system

Abstract

The system according to this embodiment aims to achieve the optimal allocation of volunteers and supplies in the event of a disaster. [Solution] The system according to the embodiment comprises a collection unit, a deployment unit, an analysis unit, a management unit, and a progress management unit. The collection unit collects the needs of disaster victims. The deployment unit assigns volunteers based on the needs collected by the collection unit. The analysis unit analyzes the needs for supplies. The management unit manages supplies based on the needs analyzed by the analysis unit. The progress management unit visualizes the progress of reconstruction and adjusts the prioritization of activities.

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 performed by at least one processor, the method including 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 is a problem that the arrangement of volunteers and the management of materials at the time of a disaster are likely to be chaotic, and it is difficult to provide efficient rehabilitation support.

[0005] The system according to the embodiment aims to realize an optimal arrangement of volunteers and materials at the time of a disaster.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a deployment unit, an analysis unit, a management unit, and a progress management unit. The collection unit collects the needs of disaster victims. The deployment unit assigns volunteers based on the needs collected by the collection unit. The analysis unit analyzes the needs for supplies. The management unit manages supplies based on the needs analyzed by the analysis unit. The progress management unit visualizes the progress of recovery and adjusts the prioritization of activities. [Effects of the Invention]

[0007] The system according to this embodiment can achieve the optimal allocation of volunteers and supplies in the event of a disaster. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards 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 recovery support system according to an embodiment of the present invention is a system that utilizes AI to achieve the optimal allocation of volunteers and supplies. This disaster recovery support system can efficiently manage the allocation of volunteers and supplies in the event of a disaster. The disaster recovery support system provides a system that assigns volunteers according to their skills and experience based on the needs of disaster victims. It also provides a system that uses AI to organize the needs of supplies and prevent oversupply or shortages so that supplies can be delivered accurately to the affected areas. For example, the disaster recovery support system optimizes volunteer allocation. AI assigns volunteers according to their skills and experience based on the needs of disaster victims. It matches the needs of the affected areas with the skills and qualifications of volunteers, automatically assigns activities according to skills, and maximizes the efficiency of activities. Next, the disaster recovery support system manages supplies. AI analyzes the needs of supplies in real time so that supplies can be delivered accurately to the affected areas. It proposes delivery procedures based on demand to list shortages and prevent surpluses. Furthermore, the disaster recovery support system manages activities. It visualizes the progress of recovery on site, and AI automatically adjusts the priority of activities. This allows the disaster recovery support system to prevent disruption to volunteer activities in disaster-stricken areas and accelerate recovery by efficiently allocating the right personnel and supplies. This enables the disaster recovery support system to optimize the allocation of volunteers and supplies based on the needs of disaster victims, thereby streamlining recovery efforts.

[0029] The disaster recovery support system according to this embodiment comprises a collection unit, a deployment unit, an analysis unit, a management unit, and a progress management unit. The collection unit collects the needs of disaster victims. The collection unit can collect the needs of disaster victims using, for example, a questionnaire survey. The collection unit can also collect the needs of disaster victims through interviews. Furthermore, the collection unit can also collect the needs of disaster victims using digital data collection technology. For example, the collection unit conducts an online questionnaire to collect the needs of disaster victims. Interviews are conducted to directly interact with disaster victims and understand their detailed needs. Digital data collection technology collects the needs of disaster victims through a smartphone application. The deployment unit assigns volunteers based on the needs collected by the collection unit. The deployment unit can assign volunteers using, for example, a skill matching algorithm. Furthermore, the deployment unit can assign volunteers considering geographical proximity. Furthermore, the deployment unit can assign volunteers based on urgency. For example, the deployment unit assigns volunteers with medical skills to disaster-stricken areas where medical assistance is needed. It prioritizes assigning nearby volunteers considering geographical proximity. It assigns volunteers to disaster-stricken areas where emergency assistance is needed based on urgency. The analysis department analyzes the needs for materials. For example, the analysis department can analyze material needs using data mining techniques. It can also analyze material needs using statistical analysis techniques. Furthermore, the analysis department can analyze material needs using machine learning algorithms. For example, the analysis department predicts material needs based on historical material data. It analyzes material demand patterns using statistical analysis techniques. It analyzes material needs in real time using machine learning algorithms. The management department manages materials based on the needs analyzed by the analysis department. For example, the management department can manage materials using an inventory management system. It can also manage materials by adjusting delivery schedules. Furthermore, the management department can manage materials using a quality management system. For example, the management department uses an inventory management system to understand the inventory status of materials.The delivery schedule is adjusted to ensure that supplies reach disaster-stricken areas at the appropriate time. A quality control system is used to maintain the quality of supplies. The progress management department visualizes the progress of reconstruction and adjusts the prioritization of activities. The progress management department can visualize the progress of reconstruction using, for example, graph displays. The progress management department can also visualize the progress of reconstruction using a dashboard. Furthermore, the progress management department can visualize the progress of reconstruction using progress reports. For example, the progress management department visually shows the progress of reconstruction using graph displays. The dashboard is used to grasp the overall picture of the progress of reconstruction. Progress reports are used to provide detailed information on the progress of reconstruction. As a result, the disaster recovery support system according to this embodiment can optimize the allocation of volunteers and supplies based on the needs of disaster victims and make reconstruction support more efficient.

[0030] The data collection department collects the needs of disaster victims. For example, the department can collect needs using questionnaires. It can also collect needs through interviews. Furthermore, the department can collect needs using digital data collection technologies. For example, the department can conduct online questionnaires to collect needs. Interviews are conducted to directly interact with disaster victims and understand their detailed needs. Digital data collection technologies collect needs through smartphone applications. By combining these methods, the data collection department can comprehensively understand the diverse needs of disaster victims. For example, online questionnaires allow for rapid collection of information from a wide range of disaster victims, while interviews allow for in-depth exploration of individual, detailed needs and background information. Digital data collection technologies using smartphone applications enable real-time information updates, allowing for immediate response to changes in the disaster victims' situations. Furthermore, the data collection department centrally manages the collected data and stores it in a database. This allows the collected needs information to be utilized in conjunction with other departments and systems. For example, the data collection department provides the collected data to the analysis department to aid in the analysis of material needs. Furthermore, the data collection unit provides the collected data to the deployment unit, which then uses it to assign volunteers. This allows the data collection unit to efficiently and effectively gather the needs of disaster victims and play a crucial role in supporting the overall recovery support activities of the system.

[0031] The deployment unit assigns volunteers based on the needs collected by the collection unit. The deployment unit can assign volunteers using, for example, a skill-matching algorithm. It can also assign volunteers considering geographical proximity. Furthermore, it can assign volunteers based on urgency. For example, the deployment unit assigns volunteers with medical skills to disaster-stricken areas where medical assistance is needed. It prioritizes assigning nearby volunteers considering geographical proximity. It assigns volunteers to disaster-stricken areas where urgent assistance is needed based on urgency. The deployment unit comprehensively considers these factors to achieve optimal volunteer deployment. Specifically, the skill-matching algorithm compares volunteer skill sets with the needs of disaster-stricken areas to achieve optimal matching. For example, volunteers with medical skills are preferentially assigned to disaster-stricken areas where medical assistance is needed. Considering geographical proximity minimizes travel time and costs, enabling rapid assistance. Furthermore, deployment based on urgency prioritizes assigning volunteers who can respond quickly to disaster-stricken areas requiring urgent assistance. The deployment unit evaluates these factors in real time and builds a system for optimal volunteer deployment. For example, the deployment department utilizes a Geographic Information System (GIS) to visualize the locations of disaster-stricken areas and volunteers, enabling optimal deployment. Furthermore, the deployment department considers volunteers' schedules and available time to ensure efficient deployment. This allows the deployment department to play a crucial role in efficiently and effectively advancing disaster recovery support activities.

[0032] The analysis department analyzes the needs for supplies. For example, the analysis department can analyze the needs for supplies using data mining techniques. It can also analyze the needs for supplies using statistical analysis techniques. Furthermore, it can analyze the needs for supplies using machine learning algorithms. For example, the analysis department predicts the needs for supplies based on past supply data. It analyzes supply demand patterns using statistical analysis techniques. It analyzes supply needs in real time using machine learning algorithms. The analysis department utilizes these techniques to predict supply needs with high accuracy and support appropriate supply supply. Specifically, it uses data mining techniques to analyze past disaster data and supply supply data to extract supply demand patterns and trends. It uses statistical analysis techniques to build supply demand forecasting models and predict future supply needs. It uses machine learning algorithms to analyze data collected in real time and dynamically predict supply needs. For example, the analysis department predicts the demand for supplies such as food, medicine, and daily necessities based on population data and disaster situation data of the affected area. Furthermore, the analysis department analyzes supply chain data to identify supply bottlenecks and challenges. This allows the analysis department to optimize supply plans and ensure the rapid and appropriate delivery of supplies to disaster-stricken areas. In addition, the analysis department provides the analysis results to the management department, which can be used for inventory management and delivery planning. This enables the analysis department to analyze supply needs with high accuracy and play a crucial role in supporting disaster recovery support activities.

[0033] The management department manages supplies based on the needs analyzed by the analysis department. For example, the management department can manage supplies using an inventory management system. It can also manage supplies by adjusting delivery schedules. Furthermore, the management department can manage supplies using a quality management system. For instance, the management department uses an inventory management system to understand the inventory status of supplies. It adjusts delivery schedules to ensure supplies arrive in the affected areas at the appropriate time. It uses a quality management system to maintain the quality of supplies. The management department operates these systems in an integrated manner to manage the supply of supplies efficiently and effectively. Specifically, the inventory management system updates supply inbound and outbound information in real time, accurately understanding the inventory status. This ensures that necessary supplies are supplied at the appropriate time without shortages. In adjusting delivery schedules, the management department plans optimal delivery routes and timings, taking into account the needs and traffic conditions of the affected areas. This ensures that supplies arrive quickly and reliably in the affected areas. Furthermore, the quality management system implements inspection and control processes to maintain the quality of supplies, guaranteeing the quality of supplies provided to the affected areas. For example, supplies such as food and medicine are regularly inspected through a quality control system to ensure their quality. By coordinating these systems, the management department streamlines the entire supply process, enabling the rapid and appropriate delivery of supplies to disaster-stricken areas. This allows the management department to play a crucial role in supporting disaster recovery efforts.

[0034] The progress management department visualizes the progress of reconstruction and adjusts the prioritization of activities. For example, the progress management department can visualize the progress of reconstruction using graphs. It can also visualize the progress of reconstruction using dashboards. Furthermore, it can visualize the progress of reconstruction using progress reports. For example, the progress management department visually shows the progress of reconstruction using graphs. It uses dashboards to grasp the overall picture of reconstruction progress. It uses progress reports to provide detailed reconstruction progress information. The progress management department makes full use of these tools to grasp the progress of reconstruction activities in real time and support appropriate decision-making. Specifically, graphs visually show the progress of reconstruction activities, allowing for a quick grasp of the degree of progress and achievement status of each activity. Dashboards display the overall picture of reconstruction activities in real time and centrally manage the progress and prioritization of each activity. Progress reports provide detailed progress information, specifically showing the progress and challenges of each activity. The progress management department utilizes these tools to continuously monitor the progress of reconstruction activities and adjust the prioritization of activities as needed. For example, the progress management department regularly evaluates the progress of reconstruction activities and reallocates resources to activities that are behind schedule or those with high priority. The progress management department also shares the progress of reconstruction activities with stakeholders to ensure transparency. In this way, the progress management department can support the efficiency and effective progress of reconstruction activities, playing a crucial role in leading disaster recovery support activities to success.

[0035] The data collection unit can analyze past needs data of disaster victims and select the optimal collection method. For example, the data collection unit can automatically display frequently requested needs as candidates based on needs data previously entered by disaster victims. The data collection unit can also predict needs according to specific time periods or situations based on past needs data of disaster victims and adjust the collection method accordingly. Furthermore, the data collection unit can analyze past needs data of disaster victims and propose the most efficient collection method. For example, the data collection unit can list needs frequently requested by disaster victims based on past survey results. It can analyze interview records to predict needs according to specific situations. It can analyze digital data to select the optimal collection method. In this way, the optimal collection method can be selected by analyzing past needs data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input past needs data into a generating AI and have the generating AI select the optimal collection method.

[0036] The data collection unit can filter the collected needs based on the disaster victims' current living situation and areas of interest. For example, the data collection unit can prioritize collecting appropriate needs based on the disaster victims' current living situation (e.g., whether they are in a shelter or at home). The data collection unit can also filter and collect relevant needs based on the disaster victims' areas of interest (e.g., medical care, food, housing). Furthermore, the data collection unit can determine the priority of the needs to be collected based on the disaster victims' living situation and areas of interest. For example, for disaster victims in shelters, the data collection unit will prioritize collecting needs required in shelters. For disaster victims interested in medical care, it will filter and collect medical-related needs. The priority of the needs to be collected will be determined based on the disaster victims' living situation and areas of interest. This ensures that appropriate needs are collected based on the disaster victims' living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input disaster victims' living situation data into a generating AI and have the generating AI perform the filtering.

[0037] 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 needs requiring assistance in the vicinity based on the current location of the disaster victim. The data collection unit can also collect region-specific needs based on the geographical location information of the disaster victim. Furthermore, the data collection unit can prioritize the collection of the most relevant needs by considering the location information of the disaster victim. For example, the data collection unit can use GPS data to identify the current location of the disaster victim and collect needs requiring assistance in the vicinity. It can collect region-specific needs based on address information. It can use map data to prioritize the collection of the most relevant needs. In this way, by considering the geographical location information of the disaster victim, region-specific needs can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of disaster victims into a generating AI and have the generating AI perform the collection of highly relevant needs.

[0038] 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 also collect needs related to areas of interest from the social media activities of disaster victims. Furthermore, the data collection unit can collect the most relevant needs based on the social media data of disaster victims. For example, the data collection unit can analyze the content of social media posts to identify urgent needs. It can collect needs related to areas of interest based on the number of comments and likes. It can analyze social media data and collect the most relevant needs. In this way, relevant needs can be collected 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, for example, or not using AI. For example, the data collection unit can input social media data into a generating AI and have the generating AI perform the collection of relevant needs.

[0039] The placement unit can adjust the level of detail in placement based on the importance of the volunteer's skills during placement. For example, if a volunteer has high skills, the placement unit can provide detailed placement information to support efficient activities. Conversely, if a volunteer has low skills, the placement unit can provide concise placement information to support basic activities. Furthermore, the placement unit can adjust the level of detail in placement according to the importance of the volunteer's skills. For example, the placement unit can provide detailed medical support placement information to volunteers with medical skills, and concise placement information to volunteers with low skills. The level of detail in placement can be adjusted according to the importance of the skills. This allows for efficient activities by adjusting the level of detail in placement according to the importance of the volunteer's skills. Some or all of the above processing in the placement unit may be performed using AI, for example, or without AI. For example, the placement unit can input volunteer skill data into a generating AI and have the generating AI perform the adjustment of the level of detail in placement.

[0040] The placement unit can apply different placement algorithms depending on the volunteer's category during placement. For example, the placement unit can apply a placement algorithm to medical facilities to medical volunteers. It can also apply a placement algorithm to construction sites to construction volunteers. Furthermore, it can apply a placement algorithm to food distribution centers to food assistance volunteers. For example, the placement unit can apply the optimal placement algorithm to medical facilities to medical volunteers, the optimal placement algorithm to construction sites to construction volunteers, and the optimal placement algorithm to food distribution centers to food assistance volunteers. This enables efficient placement by applying the appropriate placement algorithm according to the volunteer's category. Some or all of the above processing in the placement unit may be performed using AI, for example, or without AI. For example, the placement unit can input volunteer category data into a generating AI and have the generating AI execute the application of different placement algorithms.

[0041] The placement unit can determine the priority of placement based on the volunteer registration date. For example, the placement unit may prioritize placing volunteers who registered early. It can also postpone placement of recently registered volunteers. Furthermore, the placement unit can determine the optimal placement priority based on the volunteer registration date. For example, it may prioritize placing volunteers who registered early based on the registration date, postpone placement of recently registered volunteers based on the registration time, or determine the optimal placement priority based on the registration order. This enables efficient placement by determining the optimal placement priority based on the volunteer registration date. Some or all of the above processing in the placement unit may be performed using AI, for example, or without AI. For example, the placement unit can input volunteer registration date data into a generating AI and have the generating AI determine the placement priority.

[0042] The deployment unit can adjust the deployment order based on the relevance of volunteers during deployment. For example, the deployment unit can determine the optimal deployment order based on the relevance of volunteers' skills to the needs of the disaster area. The deployment unit can also adjust the deployment order based on the relevance of volunteers' experience to the situation in the disaster area. Furthermore, the deployment unit can determine the optimal deployment order based on the relevance of volunteers. For example, the deployment unit can evaluate the relevance of volunteers' skills to the needs of the disaster area based on the degree of skill match. It can evaluate the relevance of volunteers' experience to the situation in the disaster area based on their past activity history. It can prioritize the deployment of the most relevant volunteers based on geographical proximity. This enables efficient deployment by determining the optimal deployment order based on the relevance of volunteers. Some or all of the above processes in the deployment unit may be performed using AI, for example, or not. For example, the deployment unit can input volunteer relevance data into a generating AI and have the generating AI perform the adjustment of the deployment order.

[0043] The analysis unit can optimize its analysis algorithm by referring to past material data when analyzing material needs. For example, the analysis unit can select the optimal analysis algorithm based on past material data. The analysis unit can also prioritize the analysis of high-demand materials based on past material data. Furthermore, the analysis unit can optimize its analysis algorithm by referring to past material data. For example, the analysis unit can select the optimal analysis algorithm based on past inventory data. It can prioritize the analysis of high-demand materials based on delivery data. It can optimize the analysis algorithm based on consumption data. In this way, the optimal analysis algorithm can be selected by referring to past material data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past material data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0044] The analysis unit can apply different analysis methods to each category of material when analyzing material needs. For example, the analysis unit can apply food-specific analysis methods to food materials. It can also apply medical-specific analysis methods to medical supplies. Furthermore, it can apply clothing-specific analysis methods to clothing materials. For example, the analysis unit can apply food-specific data mining techniques to food materials, medical-specific statistical analysis techniques to medical supplies, and clothing-specific machine learning algorithms to clothing materials. This enables efficient analysis by applying appropriate analysis methods to each category of material. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input material category data into a generating AI and have the generating AI apply different analysis methods.

[0045] The analysis unit can determine the priority of analysis based on the timing of material delivery when analyzing material needs. For example, the analysis unit can prioritize the analysis of materials that need to be delivered early. It can also postpone the analysis of materials that will be delivered later. Furthermore, the analysis unit can determine the optimal analysis priority based on the timing of material delivery. For example, the analysis unit can prioritize the analysis of materials that need to be delivered early based on the delivery date. It can postpone the analysis of materials that will be delivered later based on the delivery time. It can determine the optimal analysis priority based on the delivery frequency. This enables efficient analysis by determining the optimal analysis priority based on the timing of material delivery. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input material delivery timing data into a generating AI and have the generating AI perform the determination of analysis priority.

[0046] The analysis unit can perform analysis by referring to relevant market data for materials when analyzing material needs. For example, the analysis unit can prioritize analyzing materials with high demand based on relevant market data. The analysis unit can also analyze optimal material needs by referring to market prices for materials. Furthermore, the analysis unit can optimize its analysis algorithm by referring to relevant market data for materials. For example, the analysis unit can prioritize analyzing materials with high demand based on market price data. It can analyze optimal material needs based on demand forecast data. It can optimize its analysis algorithm based on supply data. This allows for the prioritization of analysis of materials with high demand by referring to relevant market data for materials. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant market data for materials into a generating AI and have the generating AI perform the analysis.

[0047] The management department can optimize its management algorithm by referring to past material management data when managing materials. For example, the management department can select the optimal management algorithm based on past material management data. The management department can also prioritize the management of materials with high demand based on past material management data. Furthermore, the management department can optimize its management algorithm by referring to past material management data. For example, the management department can select the optimal management algorithm based on past inventory data. It can prioritize the management of materials with high demand based on delivery data. It can optimize the management algorithm based on consumption data. In this way, the optimal management algorithm can be selected by referring to past material management data. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input past material management data into a generating AI and have the generating AI perform the optimization of the management algorithm.

[0048] The management department can apply different management methods to each category of material when managing supplies. For example, the management department can apply food-specific management methods to food supplies. It can also apply medical-specific management methods to medical supplies. Furthermore, it can apply clothing-specific management methods to clothing supplies. For example, the management department can apply a food-specific inventory management system to food supplies, a medical-specific delivery schedule to medical supplies, and a clothing-specific quality management system to clothing supplies. This allows for efficient material management by applying appropriate management methods to each category of material. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input material category data into a generating AI and have the generating AI apply different management methods.

[0049] The management department can determine the management priority based on the timing of supply delivery when managing supplies. For example, the management department can prioritize the management of supplies that need to be delivered early. It can also postpone the management of supplies that will be delivered later. Furthermore, the management department can determine the optimal management priority based on the timing of supply delivery. For example, the management department can prioritize the management of supplies that need to be delivered early based on the delivery date. It can postpone the management of supplies that will be delivered later based on the delivery time. It can determine the optimal management priority based on the delivery frequency. This enables efficient supply management by determining the optimal management priority based on the timing of supply delivery. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input supply delivery timing data into a generating AI and have the generating AI perform the determination of management priorities.

[0050] The management department can manage materials by referring to relevant market data. For example, the management department can prioritize the management of materials with high demand based on relevant market data. The management department can also perform optimal material management by referring to the market price of materials. Furthermore, the management department can optimize the management algorithm by referring to relevant market data. For example, the management department can prioritize the management of materials with high demand based on market price data. It can perform optimal material management based on demand forecast data. It can optimize the management algorithm based on supply data. In this way, by referring to relevant market data of materials, it is possible to prioritize the management of materials with high demand. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input relevant market data of materials into a generating AI and have the generating AI perform the management.

[0051] The progress management unit can optimize the progress management algorithm by referring to past progress data during progress management. For example, the progress management unit can select the optimal progress management algorithm based on past progress data. The progress management unit can also prioritize the management of important progress items based on past progress data. Furthermore, the progress management unit can optimize the progress management algorithm by referring to past progress data. For example, the progress management unit can select the optimal progress management algorithm based on past progress data. It prioritizes the management of important progress items based on past progress data. It optimizes the progress management algorithm based on past progress data. In this way, the optimal progress management algorithm can be selected by referring to past progress data. Some or all of the above processes in the progress management unit may be performed using AI, for example, or without using AI. For example, the progress management unit can input past progress data into a generating AI and have the generating AI perform the optimization of the progress management algorithm.

[0052] The progress management department can apply different progress management methods to each category of reconstruction activity during progress management. For example, the progress management department can apply a medical-specific progress management method to medical reconstruction activities. It can also apply a construction-specific progress management method to construction reconstruction activities. Furthermore, it can apply a food-specific progress management method to food-related reconstruction activities. For example, the progress management department can apply a medical-specific progress management method to medical reconstruction activities. It can apply a construction-specific progress management method to construction reconstruction activities. It can apply a food-specific progress management method to food-related reconstruction activities. This enables efficient progress management by applying the appropriate progress management method to each category of reconstruction activity. Some or all of the above processing in the progress management department may be performed using AI, for example, or without AI. For example, the progress management department can input reconstruction activity category data into a generating AI and have the generating AI execute the application of different progress management methods.

[0053] The progress management department can determine the priority of progress management based on the start dates of reconstruction activities. For example, the progress management department can prioritize the management of reconstruction activities that started early. It can also postpone reconstruction activities that started later. Furthermore, the progress management department can determine the optimal priority of progress management based on the start dates of reconstruction activities. For example, the progress management department can prioritize the management of reconstruction activities that started early based on the start date. It can postpone reconstruction activities that started later based on the start time. It can determine the optimal priority of progress management based on the start order. This enables efficient progress management by determining the optimal priority of progress management based on the start dates of reconstruction activities. Some or all of the above processes in the progress management department may be performed using AI, for example, or not using AI. For example, the progress management department can input reconstruction activity start date data into a generating AI and have the generating AI perform the determination of progress management priorities.

[0054] The progress management department can perform progress management by referring to data related to reconstruction activities. For example, the progress management department can prioritize the management of important progress items based on the data related to reconstruction activities. The progress management department can also apply the most suitable progress management method by referring to the data related to reconstruction activities. Furthermore, the progress management department can optimize the progress management algorithm by referring to the data related to reconstruction activities. For example, the progress management department prioritizes the management of important progress items based on the relevant data. Based on the relevant data, it applies the most suitable progress management method. Based on the relevant data, it optimizes the progress management algorithm. This allows for the priority management of important progress items by referring to the data related to reconstruction activities. Some or all of the above processes in the progress management department may be performed using AI, for example, or without AI. For example, the progress management department can input data related to reconstruction activities into a generating AI and have the generating AI perform progress management.

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

[0056] The disaster recovery support system can also be equipped with an infrastructure monitoring unit that monitors the infrastructure status of the affected area in real time. The infrastructure monitoring unit monitors the status of infrastructure such as roads, bridges, and power supply in the affected area and quickly arranges necessary repair work. For example, the infrastructure monitoring unit can use drones to take aerial photographs of the infrastructure in the affected area and identify damaged areas. The infrastructure monitoring unit can also use sensors to monitor the condition of the infrastructure in real time and issue alerts if an anomaly is detected. Furthermore, the infrastructure monitoring unit can manage the progress of infrastructure repair work in the affected area and support efficient repairs. This will enable rapid infrastructure restoration in the affected area and facilitate smooth recovery activities.

[0057] The disaster recovery support system can also include a health management department to monitor the health status of disaster victims. This department collects health data from victims and provides necessary medical support. For example, it can monitor vital signs such as body temperature, blood pressure, and heart rate, and dispatch a medical team if abnormalities are detected. The health management department can also provide appropriate medical supplies based on the victims' health status. Furthermore, it can analyze the victims' health data and propose preventative health management plans. This helps maintain the health of disaster victims and supports recovery efforts.

[0058] The disaster recovery support system can also include a life reconstruction department to further assist in the rebuilding of the lives of disaster victims. This department provides the information and resources necessary for the reconstruction of victims' lives. For example, it can support victims in finding new housing. It can also provide job placement services to help victims find employment. Furthermore, it can support victims in smoothly completing necessary administrative procedures. This will support the rebuilding of victims' lives and accelerate recovery efforts.

[0059] The disaster recovery support system could also include an education support department to provide educational support to disaster victims. This department would support the education of children affected by the disaster and create a suitable learning environment. For example, it could assist in the reconstruction of schools in the affected areas. It could also provide online learning programs to children affected by the disaster. Furthermore, it could monitor the learning progress of these children and provide individualized learning support as needed. This would support the education of children affected by the disaster and underpin recovery efforts.

[0060] The disaster recovery support system can also include a community reconstruction department to assist in the rebuilding of disaster-stricken communities. This department supports the reconstruction of disaster-stricken communities and strengthens social connections. For example, it can plan and manage community events in the affected areas. It can also provide online platforms to facilitate interaction among disaster victims. Furthermore, it can collect feedback from disaster victims and incorporate it into community reconstruction plans. This can support the rebuilding of disaster-stricken communities and accelerate recovery efforts.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit gathers the needs of disaster victims. The collection unit can gather the needs of disaster victims using surveys, interviews, and digital data collection technologies. For example, needs can be collected through online surveys or smartphone applications. Step 2: The deployment unit assigns volunteers based on the needs collected by the collection unit. The deployment unit can assign volunteers based on skill matching algorithms, geographical proximity, and urgency. For example, volunteers with medical skills can be assigned to disaster areas where medical assistance is needed. Step 3: The analysis unit analyzes the needs for materials. The analysis unit can analyze the needs for materials using data mining techniques, statistical analysis techniques, and machine learning algorithms. For example, it can predict the needs for materials based on past material data. Step 4: The management department manages supplies based on the needs analyzed by the analysis department. The management department can manage supplies using inventory management systems, delivery schedules, and quality management systems. For example, they can use inventory management systems to track the inventory status of supplies and adjust delivery schedules to ensure that supplies arrive in the affected areas at the appropriate time. Step 5: The progress management department visualizes the progress of reconstruction and adjusts the prioritization of activities. The progress management department can visualize the progress of reconstruction using graphs, dashboards, and progress reports. For example, graphs can be used to visually show the progress of reconstruction, and dashboards can be used to grasp the overall picture of the reconstruction progress.

[0063] (Example of form 2) The disaster recovery support system according to an embodiment of the present invention is a system that utilizes AI to achieve the optimal allocation of volunteers and supplies. This disaster recovery support system can efficiently manage the allocation of volunteers and supplies in the event of a disaster. The disaster recovery support system provides a system that assigns volunteers according to their skills and experience based on the needs of disaster victims. It also provides a system that uses AI to organize the needs of supplies and prevent oversupply or shortages so that supplies can be delivered accurately to the affected areas. For example, the disaster recovery support system optimizes volunteer allocation. AI assigns volunteers according to their skills and experience based on the needs of disaster victims. It matches the needs of the affected areas with the skills and qualifications of volunteers, automatically assigns activities according to skills, and maximizes the efficiency of activities. Next, the disaster recovery support system manages supplies. AI analyzes the needs of supplies in real time so that supplies can be delivered accurately to the affected areas. It proposes delivery procedures based on demand to list shortages and prevent surpluses. Furthermore, the disaster recovery support system manages activities. It visualizes the progress of recovery on site, and AI automatically adjusts the priority of activities. This allows the disaster recovery support system to prevent disruption to volunteer activities in disaster-stricken areas and accelerate recovery by efficiently allocating the right personnel and supplies. This enables the disaster recovery support system to optimize the allocation of volunteers and supplies based on the needs of disaster victims, thereby streamlining recovery efforts.

[0064] The disaster recovery support system according to this embodiment comprises a collection unit, a deployment unit, an analysis unit, a management unit, and a progress management unit. The collection unit collects the needs of disaster victims. The collection unit can collect the needs of disaster victims using, for example, a questionnaire survey. The collection unit can also collect the needs of disaster victims through interviews. Furthermore, the collection unit can also collect the needs of disaster victims using digital data collection technology. For example, the collection unit conducts an online questionnaire to collect the needs of disaster victims. Interviews are conducted to directly interact with disaster victims and understand their detailed needs. Digital data collection technology collects the needs of disaster victims through a smartphone application. The deployment unit assigns volunteers based on the needs collected by the collection unit. The deployment unit can assign volunteers using, for example, a skill matching algorithm. Furthermore, the deployment unit can assign volunteers considering geographical proximity. Furthermore, the deployment unit can assign volunteers based on urgency. For example, the deployment unit assigns volunteers with medical skills to disaster-stricken areas where medical assistance is needed. It prioritizes assigning nearby volunteers considering geographical proximity. It assigns volunteers to disaster-stricken areas where emergency assistance is needed based on urgency. The analysis department analyzes the needs for materials. For example, the analysis department can analyze material needs using data mining techniques. It can also analyze material needs using statistical analysis techniques. Furthermore, the analysis department can analyze material needs using machine learning algorithms. For example, the analysis department predicts material needs based on historical material data. It analyzes material demand patterns using statistical analysis techniques. It analyzes material needs in real time using machine learning algorithms. The management department manages materials based on the needs analyzed by the analysis department. For example, the management department can manage materials using an inventory management system. It can also manage materials by adjusting delivery schedules. Furthermore, the management department can manage materials using a quality management system. For example, the management department uses an inventory management system to understand the inventory status of materials.The delivery schedule is adjusted to ensure that supplies reach disaster-stricken areas at the appropriate time. A quality control system is used to maintain the quality of supplies. The progress management department visualizes the progress of reconstruction and adjusts the prioritization of activities. The progress management department can visualize the progress of reconstruction using, for example, graph displays. The progress management department can also visualize the progress of reconstruction using a dashboard. Furthermore, the progress management department can visualize the progress of reconstruction using progress reports. For example, the progress management department visually shows the progress of reconstruction using graph displays. The dashboard is used to grasp the overall picture of the progress of reconstruction. Progress reports are used to provide detailed information on the progress of reconstruction. As a result, the disaster recovery support system according to this embodiment can optimize the allocation of volunteers and supplies based on the needs of disaster victims and make reconstruction support more efficient.

[0065] The data collection department collects the needs of disaster victims. For example, the department can collect needs using questionnaires. It can also collect needs through interviews. Furthermore, the department can collect needs using digital data collection technologies. For example, the department can conduct online questionnaires to collect needs. Interviews are conducted to directly interact with disaster victims and understand their detailed needs. Digital data collection technologies collect needs through smartphone applications. By combining these methods, the data collection department can comprehensively understand the diverse needs of disaster victims. For example, online questionnaires allow for rapid collection of information from a wide range of disaster victims, while interviews allow for in-depth exploration of individual, detailed needs and background information. Digital data collection technologies using smartphone applications enable real-time information updates, allowing for immediate response to changes in the disaster victims' situations. Furthermore, the data collection department centrally manages the collected data and stores it in a database. This allows the collected needs information to be utilized in conjunction with other departments and systems. For example, the data collection department provides the collected data to the analysis department to aid in the analysis of material needs. Furthermore, the data collection unit provides the collected data to the deployment unit, which then uses it to assign volunteers. This allows the data collection unit to efficiently and effectively gather the needs of disaster victims and play a crucial role in supporting the overall recovery support activities of the system.

[0066] The deployment unit assigns volunteers based on the needs collected by the collection unit. The deployment unit can assign volunteers using, for example, a skill-matching algorithm. It can also assign volunteers considering geographical proximity. Furthermore, it can assign volunteers based on urgency. For example, the deployment unit assigns volunteers with medical skills to disaster-stricken areas where medical assistance is needed. It prioritizes assigning nearby volunteers considering geographical proximity. It assigns volunteers to disaster-stricken areas where urgent assistance is needed based on urgency. The deployment unit comprehensively considers these factors to achieve optimal volunteer deployment. Specifically, the skill-matching algorithm compares volunteer skill sets with the needs of disaster-stricken areas to achieve optimal matching. For example, volunteers with medical skills are preferentially assigned to disaster-stricken areas where medical assistance is needed. Considering geographical proximity minimizes travel time and costs, enabling rapid assistance. Furthermore, deployment based on urgency prioritizes assigning volunteers who can respond quickly to disaster-stricken areas requiring urgent assistance. The deployment unit evaluates these factors in real time and builds a system for optimal volunteer deployment. For example, the deployment department utilizes a Geographic Information System (GIS) to visualize the locations of disaster-stricken areas and volunteers, enabling optimal deployment. Furthermore, the deployment department considers volunteers' schedules and available time to ensure efficient deployment. This allows the deployment department to play a crucial role in efficiently and effectively advancing disaster recovery support activities.

[0067] The analysis department analyzes the needs for supplies. For example, the analysis department can analyze the needs for supplies using data mining techniques. It can also analyze the needs for supplies using statistical analysis techniques. Furthermore, it can analyze the needs for supplies using machine learning algorithms. For example, the analysis department predicts the needs for supplies based on past supply data. It analyzes supply demand patterns using statistical analysis techniques. It analyzes supply needs in real time using machine learning algorithms. The analysis department utilizes these techniques to predict supply needs with high accuracy and support appropriate supply supply. Specifically, it uses data mining techniques to analyze past disaster data and supply supply data to extract supply demand patterns and trends. It uses statistical analysis techniques to build supply demand forecasting models and predict future supply needs. It uses machine learning algorithms to analyze data collected in real time and dynamically predict supply needs. For example, the analysis department predicts the demand for supplies such as food, medicine, and daily necessities based on population data and disaster situation data of the affected area. Furthermore, the analysis department analyzes supply chain data to identify supply bottlenecks and challenges. This allows the analysis department to optimize supply plans and ensure the rapid and appropriate delivery of supplies to disaster-stricken areas. In addition, the analysis department provides the analysis results to the management department, which can be used for inventory management and delivery planning. This enables the analysis department to analyze supply needs with high accuracy and play a crucial role in supporting disaster recovery support activities.

[0068] The management department manages supplies based on the needs analyzed by the analysis department. For example, the management department can manage supplies using an inventory management system. It can also manage supplies by adjusting delivery schedules. Furthermore, the management department can manage supplies using a quality management system. For instance, the management department uses an inventory management system to understand the inventory status of supplies. It adjusts delivery schedules to ensure supplies arrive in the affected areas at the appropriate time. It uses a quality management system to maintain the quality of supplies. The management department operates these systems in an integrated manner to manage the supply of supplies efficiently and effectively. Specifically, the inventory management system updates supply inbound and outbound information in real time, accurately understanding the inventory status. This ensures that necessary supplies are supplied at the appropriate time without shortages. In adjusting delivery schedules, the management department plans optimal delivery routes and timings, taking into account the needs and traffic conditions of the affected areas. This ensures that supplies arrive quickly and reliably in the affected areas. Furthermore, the quality management system implements inspection and control processes to maintain the quality of supplies, guaranteeing the quality of supplies provided to the affected areas. For example, supplies such as food and medicine are regularly inspected through a quality control system to ensure their quality. By coordinating these systems, the management department streamlines the entire supply process, enabling the rapid and appropriate delivery of supplies to disaster-stricken areas. This allows the management department to play a crucial role in supporting disaster recovery efforts.

[0069] The progress management department visualizes the progress of reconstruction and adjusts the prioritization of activities. For example, the progress management department can visualize the progress of reconstruction using graphs. It can also visualize the progress of reconstruction using dashboards. Furthermore, it can visualize the progress of reconstruction using progress reports. For example, the progress management department visually shows the progress of reconstruction using graphs. It uses dashboards to grasp the overall picture of reconstruction progress. It uses progress reports to provide detailed reconstruction progress information. The progress management department makes full use of these tools to grasp the progress of reconstruction activities in real time and support appropriate decision-making. Specifically, graphs visually show the progress of reconstruction activities, allowing for a quick grasp of the degree of progress and achievement status of each activity. Dashboards display the overall picture of reconstruction activities in real time and centrally manage the progress and prioritization of each activity. Progress reports provide detailed progress information, specifically showing the progress and challenges of each activity. The progress management department utilizes these tools to continuously monitor the progress of reconstruction activities and adjust the prioritization of activities as needed. For example, the progress management department regularly evaluates the progress of reconstruction activities and reallocates resources to activities that are behind schedule or those with high priority. The progress management department also shares the progress of reconstruction activities with stakeholders to ensure transparency. In this way, the progress management department can support the efficiency and effective progress of reconstruction activities, playing a crucial role in leading disaster recovery support activities to success.

[0070] The data collection unit can estimate the emotions of the victim and adjust the method of collecting needs based on the estimated emotions. For example, if the victim is stressed, the data collection unit provides a simple and intuitive interface to facilitate the input of needs. If the victim is relaxed, the data collection unit can provide an option to input detailed needs and collect more specific information. Furthermore, if the victim is in a hurry, the data collection unit can prioritize voice input to quickly collect needs. For example, the data collection unit can capture the victim's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. The data collection unit can also record the victim's voice and estimate their emotions using voice analysis technology. Voice analysis technology analyzes the tone and speed of the voice and calculates an emotion score. The data collection unit can also collect the victim's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on fluctuations in heart rate. This allows for the collection of more appropriate needs by adjusting the method of collecting needs according to the victim's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input image data of disaster victims captured by a camera into a generative AI and have the generative AI perform the estimation of the victims' emotions.

[0071] The data collection unit can analyze past needs data of disaster victims and select the optimal collection method. For example, the data collection unit can automatically display frequently requested needs as candidates based on needs data previously entered by disaster victims. The data collection unit can also predict needs according to specific time periods or situations based on past needs data of disaster victims and adjust the collection method accordingly. Furthermore, the data collection unit can analyze past needs data of disaster victims and propose the most efficient collection method. For example, the data collection unit can list needs frequently requested by disaster victims based on past survey results. It can analyze interview records to predict needs according to specific situations. It can analyze digital data to select the optimal collection method. In this way, the optimal collection method can be selected by analyzing past needs data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input past needs data into a generating AI and have the generating AI select the optimal collection method.

[0072] The data collection unit can filter the collected needs based on the disaster victims' current living situation and areas of interest. For example, the data collection unit can prioritize collecting appropriate needs based on the disaster victims' current living situation (e.g., whether they are in a shelter or at home). The data collection unit can also filter and collect relevant needs based on the disaster victims' areas of interest (e.g., medical care, food, housing). Furthermore, the data collection unit can determine the priority of the needs to be collected based on the disaster victims' living situation and areas of interest. For example, for disaster victims in shelters, the data collection unit will prioritize collecting needs required in shelters. For disaster victims interested in medical care, it will filter and collect medical-related needs. The priority of the needs to be collected will be determined based on the disaster victims' living situation and areas of interest. This ensures that appropriate needs are collected based on the disaster victims' living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input disaster victims' living situation data into a generating AI and have the generating AI perform the filtering.

[0073] The data collection unit can estimate the emotions of disaster victims and prioritize the needs to be collected based on those estimated emotions. For example, if a disaster victim is feeling anxious, the unit will prioritize collecting urgent needs. If a disaster victim is relaxed, the unit can also collect more detailed needs and adjust the priorities accordingly. Furthermore, if a disaster victim is in a hurry, the unit can prioritize collecting needs that require a quick response. For example, the unit can capture the disaster victim's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. The unit can also record the disaster victim's voice and estimate their emotions using voice analysis technology. Voice analysis technology analyzes the tone and speed of the voice and calculates an emotion score. The unit can also collect the disaster victim's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on fluctuations in heart rate. This allows for prioritizing needs according to the disaster victim's emotions, enabling the collection of urgent needs to be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input image data of disaster victims captured by a camera into a generative AI and have the generative AI perform the estimation of the victims' emotions.

[0074] 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 needs requiring assistance in the vicinity based on the current location of the disaster victim. The data collection unit can also collect region-specific needs based on the geographical location information of the disaster victim. Furthermore, the data collection unit can prioritize the collection of the most relevant needs by considering the location information of the disaster victim. For example, the data collection unit can use GPS data to identify the current location of the disaster victim and collect needs requiring assistance in the vicinity. It can collect region-specific needs based on address information. It can use map data to prioritize the collection of the most relevant needs. In this way, by considering the geographical location information of the disaster victim, region-specific needs can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of disaster victims into a generating AI and have the generating AI perform the collection of highly relevant needs.

[0075] 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 also collect needs related to areas of interest from the social media activities of disaster victims. Furthermore, the data collection unit can collect the most relevant needs based on the social media data of disaster victims. For example, the data collection unit can analyze the content of social media posts to identify urgent needs. It can collect needs related to areas of interest based on the number of comments and likes. It can analyze social media data and collect the most relevant needs. In this way, relevant needs can be collected 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, for example, or not using AI. For example, the data collection unit can input social media data into a generating AI and have the generating AI perform the collection of relevant needs.

[0076] The placement unit can estimate the volunteer's emotions and adjust the placement method based on the estimated emotions. For example, if the volunteer is nervous, the placement unit can provide a simple and highly visible placement method. If the volunteer is relaxed, the placement unit can also provide a placement method that includes detailed information. Furthermore, if the volunteer is in a hurry, the placement unit can provide a concise placement method. For example, the placement unit can capture the volunteer's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expression. The placement unit can also record the volunteer's voice and estimate their emotions using voice analysis technology. The voice analysis technology analyzes the tone and speed of the voice and calculates an emotion score. The placement unit can also collect the volunteer's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on fluctuations in heart rate. This allows for more appropriate placement by adjusting the placement method according to the volunteer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the deployment unit may be performed using AI, or not using AI. For example, the deployment unit can input image data of volunteers captured by a camera into the generative AI and have the generative AI perform emotion estimation of the volunteers.

[0077] The placement unit can adjust the level of detail in placement based on the importance of the volunteer's skills during placement. For example, if a volunteer has high skills, the placement unit can provide detailed placement information to support efficient activities. Conversely, if a volunteer has low skills, the placement unit can provide concise placement information to support basic activities. Furthermore, the placement unit can adjust the level of detail in placement according to the importance of the volunteer's skills. For example, the placement unit can provide detailed medical support placement information to volunteers with medical skills, and concise placement information to volunteers with low skills. The level of detail in placement can be adjusted according to the importance of the skills. This allows for efficient activities by adjusting the level of detail in placement according to the importance of the volunteer's skills. Some or all of the above processing in the placement unit may be performed using AI, for example, or without AI. For example, the placement unit can input volunteer skill data into a generating AI and have the generating AI perform the adjustment of the level of detail in placement.

[0078] The placement unit can apply different placement algorithms depending on the volunteer's category during placement. For example, the placement unit can apply a placement algorithm to medical facilities to medical volunteers. It can also apply a placement algorithm to construction sites to construction volunteers. Furthermore, it can apply a placement algorithm to food distribution centers to food assistance volunteers. For example, the placement unit can apply the optimal placement algorithm to medical facilities to medical volunteers, the optimal placement algorithm to construction sites to construction volunteers, and the optimal placement algorithm to food distribution centers to food assistance volunteers. This enables efficient placement by applying the appropriate placement algorithm according to the volunteer's category. Some or all of the above processing in the placement unit may be performed using AI, for example, or without AI. For example, the placement unit can input volunteer category data into a generating AI and have the generating AI execute the application of different placement algorithms.

[0079] The placement unit can estimate the volunteer's emotions and adjust the placement duration based on the estimated emotions. For example, if a volunteer is tired, the placement unit can suggest a short placement. Conversely, if a volunteer is energetic, the placement unit can suggest a longer placement. Furthermore, the placement unit can adjust the optimal placement duration based on the volunteer's emotions. For example, the placement unit can capture the volunteer's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. The placement unit can also record the volunteer's voice and estimate their emotions using voice analysis technology. Voice analysis technology analyzes the tone and speed of the voice and calculates an emotion score. The placement unit can also collect the volunteer's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on fluctuations in heart rate. This allows for appropriate placement by adjusting the placement duration according to the volunteer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the placement unit may be performed using AI, or not using AI. For example, the placement unit can input image data of volunteers captured by a camera into the generation AI and have the generation AI perform the estimation of the volunteers' emotions.

[0080] The placement unit can determine the priority of placement based on the volunteer registration date. For example, the placement unit may prioritize placing volunteers who registered early. It can also postpone placement of recently registered volunteers. Furthermore, the placement unit can determine the optimal placement priority based on the volunteer registration date. For example, it may prioritize placing volunteers who registered early based on the registration date, postpone placement of recently registered volunteers based on the registration time, or determine the optimal placement priority based on the registration order. This enables efficient placement by determining the optimal placement priority based on the volunteer registration date. Some or all of the above processing in the placement unit may be performed using AI, for example, or without AI. For example, the placement unit can input volunteer registration date data into a generating AI and have the generating AI determine the placement priority.

[0081] The deployment unit can adjust the deployment order based on the relevance of volunteers during deployment. For example, the deployment unit can determine the optimal deployment order based on the relevance of volunteers' skills to the needs of the disaster area. The deployment unit can also adjust the deployment order based on the relevance of volunteers' experience to the situation in the disaster area. Furthermore, the deployment unit can determine the optimal deployment order based on the relevance of volunteers. For example, the deployment unit can evaluate the relevance of volunteers' skills to the needs of the disaster area based on the degree of skill match. It can evaluate the relevance of volunteers' experience to the situation in the disaster area based on their past activity history. It can prioritize the deployment of the most relevant volunteers based on geographical proximity. This enables efficient deployment by determining the optimal deployment order based on the relevance of volunteers. Some or all of the above processes in the deployment unit may be performed using AI, for example, or not. For example, the deployment unit can input volunteer relevance data into a generating AI and have the generating AI perform the adjustment of the deployment order.

[0082] The analysis unit can estimate the emotions of disaster victims and adjust the analysis method for material needs based on the estimated emotions. For example, if a disaster victim is feeling anxious, the analysis unit will prioritize analyzing urgent material needs. If a disaster victim is relaxed, the analysis unit can also analyze detailed material needs. Furthermore, if a disaster victim is in a hurry, the analysis unit can prioritize analyzing material needs that require a quick response. For example, the analysis unit can capture the disaster victim's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. The analysis unit can also record the disaster victim's voice and estimate their emotions using voice analysis technology. Voice analysis technology analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the disaster victim's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on fluctuations in heart rate. This allows for the analysis of appropriate material needs by adjusting the analysis method according to the disaster victim's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input image data of disaster victims captured by a camera into the generative AI and have the generative AI perform the estimation of the victims' emotions.

[0083] The analysis unit can optimize its analysis algorithm by referring to past material data when analyzing material needs. For example, the analysis unit can select the optimal analysis algorithm based on past material data. The analysis unit can also prioritize the analysis of high-demand materials based on past material data. Furthermore, the analysis unit can optimize its analysis algorithm by referring to past material data. For example, the analysis unit can select the optimal analysis algorithm based on past inventory data. It can prioritize the analysis of high-demand materials based on delivery data. It can optimize the analysis algorithm based on consumption data. In this way, the optimal analysis algorithm can be selected by referring to past material data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past material data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0084] The analysis unit can apply different analysis methods to each category of material when analyzing material needs. For example, the analysis unit can apply food-specific analysis methods to food materials. It can also apply medical-specific analysis methods to medical supplies. Furthermore, it can apply clothing-specific analysis methods to clothing materials. For example, the analysis unit can apply food-specific data mining techniques to food materials, medical-specific statistical analysis techniques to medical supplies, and clothing-specific machine learning algorithms to clothing materials. This enables efficient analysis by applying appropriate analysis methods to each category of material. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input material category data into a generating AI and have the generating AI apply different analysis methods.

[0085] The analysis unit can estimate the emotions of disaster victims and prioritize their material needs based on those estimated emotions. For example, if a disaster victim is feeling anxious, the analysis unit will prioritize analyzing urgent material needs. If a disaster victim is relaxed, the analysis unit can also analyze detailed material needs. Furthermore, if a disaster victim is in a hurry, the analysis unit can prioritize analyzing material needs that require a quick response. For example, the analysis unit can capture a disaster victim's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expression. The analysis unit can also record a disaster victim's voice and estimate their emotions using voice analysis technology. Voice analysis technology analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect a disaster victim's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on fluctuations in heart rate. This allows the analysis unit to prioritize urgent material needs by determining the priority of material needs according to the disaster victim's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input image data of disaster victims captured by a camera into the generative AI and have the generative AI perform the estimation of the victims' emotions.

[0086] The analysis unit can determine the priority of analysis based on the timing of material delivery when analyzing material needs. For example, the analysis unit can prioritize the analysis of materials that need to be delivered early. It can also postpone the analysis of materials that will be delivered later. Furthermore, the analysis unit can determine the optimal analysis priority based on the timing of material delivery. For example, the analysis unit can prioritize the analysis of materials that need to be delivered early based on the delivery date. It can postpone the analysis of materials that will be delivered later based on the delivery time. It can determine the optimal analysis priority based on the delivery frequency. This enables efficient analysis by determining the optimal analysis priority based on the timing of material delivery. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input material delivery timing data into a generating AI and have the generating AI perform the determination of analysis priority.

[0087] The analysis unit can perform analysis by referring to relevant market data for materials when analyzing material needs. For example, the analysis unit can prioritize analyzing materials with high demand based on relevant market data. The analysis unit can also analyze optimal material needs by referring to market prices for materials. Furthermore, the analysis unit can optimize its analysis algorithm by referring to relevant market data for materials. For example, the analysis unit can prioritize analyzing materials with high demand based on market price data. It can analyze optimal material needs based on demand forecast data. It can optimize its analysis algorithm based on supply data. This allows for the prioritization of analysis of materials with high demand by referring to relevant market data for materials. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant market data for materials into a generating AI and have the generating AI perform the analysis.

[0088] The management department can estimate the emotions of disaster victims and adjust the methods of managing supplies based on those estimated emotions. For example, if a disaster victim is feeling anxious, the management department will prioritize the management of urgent supplies. If a disaster victim is relaxed, the management department can also perform detailed supply management. Furthermore, if a disaster victim is in a hurry, the management department can prioritize the management of supplies that require a quick response. For example, the management department can photograph the disaster victim's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. The management department can also record the disaster victim's voice and estimate their emotions using voice analysis technology. Voice analysis technology analyzes the tone and speed of the voice and calculates an emotion score. The management department can also collect the disaster victim's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on fluctuations in heart rate. This allows for appropriate supply management by adjusting the methods of managing supplies according to the disaster victim's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the management department may be performed using AI, or not using AI. For example, the management department can input image data of disaster victims captured by a camera into a generative AI and have the generative AI perform the estimation of the victims' emotions.

[0089] The management department can optimize its management algorithm by referring to past material management data when managing materials. For example, the management department can select the optimal management algorithm based on past material management data. The management department can also prioritize the management of materials with high demand based on past material management data. Furthermore, the management department can optimize its management algorithm by referring to past material management data. For example, the management department can select the optimal management algorithm based on past inventory data. It can prioritize the management of materials with high demand based on delivery data. It can optimize the management algorithm based on consumption data. In this way, the optimal management algorithm can be selected by referring to past material management data. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input past material management data into a generating AI and have the generating AI perform the optimization of the management algorithm.

[0090] The management department can apply different management methods to each category of material when managing supplies. For example, the management department can apply food-specific management methods to food supplies. It can also apply medical-specific management methods to medical supplies. Furthermore, it can apply clothing-specific management methods to clothing supplies. For example, the management department can apply a food-specific inventory management system to food supplies, a medical-specific delivery schedule to medical supplies, and a clothing-specific quality management system to clothing supplies. This allows for efficient material management by applying appropriate management methods to each category of material. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input material category data into a generating AI and have the generating AI apply different management methods.

[0091] The management department can estimate the emotions of disaster victims and determine the priority of supply management based on those estimated emotions. For example, if a disaster victim is feeling anxious, the management department will prioritize the management of urgent supplies. If a disaster victim is relaxed, the management department can also perform detailed supply management. Furthermore, if a disaster victim is in a hurry, the management department can prioritize the management of supplies that require a quick response. For example, the management department can photograph the disaster victim's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. The management department can also record the disaster victim's voice and estimate their emotions using voice analysis technology. Voice analysis technology analyzes the tone and speed of the voice and calculates an emotion score. The management department can also collect the disaster victim's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on fluctuations in heart rate. This allows for the prioritization of supply management according to the disaster victim's emotions, enabling the priority management of urgent supplies. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the management department may be performed using AI, or not using AI. For example, the management department can input image data of disaster victims captured by a camera into a generative AI and have the generative AI perform the estimation of the victims' emotions.

[0092] The management department can determine the management priority based on the timing of supply delivery when managing supplies. For example, the management department can prioritize the management of supplies that need to be delivered early. It can also postpone the management of supplies that will be delivered later. Furthermore, the management department can determine the optimal management priority based on the timing of supply delivery. For example, the management department can prioritize the management of supplies that need to be delivered early based on the delivery date. It can postpone the management of supplies that will be delivered later based on the delivery time. It can determine the optimal management priority based on the delivery frequency. This enables efficient supply management by determining the optimal management priority based on the timing of supply delivery. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input supply delivery timing data into a generating AI and have the generating AI perform the determination of management priorities.

[0093] The management department can manage materials by referring to relevant market data. For example, the management department can prioritize the management of materials with high demand based on relevant market data. The management department can also perform optimal material management by referring to the market price of materials. Furthermore, the management department can optimize the management algorithm by referring to relevant market data. For example, the management department can prioritize the management of materials with high demand based on market price data. It can perform optimal material management based on demand forecast data. It can optimize the management algorithm based on supply data. In this way, by referring to relevant market data of materials, it is possible to prioritize the management of materials with high demand. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input relevant market data of materials into a generating AI and have the generating AI perform the management.

[0094] The progress management unit can estimate the emotions of disaster victims and adjust the display method of progress management based on the estimated emotions. For example, if a disaster victim is feeling anxious, the progress management unit can provide a simple and highly visible display method. If a disaster victim is relaxed, the progress management unit can also provide a display method that includes detailed information. Furthermore, if a disaster victim is in a hurry, the progress management unit can provide a display method that gets straight to the point. For example, the progress management unit can capture the disaster victim's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expression. The progress management unit can also record the disaster victim's voice and estimate their emotions using voice analysis technology. The voice analysis technology analyzes the tone and speed of the voice and calculates an emotion score. The progress management unit can also collect the disaster victim's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on fluctuations in heart rate. This allows for appropriate display by adjusting the progress management display method according to the disaster victim's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the progress management unit may be performed using AI, or not using AI. For example, the progress management unit can input image data of disaster victims captured by a camera into a generative AI and have the generative AI perform the estimation of the victims' emotions.

[0095] The progress management unit can optimize the progress management algorithm by referring to past progress data during progress management. For example, the progress management unit can select the optimal progress management algorithm based on past progress data. The progress management unit can also prioritize the management of important progress items based on past progress data. Furthermore, the progress management unit can optimize the progress management algorithm by referring to past progress data. For example, the progress management unit can select the optimal progress management algorithm based on past progress data. It prioritizes the management of important progress items based on past progress data. It optimizes the progress management algorithm based on past progress data. In this way, the optimal progress management algorithm can be selected by referring to past progress data. Some or all of the above processes in the progress management unit may be performed using AI, for example, or without using AI. For example, the progress management unit can input past progress data into a generating AI and have the generating AI perform the optimization of the progress management algorithm.

[0096] The progress management department can apply different progress management methods to each category of reconstruction activity during progress management. For example, the progress management department can apply a medical-specific progress management method to medical reconstruction activities. It can also apply a construction-specific progress management method to construction reconstruction activities. Furthermore, it can apply a food-specific progress management method to food-related reconstruction activities. For example, the progress management department can apply a medical-specific progress management method to medical reconstruction activities. It can apply a construction-specific progress management method to construction reconstruction activities. It can apply a food-specific progress management method to food-related reconstruction activities. This enables efficient progress management by applying the appropriate progress management method to each category of reconstruction activity. Some or all of the above processing in the progress management department may be performed using AI, for example, or without AI. For example, the progress management department can input reconstruction activity category data into a generating AI and have the generating AI execute the application of different progress management methods.

[0097] The progress management department can estimate the emotions of disaster victims and determine the priority of progress management based on those estimated emotions. For example, if a disaster victim is feeling anxious, the progress management department will prioritize managing high-priority progress items. Furthermore, if a disaster victim is relaxed, the progress management department can also manage detailed progress items. Additionally, if a disaster victim is in a hurry, the progress management department can prioritize managing progress items that require a quick response. For example, the progress management department can capture the disaster victim's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. The progress management department can also record the disaster victim's voice and estimate their emotions using voice analysis technology. The voice analysis technology analyzes the tone and speed of the voice and calculates an emotion score. The progress management department can also collect the disaster victim's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on fluctuations in heart rate. This allows for prioritizing progress management according to the disaster victim's emotions, enabling the priority management of high-priority progress items. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the progress management unit may be performed using AI, or not using AI. For example, the progress management unit can input image data of disaster victims captured by a camera into a generative AI and have the generative AI perform the estimation of the victims' emotions.

[0098] The progress management department can determine the priority of progress management based on the start dates of reconstruction activities. For example, the progress management department can prioritize the management of reconstruction activities that started early. It can also postpone reconstruction activities that started later. Furthermore, the progress management department can determine the optimal priority of progress management based on the start dates of reconstruction activities. For example, the progress management department can prioritize the management of reconstruction activities that started early based on the start date. It can postpone reconstruction activities that started later based on the start time. It can determine the optimal priority of progress management based on the start order. This enables efficient progress management by determining the optimal priority of progress management based on the start dates of reconstruction activities. Some or all of the above processes in the progress management department may be performed using AI, for example, or not using AI. For example, the progress management department can input reconstruction activity start date data into a generating AI and have the generating AI perform the determination of progress management priorities.

[0099] The progress management department can perform progress management by referring to data related to reconstruction activities. For example, the progress management department can prioritize the management of important progress items based on the data related to reconstruction activities. The progress management department can also apply the most suitable progress management method by referring to the data related to reconstruction activities. Furthermore, the progress management department can optimize the progress management algorithm by referring to the data related to reconstruction activities. For example, the progress management department prioritizes the management of important progress items based on the relevant data. Based on the relevant data, it applies the most suitable progress management method. Based on the relevant data, it optimizes the progress management algorithm. This allows for the priority management of important progress items by referring to the data related to reconstruction activities. Some or all of the above processes in the progress management department may be performed using AI, for example, or without AI. For example, the progress management department can input data related to reconstruction activities into a generating AI and have the generating AI perform progress management.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The disaster recovery support system can also include a counseling department to provide psychological support to disaster victims. This department would assess the psychological state of the victims and, if necessary, arrange for professional counselors. For example, it could measure the stress levels of victims and suggest relaxation methods for those experiencing high levels of stress. The department could also set up online counseling sessions based on the victims' psychological needs. Furthermore, the department could continuously monitor the psychological state of the victims and adjust support as needed. This would help maintain the psychological health of the victims and enhance the effectiveness of recovery efforts.

[0102] The disaster recovery support system can also be equipped with an infrastructure monitoring unit that monitors the infrastructure status of the affected area in real time. The infrastructure monitoring unit monitors the status of infrastructure such as roads, bridges, and power supply in the affected area and quickly arranges necessary repair work. For example, the infrastructure monitoring unit can use drones to take aerial photographs of the infrastructure in the affected area and identify damaged areas. The infrastructure monitoring unit can also use sensors to monitor the condition of the infrastructure in real time and issue alerts if an anomaly is detected. Furthermore, the infrastructure monitoring unit can manage the progress of infrastructure repair work in the affected area and support efficient repairs. This will enable rapid infrastructure restoration in the affected area and facilitate smooth recovery activities.

[0103] The disaster recovery support system can also include a health management department to monitor the health status of disaster victims. This department collects health data from victims and provides necessary medical support. For example, it can monitor vital signs such as body temperature, blood pressure, and heart rate, and dispatch a medical team if abnormalities are detected. The health management department can also provide appropriate medical supplies based on the victims' health status. Furthermore, it can analyze the victims' health data and propose preventative health management plans. This helps maintain the health of disaster victims and supports recovery efforts.

[0104] The disaster recovery support system can also include an emotion response unit that estimates the emotions of disaster victims and adjusts the support provided based on those estimates. This unit monitors the emotions of disaster victims in real time and provides appropriate support. For example, if a disaster victim is feeling anxious, the unit can provide a relaxing environment. It can also suggest community activities if a disaster victim is feeling lonely. Furthermore, the unit can flexibly adjust the support provided based on the emotions of the disaster victims. This allows for the provision of appropriate support tailored to the emotions of disaster victims, facilitating smooth recovery efforts.

[0105] The disaster recovery support system can also include a life reconstruction department to further assist in the rebuilding of the lives of disaster victims. This department provides the information and resources necessary for the reconstruction of victims' lives. For example, it can support victims in finding new housing. It can also provide job placement services to help victims find employment. Furthermore, it can support victims in smoothly completing necessary administrative procedures. This will support the rebuilding of victims' lives and accelerate recovery efforts.

[0106] The disaster recovery support system may also include an emotion-based deployment unit that estimates the emotions of disaster victims and adjusts volunteer deployments based on these estimates. This unit monitors the emotions of disaster victims in real time and deploys appropriate volunteers. For example, if a disaster victim is experiencing stress, the unit will deploy volunteers who can provide a relaxing environment. It can also deploy volunteers with strong communication skills if a disaster victim is feeling lonely. Furthermore, the unit can flexibly adjust volunteer deployments based on the emotions of the disaster victims. This allows for appropriate volunteer deployments tailored to the emotions of the disaster victims, facilitating smooth recovery efforts.

[0107] The disaster recovery support system could also include an education support department to provide educational support to disaster victims. This department would support the education of children affected by the disaster and create a suitable learning environment. For example, it could assist in the reconstruction of schools in the affected areas. It could also provide online learning programs to children affected by the disaster. Furthermore, it could monitor the learning progress of these children and provide individualized learning support as needed. This would support the education of children affected by the disaster and underpin recovery efforts.

[0108] The disaster recovery support system can also include an "emotional supplies unit" that estimates the emotions of disaster victims and adjusts the method of supplying goods based on those estimated emotions. The emotional supplies unit monitors the emotions of disaster victims in real time and provides appropriate supplies. For example, if a disaster victim is feeling anxious, the emotional supplies unit provides supplies that provide a sense of security. Furthermore, if a disaster victim is relaxed, the emotional supplies unit can also provide detailed information about the supplies. In addition, the emotional supplies unit can flexibly adjust the method of supplying goods based on the emotions of the disaster victims. This allows for the provision of appropriate supplies in accordance with the emotions of the disaster victims, facilitating the smooth progress of recovery activities.

[0109] The disaster recovery support system can also include a community reconstruction department to assist in the rebuilding of disaster-stricken communities. This department supports the reconstruction of disaster-stricken communities and strengthens social connections. For example, it can plan and manage community events in the affected areas. It can also provide online platforms to facilitate interaction among disaster victims. Furthermore, it can collect feedback from disaster victims and incorporate it into community reconstruction plans. This can support the rebuilding of disaster-stricken communities and accelerate recovery efforts.

[0110] The disaster recovery support system may also include an emotion reporting unit that estimates the emotions of disaster victims and adjusts the reporting method of recovery progress based on the estimated emotions. The emotion reporting unit monitors the emotions of disaster victims in real time and provides an appropriate reporting method. For example, if a disaster victim is feeling anxious, the emotion reporting unit provides a simple and highly visible reporting method. Alternatively, if a disaster victim is relaxed, the emotion reporting unit can provide detailed progress information. Furthermore, the emotion reporting unit can flexibly adjust the reporting method based on the emotions of the disaster victims. This allows for appropriate progress reporting tailored to the emotions of disaster victims, facilitating the smooth progress of recovery activities.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The collection unit gathers the needs of disaster victims. The collection unit can gather the needs of disaster victims using surveys, interviews, and digital data collection technologies. For example, needs can be collected through online surveys or smartphone applications. Step 2: The deployment unit assigns volunteers based on the needs collected by the collection unit. The deployment unit can assign volunteers based on skill matching algorithms, geographical proximity, and urgency. For example, volunteers with medical skills can be assigned to disaster areas where medical assistance is needed. Step 3: The analysis unit analyzes the needs for materials. The analysis unit can analyze the needs for materials using data mining techniques, statistical analysis techniques, and machine learning algorithms. For example, it can predict the needs for materials based on past material data. Step 4: The management department manages supplies based on the needs analyzed by the analysis department. The management department can manage supplies using inventory management systems, delivery schedules, and quality management systems. For example, they can use inventory management systems to track the inventory status of supplies and adjust delivery schedules to ensure that supplies arrive in the affected areas at the appropriate time. Step 5: The progress management department visualizes the progress of reconstruction and adjusts the prioritization of activities. The progress management department can visualize the progress of reconstruction using graphs, dashboards, and progress reports. For example, graphs can be used to visually show the progress of reconstruction, and dashboards can be used to grasp the overall picture of the reconstruction progress.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, deployment unit, analysis unit, management unit, and progress management unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect the needs of disaster victims and transmits them to the data processing unit 12 via the control unit 46A. The deployment unit is implemented by the specific processing unit 290 of the data processing unit 12 and assigns volunteers based on the collected needs. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the needs for supplies. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages supplies based on the analyzed needs. The progress management unit is implemented by the specific processing unit 290 of the data processing unit 12 and visualizes the progress of reconstruction and adjusts the prioritization of activities. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, deployment unit, analysis unit, management unit, and progress management 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 transmits them to the data processing unit 12 by the control unit 46A. The deployment unit is implemented by the specific processing unit 290 of the data processing unit 12 and assigns volunteers based on the collected needs. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the needs for supplies. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages supplies based on the analyzed needs. The progress management unit is implemented by the specific processing unit 290 of the data processing unit 12 and visualizes the progress of reconstruction and adjusts the prioritization of activities. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the collection unit, deployment unit, analysis unit, management unit, and progress management 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 uses the camera 42 and microphone 238 of the headset terminal 314 to collect the needs of disaster victims and transmits them to the data processing unit 12 via the control unit 46A. The deployment unit is implemented by the specific processing unit 290 of the data processing unit 12 and assigns volunteers based on the collected needs. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the needs for supplies. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages supplies based on the analyzed needs. The progress management unit is implemented by the specific processing unit 290 of the data processing unit 12 and visualizes the progress of reconstruction and adjusts the prioritization of activities. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the collection unit, deployment unit, analysis unit, management unit, and progress management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect the needs of disaster victims and transmits them to the data processing unit 12 via the control unit 46A. The deployment unit is implemented by the specific processing unit 290 of the data processing unit 12 and assigns volunteers based on the collected needs. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the needs for supplies. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages supplies based on the analyzed needs. The progress management unit is implemented by the specific processing unit 290 of the data processing unit 12 and visualizes the progress of reconstruction and adjusts the prioritization of activities. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) The collection department gathers information on the needs of disaster victims, A placement unit that assigns volunteers based on the needs collected by the collection unit, The analysis department analyzes the needs for supplies, A management unit manages materials based on the needs analyzed by the aforementioned analysis unit, It includes a progress management department that visualizes the progress of reconstruction and adjusts the prioritization of activities. A system characterized by the following features. (Note 2) 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 3) The aforementioned collection unit is Analyze past needs data of disaster victims and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) 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 5) 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 6) 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 7) 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 8) The aforementioned arrangement section is, The system estimates the volunteers' emotions and adjusts the placement representation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned arrangement section is, When assigning volunteers, adjust the level of detail based on the importance of their skills. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned arrangement section is, During placement, different placement algorithms are applied depending on the volunteer category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned arrangement section is, Estimate the volunteers' emotions and adjust the placement length based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned arrangement section is, When assigning volunteers, priority will be determined based on when the volunteers registered. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned arrangement section is, During placement, the order of placement will be adjusted based on the relevance of the volunteers. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, We estimate the emotions of disaster victims and adjust the analysis method for material needs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing material needs, we optimize the analysis algorithm by referring to past material data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, When analyzing material needs, different analytical methods are applied to each category of material. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Estimate the emotions of disaster victims and prioritize their material needs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When analyzing material needs, prioritize the analysis based on the timing of material delivery. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, When analyzing material needs, the analysis is performed by referring to relevant market data for those materials. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, Estimate the emotions of disaster victims and adjust the methods of managing supplies based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, When managing supplies, the management algorithm is optimized by referring to past supply management data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, When managing supplies, different management methods should be applied to each category of supplies. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, Estimate the emotions of disaster victims and determine the prioritization of supply management based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, When managing supplies, prioritize management based on when the supplies were provided. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, When managing supplies, refer to relevant market data for those supplies. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned progress management unit, The system estimates the emotions of disaster victims and adjusts the display method of progress management based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned progress management unit, When managing progress, the progress management algorithm is optimized by referring to past progress data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned progress management unit, When managing progress, different progress management methods should be applied to each category of reconstruction activity. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned progress management unit, Estimate the emotions of disaster victims and determine the priority of progress management based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned progress management unit, When managing progress, prioritize progress management based on when the reconstruction activities began. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned progress management unit, When managing progress, refer to data related to reconstruction activities. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 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 placement unit that assigns volunteers based on the needs collected by the collection unit, The analysis department analyzes the needs for supplies, A management unit manages materials based on the needs analyzed by the aforementioned analysis unit, It includes a progress management department that visualizes the progress of reconstruction and adjusts the prioritization of activities. A system characterized by the following features.

2. 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.

3. The aforementioned collection unit is Analyze past needs data of disaster victims and select the optimal data collection method. The system according to feature 1.

4. 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.

5. 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 according to feature 1.

6. The aforementioned collection unit is When collecting needs, prioritize collecting highly relevant needs by considering the geographical location information of disaster victims. The system according to feature 1.

7. The aforementioned collection unit is When gathering needs, analyze the social media activity of disaster victims and collect relevant needs. The system according to feature 1.

8. The aforementioned arrangement section is, The system estimates the volunteers' emotions and adjusts the placement representation based on those estimated emotions. The system according to feature 1.

9. The aforementioned arrangement section is, When assigning volunteers, adjust the level of detail based on the importance of their skills. The system according to feature 1.

10. The aforementioned arrangement section is, During placement, different placement algorithms are applied depending on the volunteer category. The system according to feature 1.

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