The matching unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and matches appropriate support based on the collected needs. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable activities based on the skills and experience of volunteers. The distribution unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and appropriately distributes relief supplies based on the needs of disaster victims. The priority proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the situation in the disaster area and proposes priorities for infrastructure development. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the collection unit, matching unit, proposal unit, distribution unit, and priority proposal unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the needs of disaster victims using the camera 42 and microphone 238 of the smart glasses 214 and analyzes them in real time by the control unit 46A. The matching unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and matches appropriate support based on the collected needs. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable activities based on the skills and experience of volunteers. The distribution unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and appropriately distributes relief supplies based on the needs of disaster victims. The priority proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the situation in the disaster area and proposes priorities for infrastructure development. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the collection unit, matching unit, proposal unit, distribution unit, and priority proposal unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the needs of disaster victims using the camera 42 and microphone 238 of the headset terminal 314 and analyzes them in real time by the control unit 46A. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and matches appropriate support based on the collected needs. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the most suitable activities based on the skills and experience of volunteers. The distribution unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and appropriately distributes relief supplies based on the needs of disaster victims. The priority proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the situation in the disaster area and proposes priorities for infrastructure development. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the collection unit, matching unit, proposal unit, distribution unit, and priority proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the needs of disaster victims using the camera 42 and microphone 238 of the robot 414 and analyzes them in real time by the control unit 46A. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and matches appropriate support based on the collected needs. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the most suitable activities based on the skills and experience of volunteers. The distribution unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and appropriately distributes relief supplies based on the needs of disaster victims. The priority proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the situation in the disaster area and proposes priorities for infrastructure development. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] (Note 1) The collection department gathers information on the needs of disaster victims, A matching unit that matches appropriate support based on the needs collected by the aforementioned collection unit, Based on the support matched by the aforementioned matching unit, the proposal unit proposes volunteer activities. A distribution unit that distributes relief supplies based on the support proposed by the aforementioned proposal unit, The system comprises a priority proposal unit that proposes priorities for infrastructure development based on the support proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Analyzing social media and security camera footage allows for real-time assessment of the situation in disaster-stricken areas. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose the most suitable activities based on the volunteer's skills and experience. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned distribution unit is Distribute necessary relief supplies appropriately based on the needs of disaster victims. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned priority proposal unit, We will analyze the situation in the disaster-stricken areas and propose priorities for infrastructure development. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Estimate the emotions of disaster victims and adjust the methods of gathering needs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the past needs submission history of disaster victims and select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting needs, filtering is performed based on the current living situation and areas of interest of disaster victims. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Estimate the emotions of disaster victims and prioritize the needs to be collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting needs, prioritize collecting highly relevant needs by considering the geographical location information of disaster victims. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering needs, analyze the social media activity of disaster victims and collect relevant needs. The system described in Appendix 1, characterized by the features described herein. (Note 12) The matching unit is We estimate the emotions of disaster victims and adjust the support matching method based on the estimated emotions of the disaster victims. The system described in Appendix 1, characterized by the features described herein. (Note 13) The matching unit is During the matching process, adjust the level of detail based on the importance of the support provided. The system described in Appendix 1, characterized by the features described herein. (Note 14) The matching unit is During the matching process, different matching algorithms are applied depending on the support category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The matching unit is The system estimates the emotions of disaster victims and determines matching priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The matching unit is During the matching process, priority is determined based on the timing of support delivery. The system described in Appendix 1, characterized by the features described herein. (Note 17) The matching unit is During the matching process, the order of matches is adjusted based on the relevance of the support provided. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, Estimate the emotions of disaster victims and adjust the methods for proposing volunteer activities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, we suggest the most suitable activities based on the volunteer's skills and experience. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, we will refer to the volunteer's past activity history to make the most appropriate proposal. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, Estimate the emotions of disaster victims and determine the priority of volunteer activities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, we will consider the geographical location of the volunteers to suggest the most suitable activities. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, analyze the volunteers' social media activities and suggest relevant activities. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned distribution unit is Estimate the emotions of disaster victims and adjust the distribution method of relief supplies based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned distribution unit is During distribution, the optimal distribution method will be selected by referring to the disaster victims' past records of receiving relief supplies. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned distribution unit is During distribution, the distribution of relief supplies will be customized based on the current living conditions of the disaster victims. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned distribution unit is The system estimates the emotions of disaster victims and determines the priority of relief supplies based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned distribution unit is When distributing, the optimal distribution method will be selected considering the geographical location information of the disaster victims. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned distribution unit is During distribution, the social media activity of disaster victims will be analyzed, and relief supplies will be distributed based on relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned priority proposal unit, The system estimates the emotions of disaster victims and adjusts the priority of infrastructure development based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned priority proposal unit, When proposing priorities, we will refer to the past infrastructure development history of the disaster-stricken area to propose the optimal priorities. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned priority proposal unit, When proposing priorities, customize infrastructure development priorities based on the current situation in the disaster-stricken areas. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned priority proposal unit, The system estimates the emotions of disaster victims and determines the priority of infrastructure development based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned priority proposal unit, When proposing priorities, we will propose the optimal priority by considering the geographical location information of the affected areas. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned priority proposal unit, When proposing priorities, we will analyze social media activity in disaster-stricken areas and propose priorities for related infrastructure development. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0192] 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 collection department gathers information on the needs of disaster victims, A matching unit that matches appropriate support based on the needs collected by the aforementioned collection unit, Based on the support matched by the aforementioned matching unit, the proposal unit proposes volunteer activities. A distribution unit that distributes relief supplies based on the support proposed by the aforementioned proposal unit, The system comprises a priority proposal unit that proposes priorities for infrastructure development based on the support proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Analyzing social media and security camera footage allows for real-time assessment of the situation in disaster-stricken areas. The system according to feature 1.

3. The aforementioned proposal section is, We propose the most suitable activities based on the volunteer's skills and experience. The system according to feature 1.

4. The aforementioned distribution unit is Distribute necessary relief supplies appropriately based on the needs of disaster victims. The system according to feature 1.

5. The aforementioned priority proposal unit, We will analyze the situation in the disaster-stricken areas and propose priorities for infrastructure development. The system according to feature 1.

6. The aforementioned collection unit is Estimate the emotions of disaster victims and adjust the methods of gathering needs based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the past needs submission history of disaster victims and select the most suitable collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting needs, filtering is performed based on the current living situation and areas of interest of disaster victims. The system according to feature 1.

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