system

Abstract

We provide the system. [Solution] Information gathering methods for collecting information on the need for support in disaster-stricken areas, An analytical means for identifying the type and priority of necessary resources based on the aforementioned support needs information, Matching means for selecting available external resources corresponding to the identified resources, A means for generating a supply plan to efficiently distribute selected resources to the disaster-stricken area, A means of notifying disaster victims and related parties based on the supply plan, A feedback mechanism for receiving feedback after supply and improving the system, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 When a large-scale disaster occurs, there is a problem that relief supplies and personnel for the disaster-stricken area are not appropriately and promptly distributed, and the assistance needed by the disaster victims cannot be delivered. For this reason, there may be an overabundance or shortage of assistance and unfair distribution, which may delay the reconstruction of the lives of the disaster victims. 【Means for Solving the Problems】 【0005】 The present invention solves the above problems by providing a system that includes information gathering means for collecting support needs information in disaster-stricken areas, analysis means for identifying the types and priorities of necessary resources based on the support needs information, matching means for selecting available external resources corresponding to the identified resources, means for generating a supply plan for efficiently distributing the selected resources to the disaster-stricken areas, notification means for notifying disaster victims and related parties based on the supply plan, and feedback means for receiving feedback after supply and improving the system. 【0006】 "Information gathering means" refers to technologies or devices for obtaining information on the need for assistance in disaster-stricken areas, and for collecting and storing data. 【0007】 "Analysis means" refers to a technology or device that analyzes the type, quantity, and priority of resources needed based on acquired support needs information, and evaluates the urgency of the situation. 【0008】 A "matching method" is a process or technology that compares and selects resources that can be supplied from external sources according to the identified resource needs, and determines the appropriate supplier. 【0009】 "Means for generating supply plans" refers to technologies or devices that formulate schedules and plans for efficiently distributing selected resources to disaster-stricken areas. 【0010】 "Notification means" refers to technology or equipment used to deliver information to disaster victims and support personnel based on the generated supply plan. 【0011】 A "feedback mechanism" is a technology or device used to collect evaluations and requests from disaster victims and related parties after the distribution of relief supplies, and to use this information to improve the system. [Brief explanation of the drawing] 【0012】 [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 the data processing device and 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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0013】 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. 【0014】 First, the terms used in the following description will be explained. 【0015】 In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0016】 In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0017】 In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc. 【0018】 In the following embodiments, a communication I / F (Interface) with a reference numeral is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc. 【0019】 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0020】 [First Embodiment] 【0021】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0022】 As shown in Figure 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. 【0023】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0024】 The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0025】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0026】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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. 【0027】 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. 【0028】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0029】 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. 【0030】 The 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. 【0031】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0032】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0033】 This invention is a system for effectively providing support in disaster-stricken areas when a large-scale disaster occurs. In this system, terminals implemented in each disaster-stricken area collect initial information, input and analyze user needs and circumstances, and transmit them to a server. 【0034】 Device features: 【0035】 The terminals allow disaster victims to input information about the supplies and support they need. For example, terminals are installed in each evacuation center, providing a system for users to report shortages of supplies, high-priority support, and special medical needs. The terminals instantly digitize the information gathered on-site and send it to a server, enabling real-time information sharing. 【0036】 Server functions: 【0037】 The server has the ability to receive information transmitted from terminals and analyze that data. The server uses machine learning algorithms to analyze and prioritize the needs of the disaster area. The server also matches information on resources available from external sources with the needs of the disaster area and decides which resources to send to which area. 【0038】 Furthermore, the server creates an effective supply plan and notifies the affected areas and relevant organizations via terminals. This enables swift and appropriate relief efforts. 【0039】 User roles: 【0040】 Users can report their needs via terminals located in evacuation centers. This includes the types of supplies needed, the current situation, the number of people, and any special requests. After receiving relief supplies, they can input feedback on the situation and their satisfaction level into the terminal and send it to the server to help plan future relief efforts. 【0041】 This system enables disaster-stricken areas and victims to receive necessary support in a timely manner, allowing for efficient responses even in chaotic situations. For example, if a disaster victim reports a shortage of water and food via a terminal, the server immediately analyzes the need, creates a plan for supplying water and food from external aid organizations, and instructs them to deliver the supplies quickly. 【0042】 The following describes the processing flow. 【0043】 Step 1: 【0044】 The terminal displays an interface for disaster victims to input needs information at evacuation centers. Users enter the supplies they need and the most urgent assistance they require into this interface. 【0045】 Step 2: 【0046】 The terminal analyzes the input data in real time and performs an initial assessment of its importance and urgency. The data, including the results of the initial assessment, is immediately sent to the server. 【0047】 Step 3: 【0048】 The server stores the data received from the terminal in a database and analyzes the collected needs information. The server uses machine learning algorithms to classify and prioritize support needs. 【0049】 Step 4: 【0050】 The server aggregates information on resources available from external sources and matches it with the needs of disaster victims. The server then uses a matching algorithm to select the most suitable suppliers and develop a supply plan for materials. 【0051】 Step 5: 【0052】 The server notifies relevant organizations and personnel in the disaster-stricken areas of the created supply plan via terminals, informing them of the expected arrival date of the supplies. 【0053】 Step 6: 【0054】 Based on the information provided, users receive relief supplies at the designated location and time. They confirm receipt of the supplies via their device and input feedback regarding the receipt status and any additional needs. 【0055】 Step 7: 【0056】 The server receives feedback from the terminals and analyzes the data to improve the accuracy of subsequent support activities. Based on the feedback information, it improves algorithms and supply plans as needed. 【0057】 (Example 1) 【0058】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0059】 Disaster relief efforts in affected areas require the rapid and accurate identification, supply, and prioritization of necessary goods and resources. However, currently, information gathering and analysis are inefficient, leading to problems with the timely provision of necessary support. As a result, disaster victims may not receive adequate assistance, potentially causing confusion. Therefore, this invention aims to realize the efficient and effective supply of goods in disaster-stricken areas. 【0060】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0061】 In this invention, the server includes a data input device, a data analysis device, and a calibration device. This enables the immediate collection of demand information in disaster-stricken areas, the determination of priorities, and the formulation of an optimal supply plan. 【0062】 A "data input device" is a device that receives information about necessary goods and support from users in disaster-stricken areas, digitizes it, and transmits it to a server. 【0063】 A "data analysis device" is a device that analyzes the demand information received from disaster-stricken areas and sets resource priorities using machine learning algorithms. 【0064】 A "fitting device" is a device that, based on analysis results, identifies available external resources and matches them to the demand in the most suitable way. 【0065】 A "distribution plan creation device" is a device that formulates a supply plan to optimally deliver selected resources to disaster-stricken areas and communicates that plan to relevant organizations and facilities. 【0066】 A "communication device" is a device that supports the efficient supply of goods by promptly notifying relevant parties of the formulated supply plan. 【0067】 An "evaluation information processing device" is a device that analyzes the evaluation information received after supply and uses it to improve the system in order to help with future support. 【0068】 This invention is a system for providing efficient support in the event of a large-scale disaster. The system mainly consists of terminals, servers, and users, with each element having its own role. 【0069】 Terminal role: 【0070】 The terminals are installed in disaster-stricken areas and function as an interface for users to input their individual needs. Users input information such as "water is scarce" or "food is needed" using, for example, a touchscreen or voice recognition software. The entered data is immediately digitized and transmitted to a server via the internet. The hardware used includes common tablets and smartphones, and the software includes a voice recognition engine. 【0071】 Server role: 【0072】 The server functions as a central processing unit that analyzes the received data. It uses TENSORFLOW®, an open-source machine learning framework, to analyze the data and classify the needs of the disaster-stricken areas. Furthermore, the server uses external sensors and APIs to understand the local situation and create an optimal plan for supplying relief materials. Specifically, it uses the Google® Maps API for geographical optimization. 【0073】 User roles: 【0074】 Users report their needs using a device and provide feedback after receiving assistance. This feedback is then sent back to the server and used as data to improve future assistance activities. For example, if feedback indicates that "food supplies at shelters are still insufficient," the next supply plan will be adjusted based on that feedback. 【0075】 Using a generative AI model, an example of a prompt might be, "Design a prototype application that analyzes the needs of disaster-stricken areas in real time and proposes the optimal support plan." Based on this prompt, the AI ​​model provides support for efficient data analysis and automatic generation of supply plans. 【0076】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0077】 Step 1: 【0078】 The terminal functions as an interface for receiving user input. The user enters their current needs into the terminal. This input data includes the type of item, quantity, urgency, etc. The terminal receives and digitizes this data via voice recognition or touchscreen. As output, the user's needs information is generated as data packets. 【0079】 Step 2: 【0080】 The terminal sends the generated data packets to the server. Internet-related technologies are used for transmission. The terminal receives digital data generated from the terminal as input and converts it into the appropriate communication protocol. As output, it sends data packets along the path to reach the server. 【0081】 Step 3: 【0082】 The server analyzes the data received from the terminal. It receives digital data from the terminal as input. The server analyzes the data using machine learning algorithms, particularly TensorFlow, to classify and prioritize each need. A list of processed needs is generated as output. 【0083】 Step 4: 【0084】 The server creates a suitable supply plan based on the analysis results. It receives a list of categorized needs as input. The server utilizes the Google Maps API and external resource information to plan the delivery of necessary items to the optimal locations. A detailed supply plan is generated as output. 【0085】 Step 5: 【0086】 The server sends the generated supply plan to the terminal. As input, it retrieves the delivery plan from its data repository. The server sends the plan as digital data in a format the terminal can interpret. As output, the terminal receives a notification of the supply plan. 【0087】 Step 6: 【0088】 The terminal displays the supply plan from the server and notifies users and shelter staff. It receives digital data from the server as input. The terminal visually displays this data and notifies users of the plan through means such as audio alerts. The displayed plan information is transmitted to the user as output. 【0089】 Step 7: 【0090】 Users input feedback about the relief supplies and services they receive into a terminal. The terminal receives information such as satisfaction levels and areas for improvement as input. The terminal digitizes this information and sends it back to the server. The output is a data packet containing the improvement information. 【0091】 (Application Example 1) 【0092】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0093】 In the event of a large-scale disaster, there is a lack of mechanisms to quickly grasp the needs of affected areas and efficiently supply appropriate relief materials. Furthermore, traditional methods are time-consuming in gathering needs from victims, making it difficult to prioritize support, thus hindering rapid relief efforts. To address this problem, immediate data collection from the field and real-time resource supply planning are required. 【0094】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0095】 In this invention, the server includes information gathering means for collecting support needs information in disaster-stricken areas, means for users to report needs information along with local location information via a mobile terminal, and real-time processing means for analyzing information transmitted from the site and quickly formulating and executing a resource supply plan. This enables the immediate digitization of needs in disaster-stricken areas, real-time analysis, dynamic prioritization, and efficient supply of relief materials. 【0096】 "Information gathering means" refers to devices or methods for collecting information on the need for support in disaster-stricken areas, and is used to gather information on the needs of disaster victims. 【0097】 "Analysis means" refers to an apparatus or method for determining the types of resources needed and their priorities based on collected support needs information. 【0098】 "Matching means" refers to a device or method for associating a specified resource with an external resource that can supply it. 【0099】 "Means for generating supply plans" refers to an apparatus or method for creating a plan for efficiently distributing selected resources to disaster-stricken areas. 【0100】 "Notification means" refers to a device or method for conveying necessary information to residents of the disaster-stricken area and relevant support organizations based on the support plan. 【0101】 A "feedback mechanism" is a device or method that receives information about the status of relief supplies after they have been supplied and uses that information to improve the system. 【0102】 "Means of reporting via mobile devices" refers to devices or methods using mobile devices or applications that enable disaster victims to easily report their local location and needs. 【0103】 A "real-time processing means" is a device or method for immediately analyzing information transmitted from the site and quickly formulating and executing a resource supply plan. 【0104】 The system of the present invention is primarily implemented through an information collection terminal, an analysis server, and a mobile terminal. The system is configured as follows: 【0105】 Information gathering terminals are installed in disaster-stricken areas. These terminals allow disaster victims to input information about the supplies and services they need. For example, a user interface is provided via a mobile device, allowing users to report their current needs using voice or text input. GPS functionality is also used to identify the location where the input was made, by adding location information. 【0106】 The analysis server receives information transmitted from terminals in real time. The server efficiently stores data using cloud services such as Google Cloud Platform and analyzes the data using machine learning algorithms (e.g., TensorFlow). The analysis prioritizes the support needs of each region. Furthermore, it generates resource allocation plans and adjusts them to ensure optimal support is distributed. 【0107】 Users can use mobile devices such as smartphones and smart glasses to report their support needs via an application and provide feedback after receiving support. This allows for real-time support planning based on the latest information. 【0108】 As a concrete example, consider a case where residents of an area affected by heavy rain use an app to report food shortages. The information transmitted via mobile devices is immediately analyzed on a server, and a plan is created to prioritize the supply of necessary food. 【0109】 An example of a prompt is, "Explain how to analyze the information and develop a support plan when residents in flood-affected areas who need food supplies report their need for assistance using a smartphone app." Using prompts like this to the generative AI model helps to easily obtain specific support procedures and plans. 【0110】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0111】 Step 1: 【0112】 The terminal receives information about the needs of disaster victims as input. Users use their mobile devices to input location information and information about shortages of supplies, and the terminal collects this data. The input information is immediately formatted as digital data and becomes data to be sent to the server. 【0113】 Step 2: 【0114】 The terminal transmits the collected information to the server. The entered data includes the current situation of disaster victims, the supplies they need, and their location. The terminal then transfers the formatted digital data to the server via the internet. 【0115】 Step 3: 【0116】 The server receives data from terminals as input in real time. The server uses a Google Cloud Platform database to store the data and prepare it for analysis by machine learning algorithms. 【0117】 Step 4: 【0118】 The server performs analysis based on the received data. Specifically, it uses machine learning libraries such as TensorFlow to classify and prioritize needs. Through this analysis, it determines the highest priority for resource supply and then proceeds to the next processing step based on the results. 【0119】 Step 5: 【0120】 The server generates a resource supply plan using the analysis results. It uses a matching algorithm to correlate available resources with disaster-stricken areas in high need. Specifically, it develops a detailed plan specifying which resources to supply to which areas and in what quantities. 【0121】 Step 6: 【0122】 The server outputs the generated supply plan to relevant organizations and disaster victims through notification channels. The server distributes information using email and the notification function of a dedicated app, promptly informing relevant parties of the plan's contents. 【0123】 Step 7: 【0124】 Users can provide feedback again after receiving supplies. This feedback includes inputting information about their satisfaction with the supply and any additional needs. The terminal then sends this feedback information to the server. 【0125】 Step 8: 【0126】 The server receives feedback data and uses it to improve the system. By analyzing the received data, it helps improve the accuracy of future supply plans and needs forecasts. The server uses a generative AI model to continuously optimize support plans. 【0127】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0128】 This invention is a system for effectively carrying out support activities in disaster-stricken areas during large-scale disasters, and aims to provide support that takes into account the emotional state of the user. In particular, this system enables the effective collection of the urgent needs of disaster victims and the improvement of delivery plans. 【0129】 Device features: 【0130】 The terminal is equipped with an interface for inputting disaster victims' needs information, allowing users to easily report the supplies they lack and the assistance they need. Furthermore, the terminal has an emotion engine that recognizes emotions from the user's input data. For example, if a user expresses emotions such as worry or fear, the emotion engine analyzes that information and makes a judgment. This emotional information reflects not only the need for supplies but also the psychological urgency the user is experiencing. 【0131】 Server functions: 【0132】 The server receives needs information and sentiment data sent from terminals and integrates it into a database. Using machine learning algorithms, the server designs more adaptive resource allocation by reflecting the user's sentiment data in the priority of assistance. For example, if the sentiment data indicates that many disaster victims are feeling anxious, the server will develop a plan to expedite assistance to that area. 【0133】 The server also aggregates available resources from external support organizations and companies and matches them with disaster relief needs. In this process, the server utilizes data obtained from the emotion engine to determine whether psychological support should be prioritized. 【0134】 User roles: 【0135】 Users input the expected support and provide emotional feedback through their device. This includes detailed feedback, including emotions, on whether the received support supplies were appropriate and how to incorporate those points into future support plans. 【0136】 This system configuration enables accurate support in disaster-stricken areas, including addressing users' inner needs, and facilitates a swift and effective disaster response. For example, if a disaster victim expresses the emotion of "worry," the system prioritizes processing that need, providing emotional support and promptly supplying necessary materials. 【0137】 The following describes the processing flow. 【0138】 Step 1: 【0139】 The terminal provides an interface for disaster victims to input their needs for supplies and assistance. Users input the supplies they lack and their specific requests on the terminal, and select options that represent their emotional state (e.g., reassured, anxious, frightened). 【0140】 Step 2: 【0141】 The device acquires information entered by the user as digital data and analyzes the user's emotional state using its built-in emotion engine. Needs data, including the analysis results, is then generated. 【0142】 Step 3: 【0143】 The device transmits generated needs data and emotion data to the server in real time. Location and time information are also included in the data. 【0144】 Step 4: 【0145】 The server stores the data received from the terminals in a database and aggregates information on the needs and emotions of each affected area. 【0146】 Step 5: 【0147】 The server analyzes the collected data using machine learning algorithms to reassess the prioritization and urgency of needs. It takes emotional data into consideration, paying particular attention to areas where psychological support is needed. 【0148】 Step 6: 【0149】 The server matches externally available resource information with the needs of the affected area and develops an optimal support plan based on emotional data. This plan includes the prioritization of supplying materials and additional psychological support. 【0150】 Step 7: 【0151】 The server notifies aid organizations and disaster relief personnel in various locations of the formulated supply plan via terminals. The plan includes estimated arrival dates for supplies and details of psychological support. 【0152】 Step 8: 【0153】 Users can check the status of the support provided through their device and input feedback, including their feelings, about the received supplies and psychological support. 【0154】 Step 9: 【0155】 The terminal sends user feedback to the server, which uses this data to improve future support plans. Emotional data is continuously analyzed to contribute to overall system improvement. 【0156】 (Example 2) 【0157】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0158】 In disaster relief efforts, it is crucial to effectively and quickly identify the needs for supplies and services and to provide support based on priorities. However, conventional methods have made it difficult to develop support plans that take into account the emotional state of disaster victims, and have failed to adequately assess the need for psychological support. As a result, the physical and psychological needs of disaster victims have not been adequately met, leading to challenges in the efficiency and effectiveness of relief activities. 【0159】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0160】 In this invention, the server includes emotion analysis means for analyzing the emotional state of disaster victims and setting priorities for psychological support; analysis means for improving support plans that take into account the emotional information of disaster victims; and information processing means for identifying the types and priorities of necessary resources based on support needs information. This makes it possible to simultaneously consider psychological and physical needs and to formulate and implement a rapid and accurate support plan. 【0161】 "Information gathering means" refers to devices and methods for collecting information on the need for assistance from disaster-stricken areas. 【0162】 "Information processing means" refers to devices or methods that analyze collected support needs information to determine the type and priority of resources required. 【0163】 A "matching means" is a device or method that has the function of selecting available external resources that correspond to identified required resources. 【0164】 "Means for generating plans" refers to devices or methods for creating plans to efficiently provide selected resources to disaster-stricken areas. 【0165】 "Information provision means" refers to devices and methods for notifying disaster victims and related parties based on the generated plan. 【0166】 "Improvement measures" refer to devices or methods that have the function of improving the entire system based on evaluation information received after supply. 【0167】 "Emotional analysis tools" refer to devices or methods for analyzing the emotional state of disaster victims and setting priorities for psychological support based on those analyses. 【0168】 "Analysis tools" refer to devices and methods for improving effective support plans by taking into account the emotional information of disaster victims. 【0169】 To implement this system, users will need to prepare terminals for use in disaster-stricken areas. These terminals will have an interface for inputting the supplies and support needed by disaster victims, as well as an emotion engine for sentiment analysis. This emotion engine will use a general-purpose software library for sentiment analysis. Specifically, Microsoft® Azure® Text Analytics API can be used. When a user inputs needs information into the terminal, such as "We are short on drinking water and food," the emotion engine will analyze this information and identify the emotional state. 【0170】 The server receives support need information and sentiment data collected from terminals and stores this data in a recording device for centralized management. Online storage services such as Amazon S3 are used for storage. The server further analyzes the data using machine learning techniques, such as those using Python. This analysis determines the priority of support needs and identifies the necessary support resources. Based on this information, the server creates a matching and allocation plan for external resources. 【0171】 For example, if a user enters "I'm very worried, it's terrible that I can't use the bath," the terminal analyzes this emotional data and sends it to the server with "worry" emphasized. Based on this information, the server plans the rapid provision of mobile bathing equipment and prepares to implement effective support. By utilizing generative AI models and machine learning algorithms, the data is updated in real time, improving the accuracy of the support plan. 【0172】 Examples of prompt messages include the following: 【0173】 "Please explain how to analyze the emotional state of disaster victims and prioritize the necessary support." 【0174】 "What is the most appropriate support to provide to disaster victims who are feeling anxious?" 【0175】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0176】 Step 1: 【0177】 Users in disaster-stricken areas input necessary support information through the terminal's input interface. Specifically, they register needs and emotions such as "There is a shortage of drinking water" or "My family is worried and restless." At that time, the emotion analysis engine installed in the terminal analyzes the text data and identifies the emotional state. The input in this process is the user's text information, and the output is the analyzed emotion data. 【0178】 Step 2: 【0179】 The device sends collected needs information and sentiment data to the server. The server receives this data and stores it in a database. This storage is done using, for example, cloud storage, enabling efficient data management. The input is the data sent from the device, and the output is the information stored in the database. 【0180】 Step 3: 【0181】 The server applies machine learning algorithms to analyze the stored information to determine support needs. This prioritizes the needs of disaster victims and categorizes the necessary relief supplies and psychological support. In this process, user-provided needs information and emotional data are used as input, and the analysis results output support items with high priority. 【0182】 Step 4: 【0183】 The server matches external resources with priority support items. This process gathers resources available from external sources and matches them with the needs in the disaster-stricken area. The input is analysis results and resource information, and the output is a specific allocation plan. 【0184】 Step 5: 【0185】 The server develops the final support plan and notifies stakeholders and disaster victims of this information. This involves sending the plan details using a notification system. The input is the allocation plan, and the output is a message notifying the progress of the plan. 【0186】 Step 6: 【0187】 After receiving assistance, users provide feedback using a device. They report whether the assistance was provided appropriately and what improvements can be made in the future. This provides the system with valuable input for further improvement. The input is user feedback data, and the output is insights for system improvement. 【0188】 (Application Example 2) 【0189】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0190】 In disaster relief efforts, it is essential to efficiently provide support that takes into account not only the supply of goods but also the emotional state of the victims. However, conventional systems make it difficult to grasp emotional states and do not prioritize support based on psychological urgency. As a result, the psychological needs of the victims are not adequately met, leading to a problem of limited effectiveness in relief efforts. 【0191】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0192】 In this invention, the server includes information gathering means for collecting information on the need for support and emotional state in the disaster-stricken area; analysis means for identifying the type, priority, and psychological urgency of necessary resources based on the information on the need for support and emotional state; and matching means for selecting available external resources corresponding to the identified resources and matching them while taking emotional state into consideration. This makes it possible to provide rapid and accurate support that is in line with the psychological state of disaster victims. 【0193】 "Support needs information" refers to information about the requests for supplies and services needed in disaster-stricken areas. 【0194】 "Emotional state information" refers to information that indicates the psychological state of individual disaster victims and is used to prioritize support. 【0195】 "Information gathering means" refers to the components of devices and systems used to collect information on the needs for assistance and emotional states of disaster victims. 【0196】 "Analysis methods" refer to means for analyzing collected support needs information and emotional state information to identify the type and priority of necessary resources and the level of psychological urgency. 【0197】 "Matching methods" refer to processes and technologies for selecting available external resources corresponding to identified resources and for performing appropriate matching while considering emotional state information. 【0198】 A "supply plan" refers to a plan for efficiently distributing necessary goods and services to disaster-stricken areas, and also includes arranging psychological support in accordance with information on emotional states. 【0199】 "Notification means" refers to methods and means of transmitting information to disaster victims and support personnel based on the supply plan. 【0200】 "Feedback methods" refer to means of collecting information to evaluate the situation after supply and to help improve the system. 【0201】 To realize this invention, a system is needed that links a server and terminals. The server is built on a cloud service platform such as AWS® or Google Cloud and uses a database that aggregates information on the support needs and emotional states of disaster victims. The terminals are mobile devices such as smartphones and tablets, and collect data on the needs and emotional states entered by disaster victims in real time. 【0202】 The terminal program uses Google Cloud's Natural Language API to analyze the sentiment of input data from disaster victims and sends that data to a database on AWS. On the server, a machine learning algorithm using TensorFlow analyzes this data to determine the priority of assistance and the level of psychological urgency. Based on the analysis results, the server generates a supply plan and matches it with appropriate external resources. During this process, the server notifies disaster victims and aid workers of the supply plan and arranges psychological support as needed. 【0203】 For example, if an elderly person enters "I feel a little anxious today, but I need a new blanket," the server will analyze this data to understand their feelings of anxiety and prioritize the supply of blankets while also considering arranging psychological support. In this way, it becomes possible to comprehensively meet the physical and psychological needs of disaster victims. 【0204】 An example of a prompt message is: "Analyze the mental state of the person who entered this text, and prioritize the necessary support based on that state." 【0205】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0206】 Step 1: 【0207】 The device receives information on the user's need for assistance and emotional state. The input data is sent to Google Cloud's Natural Language API for sentiment analysis. This process identifies emotions contained in the text and generates sentiment data. The output is the analyzed sentiment data. 【0208】 Step 2: 【0209】 The device sends support need information along with generated sentiment data to a database on AWS. The input is support need information and sentiment data collected from the user. The output is a notification that the data has been saved to the database. 【0210】 Step 3: 【0211】 The server retrieves support needs information and sentiment data from the database. Using this data, TensorFlow is employed to perform analysis to determine support priorities and psychological urgency. The input is information extracted from the database, and the output is a list of priorities and urgency levels. 【0212】 Step 4: 【0213】 The server generates a supply plan based on the analysis results. The supply plan reflects the psychological state of disaster victims and matches them with appropriate external resources. The supply plan is created using a generative AI model. The input is a list of priorities and urgency levels, and the output is the details of the supply plan. 【0214】 Step 5: 【0215】 The server generates a supply plan and notifies disaster victims and aid workers. The notification is sent to a terminal and clearly indicates the need for psychological support. The input is the supply plan, and the output is the notification. 【0216】 Step 6: 【0217】 Users input feedback about the assistance they received via a terminal. This feedback includes the suitability of the provided materials and their emotional satisfaction. The input is the feedback information, and the output is that information stored in the database. 【0218】 Step 7: 【0219】 The server integrates feedback information and performs analysis to help improve the system. TensorFlow is used again to analyze the feedback data and incorporate it into the next support plan. This feedback loop improves the accuracy of future support plans. The input is feedback information, and the output is an indicator of the improved system. 【0220】 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. 【0221】 Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0222】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0223】 [Second Embodiment] 【0224】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0225】 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. 【0226】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0227】 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. 【0228】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0229】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0230】 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. 【0231】 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 using the processor 28. The storage 32 stores the specific processing program 56. 【0232】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0233】 The 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. 【0234】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0235】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0236】 This invention is a system for effectively providing support in disaster-stricken areas when a large-scale disaster occurs. In this system, terminals implemented in each disaster-stricken area collect initial information, input and analyze user needs and circumstances, and transmit them to a server. 【0237】 Device features: 【0238】 The terminals allow disaster victims to input information about the supplies and support they need. For example, terminals are installed in each evacuation center, providing a system for users to report shortages of supplies, high-priority support, and special medical needs. The terminals instantly digitize the information gathered on-site and send it to a server, enabling real-time information sharing. 【0239】 Server functions: 【0240】 The server has the ability to receive information transmitted from terminals and analyze that data. The server uses machine learning algorithms to analyze and prioritize the needs of the disaster area. The server also matches information on resources available from external sources with the needs of the disaster area and decides which resources to send to which area. 【0241】 Furthermore, the server creates an effective supply plan and notifies the affected areas and relevant organizations via terminals. This enables swift and appropriate relief efforts. 【0242】 User roles: 【0243】 Users can report their needs via terminals located in evacuation centers. This includes the types of supplies needed, the current situation, the number of people, and any special requests. After receiving relief supplies, they can input feedback on the situation and their satisfaction level into the terminal and send it to the server to help plan future relief efforts. 【0244】 This system enables disaster-stricken areas and victims to receive necessary support in a timely manner, allowing for efficient responses even in chaotic situations. For example, if a disaster victim reports a shortage of water and food via a terminal, the server immediately analyzes the need, creates a plan for supplying water and food from external aid organizations, and instructs them to deliver the supplies quickly. 【0245】 The following describes the processing flow. 【0246】 Step 1: 【0247】 The terminal displays an interface for disaster victims to input needs information at evacuation centers. Users enter the supplies they need and the most urgent assistance they require into this interface. 【0248】 Step 2: 【0249】 The terminal analyzes the input data in real time and performs an initial assessment of its importance and urgency. The data, including the results of the initial assessment, is immediately sent to the server. 【0250】 Step 3: 【0251】 The server stores the data received from the terminal in a database and analyzes the collected needs information. The server uses machine learning algorithms to classify and prioritize support needs. 【0252】 Step 4: 【0253】 The server aggregates information on resources available from external sources and matches it with the needs of disaster victims. The server then uses a matching algorithm to select the most suitable suppliers and develop a supply plan for materials. 【0254】 Step 5: 【0255】 The server notifies relevant organizations and personnel in the disaster-stricken areas of the created supply plan via terminals, informing them of the expected arrival date of the supplies. 【0256】 Step 6: 【0257】 Based on the information provided, users receive relief supplies at the designated location and time. They confirm receipt of the supplies via their device and input feedback regarding the receipt status and any additional needs. 【0258】 Step 7: 【0259】 The server receives feedback from the terminals and analyzes the data to improve the accuracy of subsequent support activities. Based on the feedback information, it improves algorithms and supply plans as needed. 【0260】 (Example 1) 【0261】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0262】 Disaster relief efforts in affected areas require the rapid and accurate identification, supply, and prioritization of necessary goods and resources. However, currently, information gathering and analysis are inefficient, leading to problems with the timely provision of necessary support. As a result, disaster victims may not receive adequate assistance, potentially causing confusion. Therefore, this invention aims to realize the efficient and effective supply of goods in disaster-stricken areas. 【0263】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0264】 In this invention, the server includes a data input device, a data analysis device, and a calibration device. This enables the immediate collection of demand information in disaster-stricken areas, the determination of priorities, and the formulation of an optimal supply plan. 【0265】 A "data input device" is a device that receives information about necessary goods and support from users in disaster-stricken areas, digitizes it, and transmits it to a server. 【0266】 A "data analysis device" is a device that analyzes the demand information received from disaster-stricken areas and sets resource priorities using machine learning algorithms. 【0267】 A "fitting device" is a device that, based on analysis results, identifies available external resources and matches them to the demand in the most suitable way. 【0268】 A "distribution plan creation device" is a device that formulates a supply plan to optimally deliver selected resources to disaster-stricken areas and communicates that plan to relevant organizations and facilities. 【0269】 A "communication device" is a device that supports the efficient supply of goods by promptly notifying relevant parties of the formulated supply plan. 【0270】 An "evaluation information processing device" is a device that analyzes the evaluation information received after supply and uses it to improve the system in order to help with future support. 【0271】 This invention is a system for providing efficient support in the event of a large-scale disaster. The system mainly consists of terminals, servers, and users, with each element having its own role. 【0272】 Terminal role: 【0273】 The terminals are installed in disaster-stricken areas and function as an interface for users to input their individual needs. Users input information such as "water is scarce" or "food is needed" using, for example, a touchscreen or voice recognition software. The entered data is immediately digitized and transmitted to a server via the internet. The hardware used includes common tablets and smartphones, and the software includes a voice recognition engine. 【0274】 Server role: 【0275】 The server functions as a central processing unit that analyzes the received data. It uses TensorFlow, an open-source machine learning framework, to analyze the data and classify the needs of the disaster-stricken areas. Furthermore, the server uses external sensors and APIs to understand the local situation and create an optimal plan for supplying relief materials. Specifically, it uses the Google Maps API for geographical optimization. 【0276】 User roles: 【0277】 Users report their needs using a device and provide feedback after receiving assistance. This feedback is then sent back to the server and used as data to improve future assistance activities. For example, if feedback indicates that "food supplies at shelters are still insufficient," the next supply plan will be adjusted based on that feedback. 【0278】 Using a generative AI model, an example of a prompt might be, "Design a prototype application that analyzes the needs of disaster-stricken areas in real time and proposes the optimal support plan." Based on this prompt, the AI ​​model provides support for efficient data analysis and automatic generation of supply plans. 【0279】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0280】 Step 1: 【0281】 The terminal functions as an interface for receiving user input. The user enters their current needs into the terminal. This input data includes the type of item, quantity, urgency, etc. The terminal receives and digitizes this data via voice recognition or touchscreen. As output, the user's needs information is generated as data packets. 【0282】 Step 2: 【0283】 The terminal sends the generated data packets to the server. Internet-related technologies are utilized for the transmission. It receives the digital data generated from the terminal as input and converts it into an appropriate communication protocol. As output, it sends the data packets along the route to reach the server. 【0284】 Step 3: 【0285】 The server analyzes the data received from the terminal. It receives the digital data from the terminal as input. The server analyzes the data using machine learning algorithms, especially TensorFlow, and classifies and prioritizes each need. As output, a list of processed needs is generated. 【0286】 Step 4: 【0287】 Based on the analysis results, the server creates a suitable supply plan. It receives the list of classified needs as input. The server utilizes the Google Maps API and external resource information to formulate a plan for supplying the necessary items to the optimal locations. As output, a detailed supply plan is generated. 【0288】 Step 5: 【0289】 The server sends the generated supply plan to the terminal. It obtains the delivery plan from the data warehouse within the server as input. The server sends the plan as digital data in a format interpretable by the terminal. As output, a notification of the supply plan to the terminal is made. 【0290】 Step 6: 【0291】 The terminal displays the supply plan from the server and notifies the user and the shelter staff. It receives the digital data from the server as input. The terminal visually displays the data and notifies the plan by means such as voice alerts. As output, the display information of the plan is communicated to the user. 【0292】 Step 7: 【0293】 Users input feedback about the relief supplies and services they receive into a terminal. The terminal receives information such as satisfaction levels and areas for improvement as input. The terminal digitizes this information and sends it back to the server. The output is a data packet containing the improvement information. 【0294】 (Application Example 1) 【0295】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0296】 In the event of a large-scale disaster, there is a lack of mechanisms to quickly grasp the needs of affected areas and efficiently supply appropriate relief materials. Furthermore, traditional methods are time-consuming in gathering needs from victims, making it difficult to prioritize support, thus hindering rapid relief efforts. To address this problem, immediate data collection from the field and real-time resource supply planning are required. 【0297】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0298】 In this invention, the server includes information gathering means for collecting support needs information in disaster-stricken areas, means for users to report needs information along with local location information via a mobile terminal, and real-time processing means for analyzing information transmitted from the site and quickly formulating and executing a resource supply plan. This enables the immediate digitization of needs in disaster-stricken areas, real-time analysis, dynamic prioritization, and efficient supply of relief materials. 【0299】 "Information gathering means" refers to devices or methods for collecting information on the need for support in disaster-stricken areas, and is used to gather information on the needs of disaster victims. 【0300】 The "analysis means" is a device or method for determining the types of necessary resources and their priorities based on the collected support demand information. 【0301】 The "matching means" is a device or method for associating the identified resources with external resources that can be supplied. 【0302】 The "means for generating a supply plan" is a device or method for creating a plan for efficiently distributing the selected resources to the disaster area. 【0303】 The "notification means" is a device or method for transmitting the necessary information to the residents of the disaster area and related support groups based on the support plan. 【0304】 The "feedback means" is a device or method for receiving information on the status of the support materials after supply and improving the system. 【0305】 The "means that can be reported through a mobile terminal" is a device or method using a mobile device or application to enable disaster victims to easily report local location information and needs. 【0306】 The "real-time processing means" is a device or method for immediately analyzing the information transmitted from the site and quickly formulating and executing a resource supply plan. 【0307】 The system of the present invention is mainly realized through an information collection terminal, an analysis server, and a mobile terminal. The system is configured as follows. 【0308】 The information collection terminal is installed in the disaster area. This terminal can input information about the materials and services needed by the disaster victims. For example, a user interface is provided through a mobile terminal, and the user can report current needs using voice input or text input. Also, the GPS function is utilized to assign location information to identify the location where the input was made. 【0309】 The analysis server receives information transmitted from terminals in real time. The server efficiently stores data using cloud services such as Google Cloud Platform and analyzes the data using machine learning algorithms (e.g., TensorFlow). The analysis prioritizes the support needs of each region. Furthermore, it generates resource allocation plans and adjusts them to ensure optimal support is distributed. 【0310】 Users can use mobile devices such as smartphones and smart glasses to report their support needs via an application and provide feedback after receiving support. This allows for real-time support planning based on the latest information. 【0311】 As a concrete example, consider a case where residents of an area affected by heavy rain use an app to report food shortages. The information transmitted via mobile devices is immediately analyzed on a server, and a plan is created to prioritize the supply of necessary food. 【0312】 An example of a prompt is, "Explain how to analyze the information and develop a support plan when residents in flood-affected areas who need food supplies report their need for assistance using a smartphone app." Using prompts like this to the generative AI model helps to easily obtain specific support procedures and plans. 【0313】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0314】 Step 1: 【0315】 The terminal receives information about the needs of disaster victims as input. Users use their mobile devices to input location information and information about shortages of supplies, and the terminal collects this data. The input information is immediately formatted as digital data and becomes data to be sent to the server. 【0316】 Step 2: 【0317】 The terminal transmits the collected information to the server. The entered data includes the current situation of disaster victims, the supplies they need, and their location. The terminal then transfers the formatted digital data to the server via the internet. 【0318】 Step 3: 【0319】 The server receives data from terminals as input in real time. The server uses a Google Cloud Platform database to store the data and prepare it for analysis by machine learning algorithms. 【0320】 Step 4: 【0321】 The server performs analysis based on the received data. Specifically, it uses machine learning libraries such as TensorFlow to classify and prioritize needs. Through this analysis, it determines the highest priority for resource supply and then proceeds to the next processing step based on the results. 【0322】 Step 5: 【0323】 The server generates a resource supply plan using the analysis results. It uses a matching algorithm to correlate available resources with disaster-stricken areas in high need. Specifically, it develops a detailed plan specifying which resources to supply to which areas and in what quantities. 【0324】 Step 6: 【0325】 The server outputs the generated supply plan to relevant organizations and disaster victims through notification channels. The server distributes information using email and the notification function of a dedicated app, promptly informing relevant parties of the plan's contents. 【0326】 Step 7: 【0327】 Users can provide feedback again after receiving supplies. This feedback includes inputting information about their satisfaction with the supply and any additional needs. The terminal then sends this feedback information to the server. 【0328】 Step 8: 【0329】 The server receives feedback data and uses it to improve the system. By analyzing the received data, it helps improve the accuracy of future supply plans and needs forecasts. The server uses a generative AI model to continuously optimize support plans. 【0330】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0331】 This invention is a system for effectively carrying out support activities in disaster-stricken areas during large-scale disasters, and aims to provide support that takes into account the emotional state of the user. In particular, this system enables the effective collection of the urgent needs of disaster victims and the improvement of delivery plans. 【0332】 Device features: 【0333】 The terminal is equipped with an interface for inputting disaster victims' needs information, allowing users to easily report the supplies they lack and the assistance they need. Furthermore, the terminal has an emotion engine that recognizes emotions from the user's input data. For example, if a user expresses emotions such as worry or fear, the emotion engine analyzes that information and makes a judgment. This emotional information reflects not only the need for supplies but also the psychological urgency the user is experiencing. 【0334】 Server functions: 【0335】 The server receives needs information and sentiment data sent from terminals and integrates it into a database. Using machine learning algorithms, the server designs more adaptive resource allocation by reflecting the user's sentiment data in the priority of assistance. For example, if the sentiment data indicates that many disaster victims are feeling anxious, the server will develop a plan to expedite assistance to that area. 【0336】 The server also aggregates available resources from external support organizations and companies and matches them with disaster relief needs. In this process, the server utilizes data obtained from the emotion engine to determine whether psychological support should be prioritized. 【0337】 User roles: 【0338】 Users input the expected support and provide emotional feedback through their device. This includes detailed feedback, including emotions, on whether the received support supplies were appropriate and how to incorporate those points into future support plans. 【0339】 This system configuration enables accurate support in disaster-stricken areas, including addressing users' inner needs, and facilitates a swift and effective disaster response. For example, if a disaster victim expresses the emotion of "worry," the system prioritizes processing that need, providing emotional support and promptly supplying necessary materials. 【0340】 The following describes the processing flow. 【0341】 Step 1: 【0342】 The terminal provides an interface for disaster victims to input their needs for supplies and assistance. Users input the supplies they lack and their specific requests on the terminal, and select options that represent their emotional state (e.g., reassured, anxious, frightened). 【0343】 Step 2: 【0344】 The device acquires information entered by the user as digital data and analyzes the user's emotional state using its built-in emotion engine. Needs data, including the analysis results, is then generated. 【0345】 Step 3: 【0346】 The device transmits generated needs data and emotion data to the server in real time. Location and time information are also included in the data. 【0347】 Step 4: 【0348】 The server stores the data received from the terminals in a database and aggregates information on the needs and emotions of each affected area. 【0349】 Step 5: 【0350】 The server analyzes the collected data using machine learning algorithms to reassess the prioritization and urgency of needs. It takes emotional data into consideration, paying particular attention to areas where psychological support is needed. 【0351】 Step 6: 【0352】 The server matches externally available resource information with the needs of the affected area and develops an optimal support plan based on emotional data. This plan includes the prioritization of supplying materials and additional psychological support. 【0353】 Step 7: 【0354】 The server notifies aid organizations and disaster relief personnel in various locations of the formulated supply plan via terminals. The plan includes estimated arrival dates for supplies and details of psychological support. 【0355】 Step 8: 【0356】 Users can check the status of the support provided through their device and input feedback, including their feelings, about the received supplies and psychological support. 【0357】 Step 9: 【0358】 The terminal sends user feedback to the server, which uses this data to improve future support plans. Emotional data is continuously analyzed to contribute to overall system improvement. 【0359】 (Example 2) 【0360】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0361】 In disaster relief efforts, it is crucial to effectively and quickly identify the needs for supplies and services and to provide support based on priorities. However, conventional methods have made it difficult to develop support plans that take into account the emotional state of disaster victims, and have failed to adequately assess the need for psychological support. As a result, the physical and psychological needs of disaster victims have not been adequately met, leading to challenges in the efficiency and effectiveness of relief activities. 【0362】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0363】 In this invention, the server includes emotion analysis means for analyzing the emotional state of disaster victims and setting priorities for psychological support; analysis means for improving support plans that take into account the emotional information of disaster victims; and information processing means for identifying the types and priorities of necessary resources based on support needs information. This makes it possible to simultaneously consider psychological and physical needs and to formulate and implement a rapid and accurate support plan. 【0364】 "Information gathering means" refers to devices and methods for collecting information on the need for assistance from disaster-stricken areas. 【0365】 "Information processing means" refers to devices or methods that analyze collected support needs information to determine the type and priority of resources required. 【0366】 A "matching means" is a device or method that has the function of selecting available external resources that correspond to identified required resources. 【0367】 "Means for generating plans" refers to devices or methods for creating plans to efficiently provide selected resources to disaster-stricken areas. 【0368】 "Information provision means" refers to devices and methods for notifying disaster victims and related parties based on the generated plan. 【0369】 "Improvement measures" refer to devices or methods that have the function of improving the entire system based on evaluation information received after supply. 【0370】 "Emotional analysis tools" refer to devices or methods for analyzing the emotional state of disaster victims and setting priorities for psychological support based on those analyses. 【0371】 "Analysis tools" refer to devices and methods for improving effective support plans by taking into account the emotional information of disaster victims. 【0372】 To implement this system, users will need to prepare terminals for use in disaster-stricken areas. These terminals will have an interface for inputting the supplies and support needed by disaster victims, as well as an emotion engine for sentiment analysis. This emotion engine will use a general-purpose software library for sentiment analysis. Specifically, Microsoft Azure's Text Analytics API can be used. When a user inputs needs information into the terminal, such as "We are short on drinking water and food," the emotion engine will analyze this information and identify the emotional state. 【0373】 The server receives support need information and sentiment data collected from terminals and stores this data in a recording device for centralized management. Online storage services such as Amazon S3 are used for storage. The server further analyzes the data using machine learning techniques, such as those using Python. This analysis determines the priority of support needs and identifies the necessary support resources. Based on this information, the server creates a matching and allocation plan for external resources. 【0374】 For example, if a user enters "I'm very worried, it's terrible that I can't use the bath," the terminal analyzes this emotional data and sends it to the server with "worry" emphasized. Based on this information, the server plans the rapid provision of mobile bathing equipment and prepares to implement effective support. By utilizing generative AI models and machine learning algorithms, the data is updated in real time, improving the accuracy of the support plan. 【0375】 Examples of prompt messages include the following: 【0376】 "Please explain how to analyze the emotional state of disaster victims and prioritize the necessary support." 【0377】 "What is the most appropriate support to provide to disaster victims who are feeling anxious?" 【0378】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0379】 Step 1: 【0380】 Users in disaster-stricken areas input necessary support information through the terminal's input interface. Specifically, they register needs and emotions such as "There is a shortage of drinking water" or "My family is worried and restless." At that time, the emotion analysis engine installed in the terminal analyzes the text data and identifies the emotional state. The input in this process is the user's text information, and the output is the analyzed emotion data. 【0381】 Step 2: 【0382】 The device sends collected needs information and sentiment data to the server. The server receives this data and stores it in a database. This storage is done using, for example, cloud storage, enabling efficient data management. The input is the data sent from the device, and the output is the information stored in the database. 【0383】 Step 3: 【0384】 The server applies machine learning algorithms to analyze the stored information to determine support needs. This prioritizes the needs of disaster victims and categorizes the necessary relief supplies and psychological support. In this process, user-provided needs information and emotional data are used as input, and the analysis results output support items with high priority. 【0385】 Step 4: 【0386】 The server matches external resources with priority support items. This process gathers resources available from external sources and matches them with the needs in the disaster-stricken area. The input is analysis results and resource information, and the output is a specific allocation plan. 【0387】 Step 5: 【0388】 The server develops the final support plan and notifies stakeholders and disaster victims of this information. This involves sending the plan details using a notification system. The input is the allocation plan, and the output is a message notifying the progress of the plan. 【0389】 Step 6: 【0390】 After receiving assistance, users provide feedback using a device. They report whether the assistance was provided appropriately and what improvements can be made in the future. This provides the system with valuable input for further improvement. The input is user feedback data, and the output is insights for system improvement. 【0391】 (Application Example 2) 【0392】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal". 【0393】 In disaster relief efforts, it is essential to efficiently provide support that takes into account not only the supply of goods but also the emotional state of the victims. However, conventional systems make it difficult to grasp emotional states and do not prioritize support based on psychological urgency. As a result, the psychological needs of the victims are not adequately met, leading to a problem of limited effectiveness in relief efforts. 【0394】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0395】 In this invention, the server includes information gathering means for collecting information on the need for support and emotional state in the disaster-stricken area; analysis means for identifying the type, priority, and psychological urgency of necessary resources based on the information on the need for support and emotional state; and matching means for selecting available external resources corresponding to the identified resources and matching them while taking emotional state into consideration. This makes it possible to provide rapid and accurate support that is in line with the psychological state of disaster victims. 【0396】 "Support needs information" refers to information about the requests for supplies and services needed in disaster-stricken areas. 【0397】 "Emotional state information" refers to information that indicates the psychological state of individual disaster victims and is used to prioritize support. 【0398】 "Information gathering means" refers to the components of devices and systems used to collect information on the needs for assistance and emotional states of disaster victims. 【0399】 "Analysis methods" refer to means for analyzing collected support needs information and emotional state information to identify the type and priority of necessary resources and the level of psychological urgency. 【0400】 "Matching methods" refer to processes and technologies for selecting available external resources corresponding to identified resources and for performing appropriate matching while considering emotional state information. 【0401】 A "supply plan" refers to a plan for efficiently distributing necessary goods and services to disaster-stricken areas, and also includes arranging psychological support in accordance with information on emotional states. 【0402】 "Notification means" refers to methods and means of transmitting information to disaster victims and support personnel based on the supply plan. 【0403】 "Feedback methods" refer to means of collecting information to evaluate the situation after supply and to help improve the system. 【0404】 To realize this invention, a system is needed that links a server and terminals. The server is built on a cloud service platform such as AWS or Google Cloud and uses a database that aggregates information on the support needs and emotional state of disaster victims. The terminals are mobile devices such as smartphones and tablets, and collect data on the needs and emotional state entered by disaster victims in real time. 【0405】 The terminal program uses Google Cloud's Natural Language API to analyze the sentiment of input data from disaster victims and sends that data to a database on AWS. On the server, a machine learning algorithm using TensorFlow analyzes this data to determine the priority of assistance and the level of psychological urgency. Based on the analysis results, the server generates a supply plan and matches it with appropriate external resources. During this process, the server notifies disaster victims and aid workers of the supply plan and arranges psychological support as needed. 【0406】 For example, if an elderly person enters "I feel a little anxious today, but I need a new blanket," the server will analyze this data to understand their feelings of anxiety and prioritize the supply of blankets while also considering arranging psychological support. In this way, it becomes possible to comprehensively meet the physical and psychological needs of disaster victims. 【0407】 An example of a prompt message is: "Analyze the mental state of the person who entered this text, and prioritize the necessary support based on that state." 【0408】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0409】 Step 1: 【0410】 The device receives information on the user's need for assistance and emotional state. The input data is sent to Google Cloud's Natural Language API for sentiment analysis. This process identifies emotions contained in the text and generates sentiment data. The output is the analyzed sentiment data. 【0411】 Step 2: 【0412】 The device sends support need information along with generated sentiment data to a database on AWS. The input is support need information and sentiment data collected from the user. The output is a notification that the data has been saved to the database. 【0413】 Step 3: 【0414】 The server retrieves support needs information and sentiment data from the database. Using this data, TensorFlow is employed to perform analysis to determine support priorities and psychological urgency. The input is information extracted from the database, and the output is a list of priorities and urgency levels. 【0415】 Step 4: 【0416】 The server generates a supply plan based on the analysis results. The supply plan reflects the psychological state of disaster victims and matches them with appropriate external resources. The supply plan is created using a generative AI model. The input is a list of priorities and urgency levels, and the output is the details of the supply plan. 【0417】 Step 5: 【0418】 The server generates a supply plan and notifies disaster victims and aid workers. The notification is sent to a terminal and clearly indicates the need for psychological support. The input is the supply plan, and the output is the notification. 【0419】 Step 6: 【0420】 Users input feedback about the assistance they received via a terminal. This feedback includes the suitability of the provided materials and their emotional satisfaction. The input is the feedback information, and the output is that information stored in the database. 【0421】 Step 7: 【0422】 The server integrates feedback information and performs analysis to help improve the system. TensorFlow is used again to analyze the feedback data and incorporate it into the next support plan. This feedback loop improves the accuracy of future support plans. The input is feedback information, and the output is an indicator of the improved system. 【0423】 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. 【0424】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0425】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0426】 [Third Embodiment] 【0427】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0428】 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. 【0429】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0430】 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. 【0431】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0432】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0433】 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. 【0434】 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. 【0435】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0436】 The 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. 【0437】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0438】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0439】 This invention is a system for effectively providing support in disaster-stricken areas when a large-scale disaster occurs. In this system, terminals implemented in each disaster-stricken area collect initial information, input and analyze user needs and circumstances, and transmit them to a server. 【0440】 Device features: 【0441】 The terminals allow disaster victims to input information about the supplies and support they need. For example, terminals are installed in each evacuation center, providing a system for users to report shortages of supplies, high-priority support, and special medical needs. The terminals instantly digitize the information gathered on-site and send it to a server, enabling real-time information sharing. 【0442】 Server functions: 【0443】 The server has the ability to receive information transmitted from terminals and analyze that data. The server uses machine learning algorithms to analyze and prioritize the needs of the disaster area. The server also matches information on resources available from external sources with the needs of the disaster area and decides which resources to send to which area. 【0444】 Furthermore, the server creates an effective supply plan and notifies the affected areas and relevant organizations via terminals. This enables swift and appropriate relief efforts. 【0445】 User roles: 【0446】 Users can report their needs via terminals located in evacuation centers. This includes the types of supplies needed, the current situation, the number of people, and any special requests. After receiving relief supplies, they can input feedback on the situation and their satisfaction level into the terminal and send it to the server to help plan future relief efforts. 【0447】 This system enables disaster-stricken areas and victims to receive necessary support in a timely manner, allowing for efficient responses even in chaotic situations. For example, if a disaster victim reports a shortage of water and food via a terminal, the server immediately analyzes the need, creates a plan for supplying water and food from external aid organizations, and instructs them to deliver the supplies quickly. 【0448】 The following describes the processing flow. 【0449】 Step 1: 【0450】 The terminal displays an interface for disaster victims to input needs information at evacuation centers. Users enter the supplies they need and the most urgent assistance they require into this interface. 【0451】 Step 2: 【0452】 The terminal analyzes the input data in real time and performs an initial assessment of its importance and urgency. The data, including the results of the initial assessment, is immediately sent to the server. 【0453】 Step 3: 【0454】 The server stores the data received from the terminal in a database and analyzes the collected needs information. The server uses machine learning algorithms to classify and prioritize support needs. 【0455】 Step 4: 【0456】 The server aggregates information on resources available from external sources and matches it with the needs of disaster victims. The server then uses a matching algorithm to select the most suitable suppliers and develop a supply plan for materials. 【0457】 Step 5: 【0458】 The server notifies relevant organizations and personnel in the disaster-stricken areas of the created supply plan via terminals, informing them of the expected arrival date of the supplies. 【0459】 Step 6: 【0460】 Based on the information provided, users receive relief supplies at the designated location and time. They confirm receipt of the supplies via their device and input feedback regarding the receipt status and any additional needs. 【0461】 Step 7: 【0462】 The server receives feedback from the terminals and analyzes the data to improve the accuracy of subsequent support activities. Based on the feedback information, it improves algorithms and supply plans as needed. 【0463】 (Example 1) 【0464】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0465】 Disaster relief efforts in affected areas require the rapid and accurate identification, supply, and prioritization of necessary goods and resources. However, currently, information gathering and analysis are inefficient, leading to problems with the timely provision of necessary support. As a result, disaster victims may not receive adequate assistance, potentially causing confusion. Therefore, this invention aims to realize the efficient and effective supply of goods in disaster-stricken areas. 【0466】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0467】 In this invention, the server includes a data input device, a data analysis device, and a calibration device. This enables the immediate collection of demand information in disaster-stricken areas, the determination of priorities, and the formulation of an optimal supply plan. 【0468】 A "data input device" is a device that receives information about necessary goods and support from users in disaster-stricken areas, digitizes it, and transmits it to a server. 【0469】 A "data analysis device" is a device that analyzes the demand information received from disaster-stricken areas and sets resource priorities using machine learning algorithms. 【0470】 A "fitting device" is a device that, based on analysis results, identifies available external resources and matches them to the demand in the most suitable way. 【0471】 A "distribution plan creation device" is a device that formulates a supply plan to optimally deliver selected resources to disaster-stricken areas and communicates that plan to relevant organizations and facilities. 【0472】 A "communication device" is a device that supports the efficient supply of goods by promptly notifying relevant parties of the formulated supply plan. 【0473】 An "evaluation information processing device" is a device that analyzes the evaluation information received after supply and uses it to improve the system in order to help with future support. 【0474】 This invention is a system for providing efficient support in the event of a large-scale disaster. The system mainly consists of terminals, servers, and users, with each element having its own role. 【0475】 Terminal role: 【0476】 The terminals are installed in disaster-stricken areas and function as an interface for users to input their individual needs. Users input information such as "water is scarce" or "food is needed" using, for example, a touchscreen or voice recognition software. The entered data is immediately digitized and transmitted to a server via the internet. The hardware used includes common tablets and smartphones, and the software includes a voice recognition engine. 【0477】 Server role: 【0478】 The server functions as a central processing unit that analyzes the received data. It uses TensorFlow, an open-source machine learning framework, to analyze the data and classify the needs of the disaster-stricken areas. Furthermore, the server uses external sensors and APIs to understand the local situation and create an optimal plan for supplying relief materials. Specifically, it uses the Google Maps API for geographical optimization. 【0479】 User roles: 【0480】 Users report their needs using a device and provide feedback after receiving assistance. This feedback is then sent back to the server and used as data to improve future assistance activities. For example, if feedback indicates that "food supplies at shelters are still insufficient," the next supply plan will be adjusted based on that feedback. 【0481】 Using a generative AI model, an example of a prompt might be, "Design a prototype application that analyzes the needs of disaster-stricken areas in real time and proposes the optimal support plan." Based on this prompt, the AI ​​model provides support for efficient data analysis and automatic generation of supply plans. 【0482】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0483】 Step 1: 【0484】 The terminal functions as an interface for receiving user input. The user enters their current needs into the terminal. This input data includes the type of item, quantity, urgency, etc. The terminal receives and digitizes this data via voice recognition or touchscreen. As output, the user's needs information is generated as data packets. 【0485】 Step 2: 【0486】 The terminal sends the generated data packets to the server. Internet-related technologies are used for transmission. The terminal receives digital data generated from the terminal as input and converts it into the appropriate communication protocol. As output, it sends data packets along the path to reach the server. 【0487】 Step 3: 【0488】 The server analyzes the data received from the terminal. It receives digital data from the terminal as input. The server analyzes the data using machine learning algorithms, particularly TensorFlow, to classify and prioritize each need. A list of processed needs is generated as output. 【0489】 Step 4: 【0490】 The server creates a suitable supply plan based on the analysis results. It receives a list of categorized needs as input. The server utilizes the Google Maps API and external resource information to plan the delivery of necessary items to the optimal locations. A detailed supply plan is generated as output. 【0491】 Step 5: 【0492】 The server sends the generated supply plan to the terminal. As input, it retrieves the delivery plan from its data repository. The server sends the plan as digital data in a format the terminal can interpret. As output, the terminal receives a notification of the supply plan. 【0493】 Step 6: 【0494】 The terminal displays the supply plan from the server and notifies users and shelter staff. It receives digital data from the server as input. The terminal visually displays this data and notifies users of the plan through means such as audio alerts. The displayed plan information is transmitted to the user as output. 【0495】 Step 7: 【0496】 Users input feedback about the relief supplies and services they receive into a terminal. The terminal receives information such as satisfaction levels and areas for improvement as input. The terminal digitizes this information and sends it back to the server. The output is a data packet containing the improvement information. 【0497】 (Application Example 1) 【0498】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0499】 In the event of a large-scale disaster, there is a lack of mechanisms to quickly grasp the needs of affected areas and efficiently supply appropriate relief materials. Furthermore, traditional methods are time-consuming in gathering needs from victims, making it difficult to prioritize support, thus hindering rapid relief efforts. To address this problem, immediate data collection from the field and real-time resource supply planning are required. 【0500】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0501】 In this invention, the server includes information gathering means for collecting support needs information in disaster-stricken areas, means for users to report needs information along with local location information via a mobile terminal, and real-time processing means for analyzing information transmitted from the site and quickly formulating and executing a resource supply plan. This enables the immediate digitization of needs in disaster-stricken areas, real-time analysis, dynamic prioritization, and efficient supply of relief materials. 【0502】 "Information gathering means" refers to devices or methods for collecting information on the need for support in disaster-stricken areas, and is used to gather information on the needs of disaster victims. 【0503】 "Analysis means" refers to an apparatus or method for determining the types of resources needed and their priorities based on collected support needs information. 【0504】 "Matching means" refers to a device or method for associating a specified resource with an external resource that can supply it. 【0505】 "Means for generating supply plans" refers to an apparatus or method for creating a plan for efficiently distributing selected resources to disaster-stricken areas. 【0506】 "Notification means" refers to a device or method for conveying necessary information to residents of the disaster-stricken area and relevant support organizations based on the support plan. 【0507】 A "feedback mechanism" is a device or method that receives information about the status of relief supplies after they have been supplied and uses that information to improve the system. 【0508】 "Means of reporting via mobile devices" refers to devices or methods using mobile devices or applications that enable disaster victims to easily report their local location and needs. 【0509】 A "real-time processing means" is a device or method for immediately analyzing information transmitted from the site and quickly formulating and executing a resource supply plan. 【0510】 The system of the present invention is primarily implemented through an information collection terminal, an analysis server, and a mobile terminal. The system is configured as follows: 【0511】 Information gathering terminals are installed in disaster-stricken areas. These terminals allow disaster victims to input information about the supplies and services they need. For example, a user interface is provided via a mobile device, allowing users to report their current needs using voice or text input. GPS functionality is also used to identify the location where the input was made, by adding location information. 【0512】 The analysis server receives information transmitted from terminals in real time. The server efficiently stores data using cloud services such as Google Cloud Platform and analyzes the data using machine learning algorithms (e.g., TensorFlow). The analysis prioritizes the support needs of each region. Furthermore, it generates resource allocation plans and adjusts them to ensure optimal support is distributed. 【0513】 Users can use mobile devices such as smartphones and smart glasses to report their support needs via an application and provide feedback after receiving support. This allows for real-time support planning based on the latest information. 【0514】 As a concrete example, consider a case where residents of an area affected by heavy rain use an app to report food shortages. The information transmitted via mobile devices is immediately analyzed on a server, and a plan is created to prioritize the supply of necessary food. 【0515】 An example of a prompt is, "Explain how to analyze the information and develop a support plan when residents in flood-affected areas who need food supplies report their need for assistance using a smartphone app." Using prompts like this to the generative AI model helps to easily obtain specific support procedures and plans. 【0516】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0517】 Step 1: 【0518】 The terminal receives information about the needs of disaster victims as input. Users use their mobile devices to input location information and information about shortages of supplies, and the terminal collects this data. The input information is immediately formatted as digital data and becomes data to be sent to the server. 【0519】 Step 2: 【0520】 The terminal transmits the collected information to the server. The entered data includes the current situation of disaster victims, the supplies they need, and their location. The terminal then transfers the formatted digital data to the server via the internet. 【0521】 Step 3: 【0522】 The server receives data from terminals as input in real time. The server uses a Google Cloud Platform database to store the data and prepare it for analysis by machine learning algorithms. 【0523】 Step 4: 【0524】 The server performs analysis based on the received data. Specifically, it uses machine learning libraries such as TensorFlow to classify and prioritize needs. Through this analysis, it determines the highest priority for resource supply and then proceeds to the next processing step based on the results. 【0525】 Step 5: 【0526】 The server generates a resource supply plan using the analysis results. It uses a matching algorithm to correlate available resources with disaster-stricken areas in high need. Specifically, it develops a detailed plan specifying which resources to supply to which areas and in what quantities. 【0527】 Step 6: 【0528】 The server outputs the generated supply plan to relevant organizations and disaster victims through notification channels. The server distributes information using email and the notification function of a dedicated app, promptly informing relevant parties of the plan's contents. 【0529】 Step 7: 【0530】 Users can provide feedback again after receiving supplies. This feedback includes inputting information about their satisfaction with the supply and any additional needs. The terminal then sends this feedback information to the server. 【0531】 Step 8: 【0532】 The server receives feedback data and uses it to improve the system. By analyzing the received data, it helps improve the accuracy of future supply plans and needs forecasts. The server uses a generative AI model to continuously optimize support plans. 【0533】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0534】 This invention is a system for effectively carrying out support activities in disaster-stricken areas during large-scale disasters, and aims to provide support that takes into account the emotional state of the user. In particular, this system enables the effective collection of the urgent needs of disaster victims and the improvement of delivery plans. 【0535】 Device features: 【0536】 The terminal is equipped with an interface for inputting disaster victims' needs information, allowing users to easily report the supplies they lack and the assistance they need. Furthermore, the terminal has an emotion engine that recognizes emotions from the user's input data. For example, if a user expresses emotions such as worry or fear, the emotion engine analyzes that information and makes a judgment. This emotional information reflects not only the need for supplies but also the psychological urgency the user is experiencing. 【0537】 Server functions: 【0538】 The server receives needs information and sentiment data sent from terminals and integrates it into a database. Using machine learning algorithms, the server designs more adaptive resource allocation by reflecting the user's sentiment data in the priority of assistance. For example, if the sentiment data indicates that many disaster victims are feeling anxious, the server will develop a plan to expedite assistance to that area. 【0539】 The server also aggregates available resources from external support organizations and companies and matches them with disaster relief needs. In this process, the server utilizes data obtained from the emotion engine to determine whether psychological support should be prioritized. 【0540】 User roles: 【0541】 Users input the expected support and provide emotional feedback through their device. This includes detailed feedback, including emotions, on whether the received support supplies were appropriate and how to incorporate those points into future support plans. 【0542】 This system configuration enables accurate support in disaster-stricken areas, including addressing users' inner needs, and facilitates a swift and effective disaster response. For example, if a disaster victim expresses the emotion of "worry," the system prioritizes processing that need, providing emotional support and promptly supplying necessary materials. 【0543】 The following describes the processing flow. 【0544】 Step 1: 【0545】 The terminal provides an interface for disaster victims to input their needs for supplies and assistance. Users input the supplies they lack and their specific requests on the terminal, and select options that represent their emotional state (e.g., reassured, anxious, frightened). 【0546】 Step 2: 【0547】 The device acquires information entered by the user as digital data and analyzes the user's emotional state using its built-in emotion engine. Needs data, including the analysis results, is then generated. 【0548】 Step 3: 【0549】 The device transmits generated needs data and emotion data to the server in real time. Location and time information are also included in the data. 【0550】 Step 4: 【0551】 The server stores the data received from the terminals in a database and aggregates information on the needs and emotions of each affected area. 【0552】 Step 5: 【0553】 The server analyzes the collected data using machine learning algorithms to reassess the prioritization and urgency of needs. It takes emotional data into consideration, paying particular attention to areas where psychological support is needed. 【0554】 Step 6: 【0555】 The server matches externally available resource information with the needs of the affected area and develops an optimal support plan based on emotional data. This plan includes the prioritization of supplying materials and additional psychological support. 【0556】 Step 7: 【0557】 The server notifies aid organizations and disaster relief personnel in various locations of the formulated supply plan via terminals. The plan includes estimated arrival dates for supplies and details of psychological support. 【0558】 Step 8: 【0559】 Users can check the status of the support provided through their device and input feedback, including their feelings, about the received supplies and psychological support. 【0560】 Step 9: 【0561】 The terminal sends user feedback to the server, which uses this data to improve future support plans. Emotional data is continuously analyzed to contribute to overall system improvement. 【0562】 (Example 2) 【0563】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0564】 In disaster relief efforts, it is crucial to effectively and quickly identify the needs for supplies and services and to provide support based on priorities. However, conventional methods have made it difficult to develop support plans that take into account the emotional state of disaster victims, and have failed to adequately assess the need for psychological support. As a result, the physical and psychological needs of disaster victims have not been adequately met, leading to challenges in the efficiency and effectiveness of relief activities. 【0565】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0566】 In this invention, the server includes emotion analysis means for analyzing the emotional state of disaster victims and setting priorities for psychological support; analysis means for improving support plans that take into account the emotional information of disaster victims; and information processing means for identifying the types and priorities of necessary resources based on support needs information. This makes it possible to simultaneously consider psychological and physical needs and to formulate and implement a rapid and accurate support plan. 【0567】 "Information gathering means" refers to devices and methods for collecting information on the need for assistance from disaster-stricken areas. 【0568】 "Information processing means" refers to devices or methods that analyze collected support needs information to determine the type and priority of resources required. 【0569】 A "matching means" is a device or method that has the function of selecting available external resources that correspond to identified required resources. 【0570】 "Means for generating plans" refers to devices or methods for creating plans to efficiently provide selected resources to disaster-stricken areas. 【0571】 "Information provision means" refers to devices and methods for notifying disaster victims and related parties based on the generated plan. 【0572】 "Improvement measures" refer to devices or methods that have the function of improving the entire system based on evaluation information received after supply. 【0573】 "Emotional analysis tools" refer to devices or methods for analyzing the emotional state of disaster victims and setting priorities for psychological support based on those analyses. 【0574】 "Analysis tools" refer to devices and methods for improving effective support plans by taking into account the emotional information of disaster victims. 【0575】 To implement this system, users will need to prepare terminals for use in disaster-stricken areas. These terminals will have an interface for inputting the supplies and support needed by disaster victims, as well as an emotion engine for sentiment analysis. This emotion engine will use a general-purpose software library for sentiment analysis. Specifically, Microsoft Azure's Text Analytics API can be used. When a user inputs needs information into the terminal, such as "We are short on drinking water and food," the emotion engine will analyze this information and identify the emotional state. 【0576】 The server receives support need information and sentiment data collected from terminals and stores this data in a recording device for centralized management. Online storage services such as Amazon S3 are used for storage. The server further analyzes the data using machine learning techniques, such as those using Python. This analysis determines the priority of support needs and identifies the necessary support resources. Based on this information, the server creates a matching and allocation plan for external resources. 【0577】 For example, if a user enters "I'm very worried, it's terrible that I can't use the bath," the terminal analyzes this emotional data and sends it to the server with "worry" emphasized. Based on this information, the server plans the rapid provision of mobile bathing equipment and prepares to implement effective support. By utilizing generative AI models and machine learning algorithms, the data is updated in real time, improving the accuracy of the support plan. 【0578】 Examples of prompt messages include the following: 【0579】 "Please explain how to analyze the emotional state of disaster victims and prioritize the necessary support." 【0580】 "What is the most appropriate support to provide to disaster victims who are feeling anxious?" 【0581】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0582】 Step 1: 【0583】 Users in disaster-stricken areas input necessary support information through the terminal's input interface. Specifically, they register needs and emotions such as "There is a shortage of drinking water" or "My family is worried and restless." At that time, the emotion analysis engine installed in the terminal analyzes the text data and identifies the emotional state. The input in this process is the user's text information, and the output is the analyzed emotion data. 【0584】 Step 2: 【0585】 The device sends collected needs information and sentiment data to the server. The server receives this data and stores it in a database. This storage is done using, for example, cloud storage, enabling efficient data management. The input is the data sent from the device, and the output is the information stored in the database. 【0586】 Step 3: 【0587】 The server applies machine learning algorithms to analyze the stored information to determine support needs. This prioritizes the needs of disaster victims and categorizes the necessary relief supplies and psychological support. In this process, user-provided needs information and emotional data are used as input, and the analysis results output support items with high priority. 【0588】 Step 4: 【0589】 The server matches external resources with priority support items. This process gathers resources available from external sources and matches them with the needs in the disaster-stricken area. The input is analysis results and resource information, and the output is a specific allocation plan. 【0590】 Step 5: 【0591】 The server develops the final support plan and notifies stakeholders and disaster victims of this information. This involves sending the plan details using a notification system. The input is the allocation plan, and the output is a message notifying the progress of the plan. 【0592】 Step 6: 【0593】 After receiving assistance, users provide feedback using a device. They report whether the assistance was provided appropriately and what improvements can be made in the future. This provides the system with valuable input for further improvement. The input is user feedback data, and the output is insights for system improvement. 【0594】 (Application Example 2) 【0595】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0596】 In disaster relief efforts, it is essential to efficiently provide support that takes into account not only the supply of goods but also the emotional state of the victims. However, conventional systems make it difficult to grasp emotional states and do not prioritize support based on psychological urgency. As a result, the psychological needs of the victims are not adequately met, leading to a problem of limited effectiveness in relief efforts. 【0597】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0598】 In this invention, the server includes information gathering means for collecting information on the need for support and emotional state in the disaster-stricken area; analysis means for identifying the type, priority, and psychological urgency of necessary resources based on the information on the need for support and emotional state; and matching means for selecting available external resources corresponding to the identified resources and matching them while taking emotional state into consideration. This makes it possible to provide rapid and accurate support that is in line with the psychological state of disaster victims. 【0599】 "Support needs information" refers to information about the requests for supplies and services needed in disaster-stricken areas. 【0600】 "Emotional state information" refers to information that indicates the psychological state of individual disaster victims and is used to prioritize support. 【0601】 "Information gathering means" refers to the components of devices and systems used to collect information on the needs for assistance and emotional states of disaster victims. 【0602】 "Analysis methods" refer to means for analyzing collected support needs information and emotional state information to identify the type and priority of necessary resources and the level of psychological urgency. 【0603】 "Matching methods" refer to processes and technologies for selecting available external resources corresponding to identified resources and for performing appropriate matching while considering emotional state information. 【0604】 A "supply plan" refers to a plan for efficiently distributing necessary goods and services to disaster-stricken areas, and also includes arranging psychological support in accordance with information on emotional states. 【0605】 "Notification means" refers to methods and means of transmitting information to disaster victims and support personnel based on the supply plan. 【0606】 "Feedback methods" refer to means of collecting information to evaluate the situation after supply and to help improve the system. 【0607】 To realize this invention, a system is needed that links a server and terminals. The server is built on a cloud service platform such as AWS or Google Cloud and uses a database that aggregates information on the support needs and emotional state of disaster victims. The terminals are mobile devices such as smartphones and tablets, and collect data on the needs and emotional state entered by disaster victims in real time. 【0608】 The terminal program uses Google Cloud's Natural Language API to analyze the sentiment of input data from disaster victims and sends that data to a database on AWS. On the server, a machine learning algorithm using TensorFlow analyzes this data to determine the priority of assistance and the level of psychological urgency. Based on the analysis results, the server generates a supply plan and matches it with appropriate external resources. During this process, the server notifies disaster victims and aid workers of the supply plan and arranges psychological support as needed. 【0609】 For example, if an elderly person enters "I feel a little anxious today, but I need a new blanket," the server will analyze this data to understand their feelings of anxiety and prioritize the supply of blankets while also considering arranging psychological support. In this way, it becomes possible to comprehensively meet the physical and psychological needs of disaster victims. 【0610】 An example of a prompt message is: "Analyze the mental state of the person who entered this text, and prioritize the necessary support based on that state." 【0611】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0612】 Step 1: 【0613】 The device receives information on the user's need for assistance and emotional state. The input data is sent to Google Cloud's Natural Language API for sentiment analysis. This process identifies emotions contained in the text and generates sentiment data. The output is the analyzed sentiment data. 【0614】 Step 2: 【0615】 The device sends support need information along with generated sentiment data to a database on AWS. The input is support need information and sentiment data collected from the user. The output is a notification that the data has been saved to the database. 【0616】 Step 3: 【0617】 The server retrieves support needs information and sentiment data from the database. Using this data, TensorFlow is employed to perform analysis to determine support priorities and psychological urgency. The input is information extracted from the database, and the output is a list of priorities and urgency levels. 【0618】 Step 4: 【0619】 The server generates a supply plan based on the analysis results. The supply plan reflects the psychological state of disaster victims and matches them with appropriate external resources. The supply plan is created using a generative AI model. The input is a list of priorities and urgency levels, and the output is the details of the supply plan. 【0620】 Step 5: 【0621】 The server generates a supply plan and notifies disaster victims and aid workers. The notification is sent to a terminal and clearly indicates the need for psychological support. The input is the supply plan, and the output is the notification. 【0622】 Step 6: 【0623】 Users input feedback about the assistance they received via a terminal. This feedback includes the suitability of the provided materials and their emotional satisfaction. The input is the feedback information, and the output is that information stored in the database. 【0624】 Step 7: 【0625】 The server integrates feedback information and performs analysis to help improve the system. TensorFlow is used again to analyze the feedback data and incorporate it into the next support plan. This feedback loop improves the accuracy of future support plans. The input is feedback information, and the output is an indicator of the improved system. 【0626】 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. 【0627】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0628】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0629】 [Fourth Embodiment] 【0630】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0631】 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. 【0632】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0633】 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. 【0634】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0635】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0636】 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. 【0637】 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0638】 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. 【0639】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0640】 The 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. 【0641】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0642】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0643】 This invention is a system for effectively providing support in disaster-stricken areas when a large-scale disaster occurs. In this system, terminals implemented in each disaster-stricken area collect initial information, input and analyze user needs and circumstances, and transmit them to a server. 【0644】 Device features: 【0645】 The terminals allow disaster victims to input information about the supplies and support they need. For example, terminals are installed in each evacuation center, providing a system for users to report shortages of supplies, high-priority support, and special medical needs. The terminals instantly digitize the information gathered on-site and send it to a server, enabling real-time information sharing. 【0646】 Server functions: 【0647】 The server has the ability to receive information transmitted from terminals and analyze that data. The server uses machine learning algorithms to analyze and prioritize the needs of the disaster area. The server also matches information on resources available from external sources with the needs of the disaster area and decides which resources to send to which area. 【0648】 Furthermore, the server creates an effective supply plan and notifies the affected areas and relevant organizations via terminals. This enables swift and appropriate relief efforts. 【0649】 User roles: 【0650】 Users can report their needs via terminals located in evacuation centers. This includes the types of supplies needed, the current situation, the number of people, and any special requests. After receiving relief supplies, they can input feedback on the situation and their satisfaction level into the terminal and send it to the server to help plan future relief efforts. 【0651】 This system enables disaster-stricken areas and victims to receive necessary support in a timely manner, allowing for efficient responses even in chaotic situations. For example, if a disaster victim reports a shortage of water and food via a terminal, the server immediately analyzes the need, creates a plan for supplying water and food from external aid organizations, and instructs them to deliver the supplies quickly. 【0652】 The following describes the processing flow. 【0653】 Step 1: 【0654】 The terminal displays an interface for disaster victims to input needs information at evacuation centers. Users enter the supplies they need and the most urgent assistance they require into this interface. 【0655】 Step 2: 【0656】 The terminal analyzes the input data in real time and performs an initial assessment of its importance and urgency. The data, including the results of the initial assessment, is immediately sent to the server. 【0657】 Step 3: 【0658】 The server stores the data received from the terminal in a database and analyzes the collected needs information. The server uses machine learning algorithms to classify and prioritize support needs. 【0659】 Step 4: 【0660】 The server aggregates information on resources available from external sources and matches it with the needs of disaster victims. The server then uses a matching algorithm to select the most suitable suppliers and develop a supply plan for materials. 【0661】 Step 5: 【0662】 The server notifies relevant organizations and personnel in the disaster-stricken areas of the created supply plan via terminals, informing them of the expected arrival date of the supplies. 【0663】 Step 6: 【0664】 Based on the information provided, users receive relief supplies at the designated location and time. They confirm receipt of the supplies via their device and input feedback regarding the receipt status and any additional needs. 【0665】 Step 7: 【0666】 The server receives feedback from the terminals and analyzes the data to improve the accuracy of subsequent support activities. Based on the feedback information, it improves algorithms and supply plans as needed. 【0667】 (Example 1) 【0668】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0669】 Disaster relief efforts in affected areas require the rapid and accurate identification, supply, and prioritization of necessary goods and resources. However, currently, information gathering and analysis are inefficient, leading to problems with the timely provision of necessary support. As a result, disaster victims may not receive adequate assistance, potentially causing confusion. Therefore, this invention aims to realize the efficient and effective supply of goods in disaster-stricken areas. 【0670】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0671】 In this invention, the server includes a data input device, a data analysis device, and a calibration device. This enables the immediate collection of demand information in disaster-stricken areas, the determination of priorities, and the formulation of an optimal supply plan. 【0672】 A "data input device" is a device that receives information about necessary goods and support from users in disaster-stricken areas, digitizes it, and transmits it to a server. 【0673】 A "data analysis device" is a device that analyzes the demand information received from disaster-stricken areas and sets resource priorities using machine learning algorithms. 【0674】 A "fitting device" is a device that, based on analysis results, identifies available external resources and matches them to the demand in the most suitable way. 【0675】 A "distribution plan creation device" is a device that formulates a supply plan to optimally deliver selected resources to disaster-stricken areas and communicates that plan to relevant organizations and facilities. 【0676】 A "communication device" is a device that supports the efficient supply of goods by promptly notifying relevant parties of the formulated supply plan. 【0677】 An "evaluation information processing device" is a device that analyzes the evaluation information received after supply and uses it to improve the system in order to help with future support. 【0678】 This invention is a system for providing efficient support in the event of a large-scale disaster. The system mainly consists of terminals, servers, and users, with each element having its own role. 【0679】 Terminal role: 【0680】 The terminals are installed in disaster-stricken areas and function as an interface for users to input their individual needs. Users input information such as "water is scarce" or "food is needed" using, for example, a touchscreen or voice recognition software. The entered data is immediately digitized and transmitted to a server via the internet. The hardware used includes common tablets and smartphones, and the software includes a voice recognition engine. 【0681】 Server role: 【0682】 The server functions as a central processing unit that analyzes the received data. It uses TensorFlow, an open-source machine learning framework, to analyze the data and classify the needs of the disaster-stricken areas. Furthermore, the server uses external sensors and APIs to understand the local situation and create an optimal plan for supplying relief materials. Specifically, it uses the Google Maps API for geographical optimization. 【0683】 User roles: 【0684】 Users report their needs using a device and provide feedback after receiving assistance. This feedback is then sent back to the server and used as data to improve future assistance activities. For example, if feedback indicates that "food supplies at shelters are still insufficient," the next supply plan will be adjusted based on that feedback. 【0685】 Using a generative AI model, an example of a prompt might be, "Design a prototype application that analyzes the needs of disaster-stricken areas in real time and proposes the optimal support plan." Based on this prompt, the AI ​​model provides support for efficient data analysis and automatic generation of supply plans. 【0686】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0687】 Step 1: 【0688】 The terminal functions as an interface for receiving user input. The user enters their current needs into the terminal. This input data includes the type of item, quantity, urgency, etc. The terminal receives and digitizes this data via voice recognition or touchscreen. As output, the user's needs information is generated as data packets. 【0689】 Step 2: 【0690】 The terminal sends the generated data packets to the server. Internet-related technologies are used for transmission. The terminal receives digital data generated from the terminal as input and converts it into the appropriate communication protocol. As output, it sends data packets along the path to reach the server. 【0691】 Step 3: 【0692】 The server analyzes the data received from the terminal. It receives digital data from the terminal as input. The server analyzes the data using machine learning algorithms, particularly TensorFlow, to classify and prioritize each need. A list of processed needs is generated as output. 【0693】 Step 4: 【0694】 The server creates a suitable supply plan based on the analysis results. It receives a list of categorized needs as input. The server utilizes the Google Maps API and external resource information to plan the delivery of necessary items to the optimal locations. A detailed supply plan is generated as output. 【0695】 Step 5: 【0696】 The server sends the generated supply plan to the terminal. As input, it retrieves the delivery plan from its data repository. The server sends the plan as digital data in a format the terminal can interpret. As output, the terminal receives a notification of the supply plan. 【0697】 Step 6: 【0698】 The terminal displays the supply plan from the server and notifies users and shelter staff. It receives digital data from the server as input. The terminal visually displays this data and notifies users of the plan through means such as audio alerts. The displayed plan information is transmitted to the user as output. 【0699】 Step 7: 【0700】 Users input feedback about the relief supplies and services they receive into a terminal. The terminal receives information such as satisfaction levels and areas for improvement as input. The terminal digitizes this information and sends it back to the server. The output is a data packet containing the improvement information. 【0701】 (Application Example 1) 【0702】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0703】 In the event of a large-scale disaster, there is a lack of mechanisms to quickly grasp the needs of affected areas and efficiently supply appropriate relief materials. Furthermore, traditional methods are time-consuming in gathering needs from victims, making it difficult to prioritize support, thus hindering rapid relief efforts. To address this problem, immediate data collection from the field and real-time resource supply planning are required. 【0704】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0705】 In this invention, the server includes information gathering means for collecting support needs information in disaster-stricken areas, means for users to report needs information along with local location information via a mobile terminal, and real-time processing means for analyzing information transmitted from the site and quickly formulating and executing a resource supply plan. This enables the immediate digitization of needs in disaster-stricken areas, real-time analysis, dynamic prioritization, and efficient supply of relief materials. 【0706】 "Information gathering means" refers to devices or methods for collecting information on the need for support in disaster-stricken areas, and is used to gather information on the needs of disaster victims. 【0707】 "Analysis means" refers to an apparatus or method for determining the types of resources needed and their priorities based on collected support needs information. 【0708】 "Matching means" refers to a device or method for associating a specified resource with an external resource that can supply it. 【0709】 "Means for generating supply plans" refers to an apparatus or method for creating a plan for efficiently distributing selected resources to disaster-stricken areas. 【0710】 "Notification means" refers to a device or method for conveying necessary information to residents of the disaster-stricken area and relevant support organizations based on the support plan. 【0711】 A "feedback mechanism" is a device or method that receives information about the status of relief supplies after they have been supplied and uses that information to improve the system. 【0712】 "Means of reporting via mobile devices" refers to devices or methods using mobile devices or applications that enable disaster victims to easily report their local location and needs. 【0713】 A "real-time processing means" is a device or method for immediately analyzing information transmitted from the site and quickly formulating and executing a resource supply plan. 【0714】 The system of the present invention is primarily implemented through an information collection terminal, an analysis server, and a mobile terminal. The system is configured as follows: 【0715】 Information gathering terminals are installed in disaster-stricken areas. These terminals allow disaster victims to input information about the supplies and services they need. For example, a user interface is provided via a mobile device, allowing users to report their current needs using voice or text input. GPS functionality is also used to identify the location where the input was made, by adding location information. 【0716】 The analysis server receives information transmitted from terminals in real time. The server efficiently stores data using cloud services such as Google Cloud Platform and analyzes the data using machine learning algorithms (e.g., TensorFlow). The analysis prioritizes the support needs of each region. Furthermore, it generates resource allocation plans and adjusts them to ensure optimal support is distributed. 【0717】 Users can use mobile devices such as smartphones and smart glasses to report their support needs via an application and provide feedback after receiving support. This allows for real-time support planning based on the latest information. 【0718】 As a concrete example, consider a case where residents of an area affected by heavy rain use an app to report food shortages. The information transmitted via mobile devices is immediately analyzed on a server, and a plan is created to prioritize the supply of necessary food. 【0719】 An example of a prompt is, "Explain how to analyze the information and develop a support plan when residents in flood-affected areas who need food supplies report their need for assistance using a smartphone app." Using prompts like this to the generative AI model helps to easily obtain specific support procedures and plans. 【0720】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0721】 Step 1: 【0722】 The terminal receives information about the needs of disaster victims as input. Users use their mobile devices to input location information and information about shortages of supplies, and the terminal collects this data. The input information is immediately formatted as digital data and becomes data to be sent to the server. 【0723】 Step 2: 【0724】 The terminal transmits the collected information to the server. The entered data includes the current situation of disaster victims, the supplies they need, and their location. The terminal then transfers the formatted digital data to the server via the internet. 【0725】 Step 3: 【0726】 The server receives data from terminals as input in real time. The server uses a Google Cloud Platform database to store the data and prepare it for analysis by machine learning algorithms. 【0727】 Step 4: 【0728】 The server performs analysis based on the received data. Specifically, it uses machine learning libraries such as TensorFlow to classify and prioritize needs. Through this analysis, it determines the highest priority for resource supply and then proceeds to the next processing step based on the results. 【0729】 Step 5: 【0730】 The server generates a resource supply plan using the analysis results. It uses a matching algorithm to correlate available resources with disaster-stricken areas in high need. Specifically, it develops a detailed plan specifying which resources to supply to which areas and in what quantities. 【0731】 Step 6: 【0732】 The server outputs the generated supply plan to relevant organizations and disaster victims through notification channels. The server distributes information using email and the notification function of a dedicated app, promptly informing relevant parties of the plan's contents. 【0733】 Step 7: 【0734】 Users can provide feedback again after receiving supplies. This feedback includes inputting information about their satisfaction with the supply and any additional needs. The terminal then sends this feedback information to the server. 【0735】 Step 8: 【0736】 The server receives feedback data and uses it to improve the system. By analyzing the received data, it helps improve the accuracy of future supply plans and needs forecasts. The server uses a generative AI model to continuously optimize support plans. 【0737】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0738】 This invention is a system for effectively carrying out support activities in disaster-stricken areas during large-scale disasters, and aims to provide support that takes into account the emotional state of the user. In particular, this system enables the effective collection of the urgent needs of disaster victims and the improvement of delivery plans. 【0739】 Device features: 【0740】 The terminal is equipped with an interface for inputting disaster victims' needs information, allowing users to easily report the supplies they lack and the assistance they need. Furthermore, the terminal has an emotion engine that recognizes emotions from the user's input data. For example, if a user expresses emotions such as worry or fear, the emotion engine analyzes that information and makes a judgment. This emotional information reflects not only the need for supplies but also the psychological urgency the user is experiencing. 【0741】 Server functions: 【0742】 The server receives needs information and sentiment data sent from terminals and integrates it into a database. Using machine learning algorithms, the server designs more adaptive resource allocation by reflecting the user's sentiment data in the priority of assistance. For example, if the sentiment data indicates that many disaster victims are feeling anxious, the server will develop a plan to expedite assistance to that area. 【0743】 The server also aggregates available resources from external support organizations and companies and matches them with disaster relief needs. In this process, the server utilizes data obtained from the emotion engine to determine whether psychological support should be prioritized. 【0744】 User roles: 【0745】 Users input the expected support and provide emotional feedback through their device. This includes detailed feedback, including emotions, on whether the received support supplies were appropriate and how to incorporate those points into future support plans. 【0746】 This system configuration enables accurate support in disaster-stricken areas, including addressing users' inner needs, and facilitates a swift and effective disaster response. For example, if a disaster victim expresses the emotion of "worry," the system prioritizes processing that need, providing emotional support and promptly supplying necessary materials. 【0747】 The following describes the processing flow. 【0748】 Step 1: 【0749】 The terminal provides an interface for disaster victims to input their needs for supplies and assistance. Users input the supplies they lack and their specific requests on the terminal, and select options that represent their emotional state (e.g., reassured, anxious, frightened). 【0750】 Step 2: 【0751】 The device acquires information entered by the user as digital data and analyzes the user's emotional state using its built-in emotion engine. Needs data, including the analysis results, is then generated. 【0752】 Step 3: 【0753】 The device transmits generated needs data and emotion data to the server in real time. Location and time information are also included in the data. 【0754】 Step 4: 【0755】 The server stores the data received from the terminals in a database and aggregates information on the needs and emotions of each affected area. 【0756】 Step 5: 【0757】 The server analyzes the collected data using machine learning algorithms to reassess the prioritization and urgency of needs. It takes emotional data into consideration, paying particular attention to areas where psychological support is needed. 【0758】 Step 6: 【0759】 The server matches externally available resource information with the needs of the affected area and develops an optimal support plan based on emotional data. This plan includes the prioritization of supplying materials and additional psychological support. 【0760】 Step 7: 【0761】 The server notifies aid organizations and disaster relief personnel in various locations of the formulated supply plan via terminals. The plan includes estimated arrival dates for supplies and details of psychological support. 【0762】 Step 8: 【0763】 Users can check the status of the support provided through their device and input feedback, including their feelings, about the received supplies and psychological support. 【0764】 Step 9: 【0765】 The terminal sends user feedback to the server, which uses this data to improve future support plans. Emotional data is continuously analyzed to contribute to overall system improvement. 【0766】 (Example 2) 【0767】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0768】 In disaster relief efforts, it is crucial to effectively and quickly identify the needs for supplies and services and to provide support based on priorities. However, conventional methods have made it difficult to develop support plans that take into account the emotional state of disaster victims, and have failed to adequately assess the need for psychological support. As a result, the physical and psychological needs of disaster victims have not been adequately met, leading to challenges in the efficiency and effectiveness of relief activities. 【0769】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0770】 In this invention, the server includes emotion analysis means for analyzing the emotional state of disaster victims and setting priorities for psychological support; analysis means for improving support plans that take into account the emotional information of disaster victims; and information processing means for identifying the types and priorities of necessary resources based on support needs information. This makes it possible to simultaneously consider psychological and physical needs and to formulate and implement a rapid and accurate support plan. 【0771】 "Information gathering means" refers to devices and methods for collecting information on the need for assistance from disaster-stricken areas. 【0772】 "Information processing means" refers to devices or methods that analyze collected support needs information to determine the type and priority of resources required. 【0773】 A "matching means" is a device or method that has the function of selecting available external resources that correspond to identified required resources. 【0774】 "Means for generating plans" refers to devices or methods for creating plans to efficiently provide selected resources to disaster-stricken areas. 【0775】 "Information provision means" refers to devices and methods for notifying disaster victims and related parties based on the generated plan. 【0776】 "Improvement measures" refer to devices or methods that have the function of improving the entire system based on evaluation information received after supply. 【0777】 "Emotional analysis tools" refer to devices or methods for analyzing the emotional state of disaster victims and setting priorities for psychological support based on those analyses. 【0778】 "Analysis tools" refer to devices and methods for improving effective support plans by taking into account the emotional information of disaster victims. 【0779】 To implement this system, users will need to prepare terminals for use in disaster-stricken areas. These terminals will have an interface for inputting the supplies and support needed by disaster victims, as well as an emotion engine for sentiment analysis. This emotion engine will use a general-purpose software library for sentiment analysis. Specifically, Microsoft Azure's Text Analytics API can be used. When a user inputs needs information into the terminal, such as "We are short on drinking water and food," the emotion engine will analyze this information and identify the emotional state. 【0780】 The server receives support need information and sentiment data collected from terminals and stores this data in a recording device for centralized management. Online storage services such as Amazon S3 are used for storage. The server further analyzes the data using machine learning techniques, such as those using Python. This analysis determines the priority of support needs and identifies the necessary support resources. Based on this information, the server creates a matching and allocation plan for external resources. 【0781】 For example, if a user enters "I'm very worried, it's terrible that I can't use the bath," the terminal analyzes this emotional data and sends it to the server with "worry" emphasized. Based on this information, the server plans the rapid provision of mobile bathing equipment and prepares to implement effective support. By utilizing generative AI models and machine learning algorithms, the data is updated in real time, improving the accuracy of the support plan. 【0782】 Examples of prompt messages include the following: 【0783】 "Please explain how to analyze the emotional state of disaster victims and prioritize the necessary support." 【0784】 "What is the most appropriate support to provide to disaster victims who are feeling anxious?" 【0785】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0786】 Step 1: 【0787】 Users in disaster-stricken areas input necessary support information through the terminal's input interface. Specifically, they register needs and emotions such as "There is a shortage of drinking water" or "My family is worried and restless." At that time, the emotion analysis engine installed in the terminal analyzes the text data and identifies the emotional state. The input in this process is the user's text information, and the output is the analyzed emotion data. 【0788】 Step 2: 【0789】 The device sends collected needs information and sentiment data to the server. The server receives this data and stores it in a database. This storage is done using, for example, cloud storage, enabling efficient data management. The input is the data sent from the device, and the output is the information stored in the database. 【0790】 Step 3: 【0791】 The server applies machine learning algorithms to analyze the stored information to determine support needs. This prioritizes the needs of disaster victims and categorizes the necessary relief supplies and psychological support. In this process, user-provided needs information and emotional data are used as input, and the analysis results output support items with high priority. 【0792】 Step 4: 【0793】 The server matches external resources with priority support items. This process gathers resources available from external sources and matches them with the needs in the disaster-stricken area. The input is analysis results and resource information, and the output is a specific allocation plan. 【0794】 Step 5: 【0795】 The server develops the final support plan and notifies stakeholders and disaster victims of this information. This involves sending the plan details using a notification system. The input is the allocation plan, and the output is a message notifying the progress of the plan. 【0796】 Step 6: 【0797】 After receiving assistance, users provide feedback using a device. They report whether the assistance was provided appropriately and what improvements can be made in the future. This provides the system with valuable input for further improvement. The input is user feedback data, and the output is insights for system improvement. 【0798】 (Application Example 2) 【0799】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0800】 In disaster relief efforts, it is essential to efficiently provide support that takes into account not only the supply of goods but also the emotional state of the victims. However, conventional systems make it difficult to grasp emotional states and do not prioritize support based on psychological urgency. As a result, the psychological needs of the victims are not adequately met, leading to a problem of limited effectiveness in relief efforts. 【0801】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0802】 In this invention, the server includes information gathering means for collecting information on the need for support and emotional state in the disaster-stricken area; analysis means for identifying the type, priority, and psychological urgency of necessary resources based on the information on the need for support and emotional state; and matching means for selecting available external resources corresponding to the identified resources and matching them while taking emotional state into consideration. This makes it possible to provide rapid and accurate support that is in line with the psychological state of disaster victims. 【0803】 "Support needs information" refers to information about the requests for supplies and services needed in disaster-stricken areas. 【0804】 "Emotional state information" refers to information that indicates the psychological state of individual disaster victims and is used to prioritize support. 【0805】 "Information gathering means" refers to the components of devices and systems used to collect information on the needs for assistance and emotional states of disaster victims. 【0806】 "Analysis methods" refer to means for analyzing collected support needs information and emotional state information to identify the type and priority of necessary resources and the level of psychological urgency. 【0807】 "Matching methods" refer to processes and technologies for selecting available external resources corresponding to identified resources and for performing appropriate matching while considering emotional state information. 【0808】 A "supply plan" refers to a plan for efficiently distributing necessary goods and services to disaster-stricken areas, and also includes arranging psychological support in accordance with information on emotional states. 【0809】 "Notification means" refers to methods and means of transmitting information to disaster victims and support personnel based on the supply plan. 【0810】 "Feedback methods" refer to means of collecting information to evaluate the situation after supply and to help improve the system. 【0811】 To realize this invention, a system is needed that links a server and terminals. The server is built on a cloud service platform such as AWS or Google Cloud and uses a database that aggregates information on the support needs and emotional state of disaster victims. The terminals are mobile devices such as smartphones and tablets, and collect data on the needs and emotional state entered by disaster victims in real time. 【0812】 The terminal program uses Google Cloud's Natural Language API to analyze the sentiment of input data from disaster victims and sends that data to a database on AWS. On the server, a machine learning algorithm using TensorFlow analyzes this data to determine the priority of assistance and the level of psychological urgency. Based on the analysis results, the server generates a supply plan and matches it with appropriate external resources. During this process, the server notifies disaster victims and aid workers of the supply plan and arranges psychological support as needed. 【0813】 For example, if an elderly person enters "I feel a little anxious today, but I need a new blanket," the server will analyze this data to understand their feelings of anxiety and prioritize the supply of blankets while also considering arranging psychological support. In this way, it becomes possible to comprehensively meet the physical and psychological needs of disaster victims. 【0814】 An example of a prompt message is: "Analyze the mental state of the person who entered this text, and prioritize the necessary support based on that state." 【0815】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0816】 Step 1: 【0817】 The device receives information on the user's need for assistance and emotional state. The input data is sent to Google Cloud's Natural Language API for sentiment analysis. This process identifies emotions contained in the text and generates sentiment data. The output is the analyzed sentiment data. 【0818】 Step 2: 【0819】 The device sends support need information along with generated sentiment data to a database on AWS. The input is support need information and sentiment data collected from the user. The output is a notification that the data has been saved to the database. 【0820】 Step 3: 【0821】 The server retrieves support needs information and sentiment data from the database. Using this data, TensorFlow is employed to perform analysis to determine support priorities and psychological urgency. The input is information extracted from the database, and the output is a list of priorities and urgency levels. 【0822】 Step 4: 【0823】 The server generates a supply plan based on the analysis results. The supply plan reflects the psychological state of disaster victims and matches them with appropriate external resources. The supply plan is created using a generative AI model. The input is a list of priorities and urgency levels, and the output is the details of the supply plan. 【0824】 Step 5: 【0825】 The server generates a supply plan and notifies disaster victims and aid workers. The notification is sent to a terminal and clearly indicates the need for psychological support. The input is the supply plan, and the output is the notification. 【0826】 Step 6: 【0827】 Users input feedback about the assistance they received via a terminal. This feedback includes the suitability of the provided materials and their emotional satisfaction. The input is the feedback information, and the output is that information stored in the database. 【0828】 Step 7: 【0829】 The server integrates feedback information and performs analysis to help improve the system. TensorFlow is used again to analyze the feedback data and incorporate it into the next support plan. This feedback loop improves the accuracy of future support plans. The input is feedback information, and the output is an indicator of the improved system. 【0830】 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. 【0831】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0832】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0833】 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. 【0834】 Figure 9 shows an 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. 【0835】 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. 【0836】 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. 【0837】 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, motorcycles, etc., 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, for example, based 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. 【0838】 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." 【0839】 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. 【0840】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0841】 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0842】 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. 【0843】 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. 【0844】 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. 【0845】 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 this memory. 【0846】 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. 【0847】 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. 【0848】 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. 【0849】 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 the like 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. 【0850】 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. 【0851】 The following is further disclosed regarding the embodiments described above. 【0852】 (Claim 1) 【0853】 Information gathering methods for collecting information on the need for support in disaster-stricken areas, 【0854】 An analytical means for identifying the type and priority of necessary resources based on the aforementioned support needs information, 【0855】 Matching means for selecting available external resources corresponding to the identified resources, 【0856】 A means for generating a supply plan to efficiently distribute selected resources to the disaster-stricken area, 【0857】 A means of notifying disaster victims and related parties based on the supply plan, 【0858】 A feedback mechanism for receiving feedback after supply and improving the system, 【0859】 A system that includes this. 【0860】 (Claim 2) 【0861】 The system according to claim 1, characterized in that the information gathering means receives needs information from disaster victims in real time and stores it in a database. 【0862】 (Claim 3) 【0863】 The system according to claim 1, characterized in that the analysis means classifies support demand information using a machine learning algorithm and sets the priority of needs. 【0864】 "Example 1" 【0865】 (Claim 1) 【0866】 A data input device for collecting information on the demand for goods in disaster-stricken areas, 【0867】 A data analysis device for determining the classification and priority of necessary resources based on the aforementioned goods demand information, 【0868】 A suitability device for selecting available external resources for the determined resources, 【0869】 A device for creating a distribution plan to effectively deliver selected resources to disaster-stricken areas, 【0870】 A communication device for notifying users and related organizations based on the delivery plan, 【0871】 An evaluation information processing device for receiving evaluation information after supply and reflecting it in system improvements, 【0872】 A system that includes this. 【0873】 (Claim 2) 【0874】 The system according to claim 1, characterized in that the data input device immediately receives request information from disaster victims and records it in an information storage location. 【0875】 (Claim 3) 【0876】 The system according to claim 1, characterized in that the data analysis device classifies goods demand information using a machine learning analysis method and determines the priority of needs. 【0877】 "Application Example 1" 【0878】 (Claim 1) 【0879】 Information gathering methods for collecting information on the need for support in disaster-stricken areas, 【0880】 An analytical means for identifying the type and priority of necessary resources based on the aforementioned support needs information, 【0881】 Matching means for selecting available external resources corresponding to the identified resources, 【0882】 A means for generating a supply plan to efficiently distribute selected resources to the disaster-stricken area, 【0883】 A means of notifying disaster victims and related parties based on the supply plan, 【0884】 A feedback mechanism for receiving feedback after supply and improving the system, 【0885】 A means for users to report needs information along with local location information via their mobile devices, 【0886】 A real-time processing system for analyzing information transmitted from the site and quickly formulating and executing resource supply plans, 【0887】 A system that includes this. 【0888】 (Claim 2) 【0889】 The aforementioned information gathering means receives information on the needs of disaster victims in real time and stores it in a database. 【0890】 The system according to claim 1, further characterized by supporting data input including location information via a mobile device. 【0891】 (Claim 3) 【0892】 The aforementioned analysis means classifies support demand information using a machine learning algorithm, 【0893】 The system according to claim 1, further characterized by setting priority of needs based on location information. 【0894】 "Example 2 of combining an emotion engine" 【0895】 (Claim 1) 【0896】 Information gathering methods for collecting information on the need for support in disaster-stricken areas, 【0897】 Information processing means for identifying the type and priority of necessary resources based on the aforementioned support needs information, 【0898】 Matching means for selecting available external resources corresponding to the identified resources, 【0899】 A means for generating a plan to efficiently distribute selected resources to disaster-stricken areas, 【0900】 A means of providing information to disaster victims and related parties in accordance with the plan, 【0901】 A means of improvement that receives evaluation information after supply and uses it to improve the system, 【0902】 A means of emotional analysis to analyze the emotional state of disaster victims and set priorities for psychological support, 【0903】 Analytical methods for improving support plans that take into account the emotional information of disaster victims, 【0904】 A system that includes this. 【0905】 (Claim 2) 【0906】 The system according to claim 1, characterized in that the information gathering means immediately receives emergency demand information from disaster victims and stores it in a recording device. 【0907】 (Claim 3) 【0908】 The system according to claim 1, characterized in that the information processing means classifies support demand information using a machine learning method and determines the importance of the needs. 【0909】 "Application example 2 when combining with an emotional engine" 【0910】 (Claim 1) 【0911】 Information gathering means for collecting information on support needs and emotional states in disaster-stricken areas, 【0912】 An analytical means for identifying the type, priority, and psychological urgency of necessary resources based on the aforementioned support needs information and emotional state information, 【0913】 A matching means for selecting available external resources corresponding to the identified resources and performing matching while taking into consideration emotional states, 【0914】 To generate a supply plan for efficiently distributing selected resources to the disaster-stricken area, and to include means for psychological support tailored to emotional states, 【0915】 Based on the supply plan, notify the victims and related parties, including the means of notification, including the status of psychological support, 【0916】 A feedback mechanism that receives post-supply feedback and emotional feedback to improve the system, 【0917】 A system that includes this. 【0918】 (Claim 2) 【0919】 The system according to claim 1, characterized in that the information gathering means receives information on the needs and emotional state of disaster victims in real time and stores it in a database. 【0920】 (Claim 3) 【0921】 The system according to claim 1, characterized in that the analysis means classifies support needs information and emotional state information using a machine learning algorithm and sets the priority of needs and psychological urgency. [Explanation of symbols] 【0922】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] Information gathering methods for collecting information on the need for support in disaster-stricken areas, An analytical means for identifying the type and priority of necessary resources based on the aforementioned support needs information, Matching means for selecting available external resources corresponding to the identified resources, A means for generating a supply plan to efficiently distribute selected resources to the disaster-stricken area, A means of notifying disaster victims and related parties based on the supply plan, A feedback mechanism for receiving feedback after supply and improving the system, A system that includes this. [Claim 2] The system according to claim 1, characterized in that the information gathering means receives needs information from disaster victims in real time and stores it in a database. [Claim 3] The system according to claim 1, characterized in that the analysis means classifies support demand information using a machine learning algorithm and sets the priority of needs.

Images