System

Abstract

A system is provided.SOLUTION: Means for acquiring seismic intensity data and damage range data, means for analyzing the acquired seismic intensity data and damage range data and calculating an influence score, means for storing an analysis result in a database, means for acquiring a satellite image and a drone image, means for analyzing the acquired images and specifying a road condition of a disaster area, and means for calculating an optimal delivery route, A system comprising: means for storing in a database; means for obtaining population and population pyramid information; means for generating estimates of victims based on the obtained information; means for determining types and amounts of relief supplies; means for identifying and communicating available areas and businesses; means for collecting feedback from victims and relief organizations; and means for analyzing the collected feedback and updating supply plans.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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 Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In the event of a large-scale disaster such as an earthquake, it is extremely important to provide relief supplies to affected areas quickly and appropriately. However, it remains difficult to quickly and accurately determine the type, quantity, priority, and delivery method of supplies according to the situation and needs of the affected area. Other issues include balancing supply and demand, inventory management, and optimizing delivery routes. It is necessary to solve these issues and streamline the delivery of relief supplies when a disaster occurs. [Means for solving the problem]

[0005] The present invention provides a means for acquiring seismic intensity data and damage extent data, a means for analyzing the acquired seismic intensity data and damage extent data to calculate an impact score, and a means for storing the analysis results in a database. It also includes a means for acquiring satellite images and drone images, a means for analyzing the acquired images to identify road conditions in the disaster area, and a means for calculating optimal delivery routes and storing the results in a database. It also provides a means for acquiring population density and population pyramid data, a means for generating estimated information on disaster victims based on the acquired data, and a means for determining the type and quantity of relief supplies. It also includes a means for identifying and notifying regions and companies that can cooperate, a means for collecting feedback from disaster victims and relief organizations, and a means for analyzing the collected feedback and updating the supply plan. In this way, a system is provided that optimizes the type, quantity, and delivery route of relief supplies in the event of a disaster, enabling the prompt and appropriate provision of supplies.

[0006] "Seismic intensity data" is numerical information that indicates the strength of shaking in various locations when an earthquake occurs.

[0007] "Damage extent data" is information indicating the size and extent of the area damaged by the earthquake.

[0008] The "impact score" is a number that indicates the degree of impact of a disaster, calculated by combining data such as seismic intensity, damage area, and number of victims.

[0009] A "database" is a management system for efficiently storing, searching, and updating information.

[0010] "Satellite imagery" refers to image data of the Earth's surface taken from an artificial satellite.

[0011] "Drone imagery" refers to image data of the ground or facilities taken by small unmanned aerial vehicles (drones).

[0012] "Image analysis" is a technique for processing image data and extracting specific information.

[0013] "Road conditions" is information indicating whether the road is passable, whether there are any obstacles, and whether the road is damaged.

[0014] "Delivery route" is information indicating a route for efficiently transporting goods.

[0015] "Population density data" is information that indicates the density of people living in a particular area.

[0016] "Population pyramid data" is graph data that shows the population distribution by age group and gender in a specific area.

[0017] "Estimated information" is information obtained by making inferences or predictions based on existing data.

[0018] "Relief supplies" are necessities such as food, drinking water, medicine, and clothing provided to disaster victims.

[0019] A "cooperative region" is a region that can cooperate in relief efforts and provide supplies in the event of a disaster.

[0020] "Companies" are legal entities that provide supplies and human support.

[0021] "Notification" is the act of officially informing others of information.

[0022] "Feedback" refers to information such as requests, opinions, and actual situation reports provided by disaster victims and relief organizations.

[0023] A "supply plan" is a plan that determines how relief supplies will be distributed to which areas. [Brief explanation of the drawings]

[0024] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0025] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0026] First, the terms used in the following description will be explained.

[0027] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0028] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0029] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0030] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, 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), Bluetooth (registered trademark), etc.

[0031] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0032] [First embodiment]

[0033] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0034] 1, a 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.

[0035] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0036] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0037] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0038] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0039] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0040] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0041] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0042] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0043] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0044] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0045] This section describes a system for implementing the present invention. This system collects and analyzes a wide variety of data in order to quickly and appropriately supply relief supplies to disaster-stricken areas in the event of a disaster, and then optimizes the type and quantity of supplies and delivery routes based on the results.

[0046] Program processing and specific examples

[0047] Data acquisition and analysis

[0048] 1. Server: Obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. This data includes the time of the earthquake, epicenter, damage extent, number of victims, etc.

[0049] Examples:

[0050] Seismic intensity data: 7

[0051] Area of ​​damage: 50 square kilometers

[0052] Number of victims: 20,000

[0053] 2. Server: Analyzes the acquired data and calculates the impact score. The impact score is calculated based on the seismic intensity, the extent of the damage, the number of victims, etc.

[0054] Examples:

[0055] Seismic intensity x 1.5 + damage area x 0.3 + number of affected people x 0.2 = impact score 9.5

[0056] 3. Server: The analysis results are stored in a database and used for subsequent processing.

[0057] Image analysis and delivery route optimization

[0058] 1. Server: Acquires satellite and drone images and performs image analysis to identify road conditions and the location of obstacles in the affected areas.

[0059] Examples:

[0060] Satellite image: Wide range of the affected area

[0061] Drone imagery: detailed road conditions

[0062] 2. Server: Uses GIS (geographic information systems) to calculate optimal delivery routes, avoiding blocked roads and identifying routes that will deliver goods quickly.

[0063] Examples:

[0064] The main road is closed, so a detour route is calculated to determine the shortest delivery route.

[0065] Population data analysis and supply decisions

[0066] 1. Server: Obtains population density and population pyramid data from government and statistical bureaus.

[0067] Examples:

[0068] Population density: 1,000 people per square kilometer

[0069] Population pyramid: 30% elderly, 20% children

[0070] 2. Server: Analyzes the acquired data and estimates the age, gender, and health status of the victims. Based on this information, the type and amount of relief supplies needed are determined.

[0071] Examples:

[0072] Identify supplies of medicine for the elderly, food for children, and water

[0073] Identifying and notifying cooperating regions and companies

[0074] 1. Server: Identifies neighboring areas and cooperating companies from a database based on the type and quantity of relief supplies obtained and their necessity.

[0075] Examples:

[0076] Supply of medicines from neighboring cities and delivery support from logistics companies

[0077] 2. Server: Notify the identified partners and promptly begin supplying supplies.

[0078] Gather feedback and update supply plans

[0079] 1. Terminal: Providing a platform that disaster victims and relief organizations can access and input their needs and emergency requests.

[0080] Examples:

[0081] Victims can enter their supply needs using a web form or mobile app.

[0082] 2. Users: Victims and relief organizations input their actual needs and feedback.

[0083] Examples:

[0084] Necessary supplies: fresh water, food, blankets

[0085] 3. Server: Analyzes the collected feedback and reflects it in the current supply plan, thereby ensuring more accurate supply of supplies to disaster-stricken areas.

[0086] Examples:

[0087] Based on feedback, we have increased the supply of fresh water and added more types of medical supplies.

[0088] In this way, by using the system according to the present invention, it is possible to quickly collect and analyze a variety of data and realize optimal provision and delivery of relief supplies.

[0089] The processing flow will be explained below.

[0090] Step 1:

[0091] Server: Obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. This includes information such as the time of the earthquake, epicenter, damage extent, and number of victims. For example, an HTTP request is used to access a specified API and obtain the required data.

[0092] Step 2:

[0093] Server: The acquired seismic intensity data and damage extent data are integrated to calculate the impact score. The impact score is calculated by combining the seismic intensity, damage extent, number of victims, etc. For example, the formula used is seismic intensity x 1.5 + damage extent x 0.3 + number of victims x 0.2.

[0094] Step 3:

[0095] Server: The calculated impact scores are stored in a database for later analysis and processing.

[0096] Step 4:

[0097] Server: Acquires satellite and drone imagery. This includes retrieving data from imagery providers and drone operators. For example, downloading image data from an AWS S3 bucket.

[0098] Step 5:

[0099] Server: Analyzes acquired satellite and drone images to identify road conditions and transportation options in the affected areas. Utilizes deep learning-based image recognition algorithms to detect damaged roads and obstacles.

[0100] Step 6:

[0101] Server: Based on the analysis results, the GIS system is used to calculate the optimal delivery route. A shortest path algorithm is used to identify the most efficient route to evacuation shelters and collection points.

[0102] Step 7:

[0103] Server: Stores the generated delivery route information in a database, allowing logistics planning to proceed efficiently.

[0104] Step 8:

[0105] Server: Obtain population density and population pyramid data from governments and statistical agencies. This includes the number of people in a particular area and their age distribution. Obtain the data using API requests.

[0106] Step 9:

[0107] Server: Analyzes acquired population data to estimate the age, gender, and health status of disaster victims. Performs statistical analysis to determine the type and amount of relief supplies based on specific needs.

[0108] Step 10:

[0109] Server: Based on the estimated number of victims, calculates the type and amount of relief supplies needed. Selects supplies according to specific needs, such as medicines for the elderly or nutritional foods for children.

[0110] Step 11:

[0111] Server: Searches the database for regions and companies that can cooperate, and identifies the most suitable partner based on the type and quantity of supplies and speed of supply.

[0112] Step 12:

[0113] Server: Notify the identified collaborators. This is done using an automatic email sending system or emergency contact tool.

[0114] Step 13:

[0115] Server: Receives responses from partners and reflects them in the supply plan. This updates the supply status of materials in real time.

[0116] Step 14:

[0117] Terminal: Provide an accessible communication platform for disaster victims and relief organizations to gather supply needs and feedback through web forms and mobile apps.

[0118] Step 15:

[0119] Users: Victims and relief organizations enter their required supplies and urgent requests through the platform, including information on the type, quantity, and urgency of the supplies.

[0120] Step 16:

[0121] Server: Analyzes collected feedback in real time and reflects it in supply plans. Based on the feedback, the type and quantity of supplies can be adjusted, enabling efficient delivery to disaster-stricken areas.

[0122] This concrete step will ensure the prompt and appropriate delivery of relief supplies to disaster-stricken areas.

[0123] Example 1

[0124] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0125] In recent years, natural disasters have become more frequent, creating a need for the rapid and effective delivery of relief supplies to affected areas. Conventional methods make it difficult to quickly grasp the disaster situation, secure optimal delivery routes to affected areas, and accurately deliver supplies that meet the needs of disaster victims. Furthermore, there is a lack of a system that reflects the supply status and feedback from disaster victims in real time. This results in delays and imbalances in the supply of supplies, leading to problems with insufficient relief for disaster victims. The purpose of this invention is to solve these problems.

[0126] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0127] In this invention, the server includes means for acquiring weather data and disaster impact data, means for analyzing the acquired weather data and disaster impact data and calculating an impact score, means for saving the analysis results in a database, means for acquiring remote image data, means for analyzing the acquired images and identifying road conditions in the disaster area, means for calculating an optimal delivery route and saving the results in a database, means for acquiring demographic data, means for generating estimated information on disaster victims based on the acquired data, means for determining the type and amount of relief supplies, means for identifying and notifying regions and companies that can cooperate, means for providing a platform for collecting feedback from disaster victims and support organizations, and means for analyzing the collected feedback and updating the supply plan, thereby enabling rapid and appropriate disaster response and relief supply.

[0128] "Weather data" refers to information about weather, temperature, precipitation, wind speed, and other meteorological information.

[0129] "Disaster impact data" refers to data that shows the impact of natural disasters, such as seismic intensity, damage area, and number of victims.

[0130] The "impact score" is a numerical indicator of the impact of a disaster, calculated based on factors such as seismic intensity, area of ​​damage, and number of victims.

[0131] "Remote image data" refers to image data acquired by remotely controlled devices such as satellites and drones.

[0132] "Demographic data" refers to statistical information about age groups, gender, population density, population composition, etc. in a particular area.

[0133] "Estimated information" refers to information such as the age group, gender, and health status of victims that is estimated based on the data obtained.

[0134] "Relief supplies" refer to items such as food, drinking water, medicine, and evacuation supplies that are provided to victims of natural disasters.

[0135] "Regions and companies that can cooperate" refers to regions and companies that can cooperate in supplying relief supplies.

[0136] "Feedback" refers to opinions and requests received from disaster victims and aid organizations regarding the needs and supply status of supplies.

[0137] A "supply plan" is a plan for appropriately supplying relief supplies to disaster-stricken areas, and is updated based on collected data and feedback.

[0138] The system for implementing this invention collects and analyzes various data in order to quickly and appropriately supply relief supplies to disaster-stricken areas in the event of a disaster, and optimizes the type and quantity of supplies and delivery routes based on the results.

[0139] Data acquisition and analysis

[0140] 1. Server: The server obtains weather data and disaster impact data from disaster information providers such as the Japan Meteorological Agency through API requests, including seismic intensity data and damage extent data.

[0141] Hardware and software used: curl command, MongoDB database

[0142] Example: Seismic intensity 7, damage area 50 square kilometers, number of affected people 20,000

[0143] 2. Server: The server analyzes the acquired data using a Python program and calculates an impact score based on factors such as the seismic intensity, the extent of the damage, and the number of victims.

[0144] Hardware and software used: Python, pandas library

[0145] Example: Seismic intensity x 1.5 + damage area x 0.3 + number of affected people x 0.2 = impact score 9.5

[0146] 3. Server: The server stores the analysis results in the MongoDB database again and uses them for further processing.

[0147] Image analysis and delivery route optimization

[0148] 1. Server: The server acquires remote image data obtained from satellites and drones and stores it in NAS (Network Attached Storage).

[0149] Hardware and software used: FTP client, NAS

[0150] Examples: Satellite images (wide area of ​​the affected area), drone images (detailed road conditions)

[0151] 2. Server: The server performs image analysis using the OpenCV library to identify road conditions and obstacles in the affected area.

[0152] Hardware and software used: OpenCV library, GeoJSON format

[0153] Example: Identifying road closures and detour routes

[0154] 3. Server: The server uses GIS (Geographic Information System) tools to calculate the optimal delivery route. For this, we use QGIS software.

[0155] Hardware and software used: QGIS Python API

[0156] Example: A main road is blocked, so a detour route is calculated to determine the shortest delivery route.

[0157] Population data analysis and supply decisions

[0158] 1. Server: The server retrieves population density and population pyramid data from government statistical sites and saves them in CSV format.

[0159] Hardware and software used: crawler, CSV format

[0160] Example: Population density 1000 people / km2, population pyramid (30% elderly, 20% children)

[0161] 2. Server: The server analyzes the acquired demographic data to estimate the age group, gender, and health status of the victims, and determines the type and amount of relief supplies needed.

[0162] Hardware and software used: Python, pandas library

[0163] Example: Identifying medicines for the elderly, food for children, and water supplies

[0164] Identifying and notifying cooperating regions and companies

[0165] 1. Server: The server identifies regions and companies that can cooperate from the database based on the type and quantity of relief supplies acquired and their necessity.

[0166] Hardware and software used: SQL queries, database

[0167] Example: Supply of medicines from neighboring cities, delivery support by logistics companies

[0168] 2. Server: The server notifies the identified partners via email or SMS and promptly begins supplying supplies.

[0169] Hardware and software used: SMTP server

[0170] Example: Automatically send emails to partner companies requesting the supply of supplies

[0171] Gather feedback and update supply plans

[0172] 1. Terminals: The terminals provide accessible web forms and mobile apps for disaster victims and relief organizations to input their supply needs and emergency requests.

[0173] Hardware and software used: React, Node.js

[0174] Example: Evacuees enter their required supplies via a web form or mobile app.

[0175] 2. Users: Victims and relief organizations input their actual needs and feedback.

[0176] Example: Necessary supplies (fresh water, food, blankets)

[0177] 3. Server: The server analyzes the collected feedback and reflects it in real time in the current supply plan, thereby improving the efficiency of supply.

[0178] Hardware and software used: Machine learning model

[0179] Example: Based on feedback, we increased the supply of fresh water and added more types of medical supplies.

[0180] With this mechanism, the system of the present invention can quickly collect and analyze a variety of data, enabling optimal provision and delivery of relief supplies.

[0181] Prompt Sentence Examples

[0182] "Get the current seismic intensity data and calculate the impact score."

[0183] "Analyze the latest satellite and drone images of the affected area and calculate the optimal delivery route."

[0184] "Get the latest population data from government statistical sites and estimate the age range of the affected people."

[0185] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0186] Step 1:

[0187] Server: The server sends API requests from disaster information providers such as the Japan Meteorological Agency to obtain weather data and disaster impact data (seismic intensity data and damage extent data). The API request is used as input, and the output is disaster data in JSON format. Specifically, the server executes the API request using the curl command and stores the obtained data in a MongoDB database.

[0188] Step 2:

[0189] Server: The server analyzes the acquired disaster data using a Python program and calculates the impact score. The input is disaster data in JSON format, and the output is a numerical impact score. For data processing, the Python pandas library is used to import the data into a data frame and calculate the impact score based on a formula. Specifically, the server calculates the impact score and stores it again in the MongoDB database.

[0190] Step 3:

[0191] Server: The server obtains remote image data from satellite data providers and drone operators. The input is image data in response to a request, and the output is an image file stored in a NAS (Network Attached Storage). Specifically, the server downloads the image data using an FTP client and stores it in the NAS.

[0192] Step 4:

[0193] Server: The server uses the OpenCV library to analyze the acquired remote image data and identify road conditions and obstacles in the disaster area. The input is image data, and the output is analysis data including road conditions and obstacle location information. For data processing, OpenCV is used to extract important features from the image and save them in GeoJSON format. Specifically, the server identifies road closures and detour routes in the image.

[0194] Step 5:

[0195] Server: The server uses GIS (geographic information system) tools to calculate the optimal delivery route. The input is road condition analysis data, and the output is coordinate information for the delivery route. For data processing, the QGIS Python API is used to calculate the optimal route that avoids closed roads, and the results are saved in a database. Specifically, the server creates the shortest delivery route and saves it in GeoJSON format.

[0196] Step 6:

[0197] Server: The server retrieves population density and population pyramid data from government statistical websites. The input is a request from the crawler, and the output is population data in CSV format. Specifically, the server runs the crawler periodically, downloads CSV data from government statistical websites, and uses it for analysis.

[0198] Step 7:

[0199] Server: The server analyzes the acquired population data and estimates the age group, gender, and health status of the victims. The input is population data in CSV format, and the output is estimated information about the victims. For data processing, the server aggregates the data using Python's pandas library, and determines the type and amount of relief supplies needed based on the results. Specific operations include creating lists of medicines for the elderly, food for children, etc.

[0200] Step 8:

[0201] Server: The server identifies regions and companies that can cooperate based on the type and amount of relief supplies from a database. The input is the required relief supply data, and the output is a list of cooperation partners. Data processing involves extracting the necessary information from the database using SQL queries. Specifically, the server obtains contact information for cooperation regions and companies.

[0202] Step 9:

[0203] Server: The server notifies the partners via email or SMS. The input is the list of partners and the notification content, and the output is the status of completion of the transmission. Specifically, the server automatically sends an email requesting the supply of supplies via the SMTP server.

[0204] Step 10:

[0205] Terminal: The terminal provides a web form and a mobile app that can be accessed by disaster victims and relief organizations, allowing them to enter their supply needs and emergency requests. The input is feedback information entered by the user, and the output is feedback data stored in a database. To implement the specific operation, the web form and mobile app are implemented using React and Node.js.

[0206] Step 11:

[0207] User: Victims and relief organizations enter their actual needs and feedback. The input is information about the supplies they need (e.g., fresh water, food, blankets), and the output is saved in the database as feedback. Specific actions involve the user entering information into a form and submitting it.

[0208] Step 12:

[0209] Server: The server analyzes the collected feedback and updates the supply plan in real time. The input is the feedback data, and the output is the updated supply plan. For data processing, a machine learning model is used to analyze the feedback data and dynamically adjust the supply plan. Specifically, the server updates the supply volume and type of materials based on the analysis results.

[0210] The above processing steps enable rapid collection and analysis of a variety of data, enabling the provision and delivery of appropriate relief supplies.

[0211] (Application example 1)

[0212] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0213] In the event of a disaster, it is extremely important to quickly and appropriately supply relief supplies to affected areas. However, in conventional systems, the collection and analysis of various data tended to be done manually, resulting in supply delays and shortages or surpluses of supplies. It was also difficult to grasp road and traffic conditions in affected areas in real time, making it difficult to select appropriate delivery routes. Furthermore, there was a lack of efficient means to collect real-time needs from disaster victims, which often led to supply plans deviating from actual needs. The present invention is provided to solve these problems.

[0214] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0215] In this invention, the server includes means for acquiring seismic intensity data and damage extent data, means for analyzing the acquired seismic intensity data and damage extent data and calculating an impact score, means for storing the analysis results in a database, means for acquiring satellite images and unmanned aerial vehicle images, means for analyzing the acquired images and identifying road conditions in the disaster area, means for calculating optimal delivery routes using a geographic information system and storing the results in a database, means for acquiring population density and demographic data, means for generating estimated information on disaster victims based on the acquired data, means for determining the type and amount of relief supplies, means for identifying and notifying regions and organizations that can cooperate, means for collecting feedback from disaster victims and support organizations, means for analyzing the collected feedback and updating a supply plan, means for optimizing delivery routes using a generative AI model, and means for collecting information on the need for supplies from disaster victims in real time using a smartphone application. This allows for rapid collection and analysis of a wide variety of data, enabling optimal provision and delivery of relief supplies.

[0216] "Seismic intensity data" is data that indicates the strength of shaking when an earthquake occurs.

[0217] "Damage extent data" is data that indicates the extent of the area affected by a disaster.

[0218] The "impact score" is a quantitative score that indicates the impact of a disaster, calculated based on factors such as seismic intensity, the extent of damage, and the number of victims.

[0219] "Satellite imagery" refers to images of the Earth's surface taken by an artificial satellite located in space.

[0220] "Unmanned aerial vehicle images" are images taken from the sky by unmanned aerial vehicles such as drones.

[0221] A "geographic information system" is a system for collecting, displaying, and analyzing geographic information.

[0222] "Population density data" is data that indicates the number of people per unit area within a specific region.

[0223] "Demographic data" refers to data that shows the distribution of age groups, gender, and other demographic attributes in a certain region or group.

[0224] "Estimated information" refers to information such as the age group, gender, and health status of victims estimated based on the data obtained.

[0225] "Relief supplies" are essential items such as food, water, and medical supplies that are supplied to disaster-stricken areas during disasters.

[0226] A "generative AI model" is a model that uses artificial intelligence to perform data analysis and inference.

[0227] A "smartphone application" is a software program that runs on a smartphone and is used to collect data from users.

[0228] "Feedback" is information collected from disaster victims and aid organizations regarding an assessment of current needs and supplies.

[0229] MODE FOR CARRYING OUT THE INVENTION

[0230] This section describes a system for implementing the present invention. This system collects and analyzes a wide variety of data to quickly and appropriately supply relief supplies to disaster-stricken areas in the event of a disaster, and uses that data to optimize the type and quantity of supplies and delivery routes.

[0231] 1. Data acquisition and analysis

[0232] The server obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency, analyzes this data, and calculates an impact score. This impact score is calculated based on indicators such as seismic intensity, damage extent, and number of victims, and the analysis results are stored in a database. For example, if an earthquake with a seismic intensity of 7 occurs, the damage extent is 50 square kilometers, and the number of victims is 20,000, the impact score is calculated as follows:

[0233] Examples:

[0234] Seismic intensity data: 7

[0235] Area of ​​damage: 50 square kilometers

[0236] Number of victims: 20,000

[0237] Impact score: Seismic intensity x 1.5 + damage area x 0.3 + number of victims x 0.2 = impact score 9.5

[0238] 2. Image analysis and delivery route optimization

[0239] The server acquires satellite and drone imagery and performs image analysis to identify road conditions and obstacles in the affected area, then uses a geographic information system (GIS) to calculate optimal delivery routes and identify routes that avoid blocked roads.

[0240] Examples:

[0241] Satellite image: Wide range of the affected area

[0242] Unmanned aerial vehicle imagery: detailed road conditions

[0243] Delivery route optimization: When a main road is blocked, a detour route is calculated to determine the shortest delivery route.

[0244] 3. Population data analysis and supply decisions

[0245] The server obtains population density and demographic data from government and statistical agencies, and uses this information to estimate the age, gender, and health status of disaster victims. This information is then used to determine the type and quantity of relief supplies needed.

[0246] Examples:

[0247] Population density: 1,000 people per square kilometer

[0248] Demographics: 30% elderly, 20% children

[0249] Relief supply determination: Identify supplies of medicine for the elderly, food for children, and water.

[0250] 4. Identifying and notifying cooperating regions and organizations

[0251] The server identifies regions and organizations that can cooperate based on the type and quantity of relief supplies received and the need for them, and notifies them. At this time, notifications are sent to quickly begin supplying supplies.

[0252] Examples:

[0253] Medicine supplies from nearby cities

[0254] Delivery support by logistics companies

[0255] 5. Gather feedback and update supply plans

[0256] The user provides a smartphone application that disaster victims and relief organizations can access, allowing them to input their needs and urgent requests for supplies. The feedback is analyzed by the server and reflected in the current supply plan.

[0257] Examples:

[0258] Using a web form or smartphone app, disaster victims can input the supplies they need.

[0259] Feedback: Request for fresh water, food, and blankets

[0260] Updated supply plan based on feedback: Increased fresh water supply and added more medical supplies

[0261] 6. Use of generative AI models

[0262] The server uses a generative AI model to optimize delivery routes and generate estimated information. By inputting prompt statements to the AI ​​model, efficient analysis becomes possible.

[0263] Examples:

[0264] Disaster data: intensity: 7, affected_area: 50, affected_population: 20000

[0265] What is the best delivery route?

[0266] Also, what types and amounts of food do you need?

[0267] In this way, the system of the present invention can quickly collect and analyze a wide variety of data to provide and deliver optimal relief supplies, thereby significantly improving support for disaster victims.

[0268] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0269] Step 1:

[0270] The server receives real-time seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. Based on the input seismic intensity data and damage extent data, the server records them in a database. During this process, the latest information is collected using APIs and data feeds.

[0271] Step 2:

[0272] The server analyzes the acquired seismic intensity data and damage extent data and calculates an impact score. This weights each data point and derives an impact score. Specifically, the impact score is calculated by multiplying the seismic intensity by 1.5, the damage extent by 0.3, and the number of victims by 0.2.

[0273] Input: Seismic intensity data, damage area data, number of victims

[0274] Output: Impact score

[0275] Step 3:

[0276] The server stores the calculated impact score and the analysis results in a database. The impact score is important data to be used in the future analysis process.

[0277] Step 4:

[0278] The server acquires various satellite and unmanned aerial vehicle images, analyzes the image data, and uses machine learning algorithms to identify road conditions and obstacle locations throughout the disaster area.

[0279] Input: Satellite images, unmanned aerial vehicle images

[0280] Output: Road conditions in the affected area, location of obstacles

[0281] Step 5:

[0282] The server uses a geographic information system (GIS) to calculate the optimal delivery route, identifying routes that avoid road blocks and obstacles, and stores the optimized delivery route in a database.

[0283] Input: Road conditions, obstacle locations

[0284] Output: Optimized delivery route

[0285] Step 6:

[0286] The server obtains population density and demographic data from government and statistical agencies, analyzes it, estimates the age, gender, and health status of disaster victims, and uses this information to determine the type and amount of relief supplies needed.

[0287] Input: population density data, population composition data

[0288] Output: Type and quantity of relief supplies

[0289] Step 7:

[0290] The server identifies and notifies regions and organizations that can cooperate based on the type and amount of relief supplies, implementing protocols to quickly contact nearby cities and logistics companies.

[0291] Input: Type and quantity of relief supplies

[0292] Output: Notification to cooperating regions and organizations

[0293] Step 8:

[0294] The device uses a smartphone application to collect feedback from disaster victims and relief organizations, through which disaster victims input their needs and emergency requests.

[0295] Input: Feedback from victims and relief organizations

[0296] Output: Collected feedback data

[0297] Step 9:

[0298] The server analyzes the collected feedback and updates the supply plan, reevaluating the types and quantities of supplies to be supplied and adjusting them as necessary.

[0299] Input: Feedback data

[0300] Output: Updated supply plan

[0301] Step 10:

[0302] The server uses the generative AI model to optimize delivery routes and generate estimated information. It inputs prompts to the AI ​​model to achieve efficient analysis.

[0303] Input: prompt statement, real-time data

[0304] Output: Optimized delivery route, estimated information

[0305] As a concrete example, the following prompt sentence is input to the AI ​​model:

[0306] Disaster data: intensity: 7, affected_area: 50, affected_population: 20000

[0307] What is the best delivery route?

[0308] Also, what types and amounts of food do you need?

[0309] This will enable us to respond effectively and quickly to any disasters that occur.

[0310] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0311] This article describes a system for implementing the present invention. This system collects and analyzes a variety of data, and combines it with an emotion engine that recognizes the user's emotions to optimize the type and quantity of supplies and delivery routes in order to quickly and appropriately provide relief supplies to disaster-stricken areas in the event of a disaster.

[0312] Program processing and specific examples

[0313] Data acquisition and analysis

[0314] 1. The server obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. This data includes the time of the earthquake, epicenter, damage extent, number of victims, etc. For example, it accesses a specified API using an HTTP request to obtain the required data.

[0315] Examples:

[0316] Seismic intensity data: 7

[0317] Area of ​​damage: 50 square kilometers

[0318] Number of victims: 20,000

[0319] 2. The server integrates the acquired seismic intensity data and damage extent data to calculate an impact score. The impact score is calculated by combining the seismic intensity, damage extent, number of victims, etc. For example, the formula used is seismic intensity x 1.5 + damage extent x 0.3 + number of victims x 0.2.

[0320] Examples:

[0321] Seismic intensity x 1.5 + damage area x 0.3 + number of affected people x 0.2 = impact score 9.5

[0322] 3. The server stores the calculated impact score in a database and uses it for subsequent analysis and processing.

[0323] Image analysis and delivery route optimization

[0324] 1. The server acquires satellite and drone images and performs image analysis to identify road conditions and the location of obstacles in the affected area. It uses an image recognition algorithm based on deep learning to detect damaged roads and obstacles.

[0325] Examples:

[0326] Satellite image: Wide range of the affected area

[0327] Drone imagery: detailed road conditions

[0328] 2. The server uses a GIS (geographic information system) to calculate the optimal delivery route, using a shortest path algorithm to identify the most efficient route to evacuation shelters or collection points.

[0329] Examples:

[0330] The main road is closed, so a detour route is calculated to determine the shortest delivery route.

[0331] Population data analysis and supply decisions

[0332] 1. The server retrieves population density and population pyramid data from government or statistical agencies, including the number of people in a particular area and their age distribution. It retrieves the data using an API request.

[0333] Examples:

[0334] Population density: 1,000 people per square kilometer

[0335] Population pyramid: 30% elderly, 20% children

[0336] 2. The server analyzes the population data and estimates the age, gender, and health status of the victims. Based on this information, it determines the type and amount of relief supplies needed.

[0337] Examples:

[0338] Identify supplies of medicine for the elderly, food for children, and water

[0339] Use of emotion engine

[0340] 1. The device provides an accessible communication platform for disaster victims and relief organizations to input their needs and feedback. The platform is equipped with an emotion engine that recognizes the user's emotional state as they input their needs.

[0341] Examples:

[0342] Victims enter the supplies they need through a web form or mobile app, and the emotion engine analyzes the emotions from the text input.

[0343] 2. Users, as disaster victims or relief organizations, input their needed supplies and urgent requests through the platform, and the emotion engine recognizes emotions in real time and collects the data.

[0344] Examples:

[0345] Supplies needed: Fresh water, food, blankets. Emotional state: "Anxious" "Urgent".

[0346] 3. The server analyzes the collected feedback and emotion data in real time and reflects it in the current supply plan. By analyzing the feedback and emotion data together, the urgency and priority of supplies can be determined with even greater accuracy.

[0347] Examples:

[0348] Based on feedback, we've increased the supply of fresh water, added new types of medical supplies, and prioritized the delivery of some supplies based on emotion data.

[0349] 4. The server updates supply plans in real time and addresses the psychological needs of disaster victims, ensuring more accurate distribution of supplies.

[0350] In this way, by using the system according to the present invention, it is possible to quickly collect and analyze various data and the emotional state of the user, and to provide and deliver optimal relief supplies.

[0351] The processing flow will be explained below.

[0352] Step 1:

[0353] Server: Obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. This includes information such as the time of the earthquake, epicenter, damage extent, and number of victims. For example, an HTTP request is used to access a specified API and obtain the required data.

[0354] Step 2:

[0355] Server: The acquired seismic intensity data and damage extent data are integrated to calculate the impact score. The impact score is calculated by combining the seismic intensity, damage extent, number of victims, etc. For example, the formula used is seismic intensity x 1.5 + damage extent x 0.3 + number of victims x 0.2.

[0356] Step 3:

[0357] Server: The calculated impact scores are stored in a database for later analysis and processing.

[0358] Step 4:

[0359] Server: Acquires satellite and drone imagery. This includes retrieving data from imagery providers and drone operators. For example, downloading image data from an AWS S3 bucket.

[0360] Step 5:

[0361] Server: Analyzes acquired satellite and drone images to identify road conditions and transportation options in the affected areas. Utilizes deep learning-based image recognition algorithms to detect damaged roads and obstacles.

[0362] Step 6:

[0363] Server: Based on the analysis results, the GIS system is used to calculate the optimal delivery route. A shortest path algorithm is used to identify the most efficient route to evacuation shelters and collection points.

[0364] Step 7:

[0365] Server: Stores the generated delivery route information in a database, allowing logistics planning to proceed efficiently.

[0366] Step 8:

[0367] Server: Obtain population density and population pyramid data from governments and statistical agencies. This includes the number of people in a particular area and their age distribution. Obtain the data using API requests.

[0368] Step 9:

[0369] Server: Analyzes acquired population data to estimate the age, gender, and health status of disaster victims. Performs statistical analysis to determine the type and amount of relief supplies based on specific needs.

[0370] Step 10:

[0371] Server: Based on the estimated number of victims, calculates the type and amount of relief supplies needed. Selects supplies according to specific needs, such as medicines for the elderly or nutritional foods for children.

[0372] Step 11:

[0373] Server: Searches the database for regions and companies that can cooperate, and identifies the most suitable partner based on the type and quantity of supplies and speed of supply.

[0374] Step 12:

[0375] Server: Notify the identified collaborators. This is done using an automatic email sending system or emergency contact tool.

[0376] Step 13:

[0377] Server: Receives responses from partners and reflects them in the supply plan. This updates the supply status of materials in real time.

[0378] Step 14:

[0379] Terminal: Provides an accessible communication platform for disaster victims and relief organizations to input their needs and feedback. The platform is equipped with an emotion engine that recognizes the user's emotional state as they input.

[0380] Step 15:

[0381] Users: Victims and relief organizations input their needs and urgent requests through the platform. The emotion engine recognizes emotions in real time and collects the data.

[0382] Step 16:

[0383] Server: Analyzes collected feedback and sentiment data in real time and reflects it in the current supply plan. By analyzing feedback and sentiment data together, the urgency and priority of supplies can be determined with even greater accuracy.

[0384] Step 17:

[0385] Server: Updates supply plans in real time and addresses the psychological needs of disaster victims, ensuring more accurate distribution of supplies.

[0386] Example 2

[0387] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0388] Conventional disaster relief systems have struggled to collect and analyze diverse data quickly and accurately, and efficiently provide and deliver relief supplies. Furthermore, they lack the ability to incorporate feedback from disaster victims and relief organizations, making it difficult to formulate supply plans that respond to actual needs. Furthermore, supply plans do not take into account the psychological state of disaster victims, making it difficult to improve disaster satisfaction.

[0389] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0390] In this invention, the server includes means for acquiring seismic intensity data and damage extent data, means for analyzing the acquired seismic intensity data and damage extent data to calculate an impact score, means for saving the analysis results in a database, means for acquiring satellite images and aerial images, means for analyzing the acquired images to identify road conditions and the locations of obstacles in the disaster area, means for calculating an optimal delivery route using an optimal route algorithm and saving the results in a database, means for acquiring population density and population composition data, means for generating estimated information on disaster victims based on the acquired data, means for determining the type and quantity of relief supplies, means for identifying and notifying regions and organizations that can cooperate, means for collecting feedback from disaster victims and relief organizations, means for analyzing the collected feedback and updating the supply plan, means for recognizing emotional states from user input and collecting that data, and means for analyzing the emotional data to determine the urgency and priority of supplies and reflecting the results in the supply plan.

[0391] This will enable the rapid collection of diverse data, highly accurate analysis, and optimization of relief supply taking into account user emotions.

[0392] "Seismic intensity data" is a numerical value that indicates the strength of earthquake shaking and quantitatively represents the impact of an earthquake.

[0393] "Damage extent data" means data that indicates the extent of damage in a disaster-affected region and identifies the geographic area affected.

[0394] The "impact score" is a numerical value calculated by combining multiple data elements, such as seismic intensity data, damage extent data, and number of victims, and is used to comprehensively evaluate the impact of a disaster.

[0395] "Satellite imagery" means an image of a geographic area taken from a satellite that has a wide field of view and shows the ground in detail.

[0396] "Airborne imagery" means images of a geographic area taken from a drone or other aircraft that provide a detailed view from a low altitude.

[0397] An "optimal route algorithm" is an algorithm that calculates the shortest distance to a destination or the most efficient route, and is used to optimize delivery routes.

[0398] "Population density data" is data that indicates the density of people living in a particular area and is used to understand the population distribution of the area.

[0399] "Demographic data" refers to data that shows the population distribution of age, gender, etc. in a specific area, and is used to understand the characteristics of disaster victims.

[0400] "Estimated information on victims" is information used to estimate the age group, gender, health status, etc. of victims based on acquired population data, etc., and to assess the need for relief.

[0401] "Relief supplies" are items needed when a disaster occurs, such as food, medicine, clothing, and water, to support the lives of disaster victims.

[0402] "Regions and organizations that can cooperate" refers to regions and cooperative organizations that can support relief efforts in the event of a disaster, including the provision of relief supplies and human support.

[0403] "Feedback" refers to information collected from disaster victims and relief organizations, and indicates the needs for supplies, the situation on the ground, and the level of urgency.

[0404] "Emotional state" indicates the emotional state the user is feeling, and recognizes emotions such as anxiety, urgency, and relief in real time.

[0405] The system of the present invention collects and analyzes a variety of data, and further combines it with an emotion engine that recognizes the user's emotions to optimize the type and quantity of supplies and delivery routes in order to quickly and appropriately supply relief supplies to disaster-stricken areas in the event of a disaster. Specific embodiments of the system are described below.

[0406] Hardware and Software Configuration

[0407] This system uses the following hardware and software:

[0408] Server: A computer system with high-performance computing power that collects, analyzes, stores, and calculates routes.

[0409] Terminal: A device that provides a web browser and mobile apps for use by disaster victims and relief organizations.

[0410] Emotion engine: A software module for recognizing the emotional state from user input, such as a sentiment analysis tool that uses natural language processing (NLP) techniques.

[0411] GIS (Geographic Information System): A tool that handles geographic data and calculates optimal delivery routes. Examples include QGIS and ArcGIS.

[0412] Image recognition algorithm: A deep learning algorithm used to analyze satellite and aerial images. TensorFlow and PyTorch are used.

[0413] Data collection and analysis

[0414] 1. The server obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. This data includes the time of the earthquake, epicenter, damage extent, and number of victims. As a specific example, it accesses the API using an HTTP request and obtains a response in JSON format.

[0415] For example, send a request like GET / earthquake / data.

[0416] 2. The server integrates the acquired seismic intensity data and damage extent data to calculate the impact score. The impact score is calculated using the formula: seismic intensity x 1.5 + damage extent x 0.3 + number of victims x 0.2.

[0417] Example: If the earthquake has a magnitude of 7, the damage area is 50 square kilometers, and the number of affected people is 20,000, the impact score is 7 x 1.5 + 50 x 0.3 + 20,000 x 0.2 = 9.5.

[0418] Image analysis and delivery route optimization

[0419] 1. The server acquires satellite and aerial imagery and analyzes them using an image recognition algorithm based on deep learning, thereby identifying road damage and the location of obstacles.

[0420] Example: Access the GET / satellite_images and GET / drone_images endpoints to retrieve and analyze image data.

[0421] 2. The server uses GIS to calculate the optimal delivery route, using a shortest path algorithm (e.g., Dijkstra algorithm) to identify the most efficient route to a shelter or collection point.

[0422] Example: If a major road is blocked, calculate a detour route to create the shortest route.

[0423] Population data analysis and material decisions

[0424] 1. The server obtains population density and demographic data from government and statistical agencies, including the number of people in a particular area and their age distribution.

[0425] Example: Access the GET / population_data endpoint to retrieve data.

[0426] 2. The server analyzes the acquired population data to estimate the age, gender, and health status of the victims. Based on this information, it determines the type and amount of relief supplies needed.

[0427] Examples: Identifying supplies of medicine for the elderly, food for children, and water.

[0428] Use of emotion engine

[0429] 1. The device provides a communication platform for disaster victims and relief organizations, allowing them to input their needs and feedback. It also has a built-in emotion engine that recognizes the user's emotional state from their input.

[0430] Example: When a disaster victim enters "I need fresh water. I'm anxious" through a web form or mobile app, the emotion engine recognizes the emotion "anxiety."

[0431] 2. The server analyzes the collected feedback and emotion data in real time and reflects it in the supply plan, thereby determining the urgency and priority of supplies with even greater accuracy.

[0432] Example: Increase the supply of fresh water based on feedback, and prioritize the delivery of certain supplies based on sentiment data.

[0433] Prompt Sentence Examples

[0434] By inputting the following prompt sentence into the generative AI model, it is possible to generate a list of supplies appropriate for the needs of the disaster-stricken area.

[0435] Please create a list of supplies needed for elderly people in the disaster-stricken areas. There has been a magnitude 7 earthquake, affecting an area of ​​50 square kilometers and affecting 20,000 people. Many of the victims are feeling anxious and in a state of emergency.

[0436] This invention enables rapid collection and highly accurate analysis of various data during disasters, as well as optimization of relief supplies that take into account the emotions of users, thereby enabling rapid and accurate support for disaster victims.

[0437] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0438] Step 1: Obtain disaster information

[0439] 1. The server obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. Specifically, it sends an HTTP request to access a specified API and obtains data such as the time of the earthquake, epicenter, damage extent, and number of victims. The input is a request to the API endpoint, and the output is the obtained seismic intensity data and damage extent data in JSON format.

[0440] What it does: The server sends a request to an API endpoint such as GET / earthquake / data, parses the data received in response, and stores it in an internal database.

[0441] Step 2: Calculating the impact score

[0442] 2. The server integrates the acquired seismic intensity data and damage extent data to calculate an impact score. The impact score is calculated using the formula: seismic intensity x 1.5 + damage extent x 0.3 + number of victims x 0.2. The input is the acquired seismic intensity data and damage extent data, and the output is the impact score.

[0443] What it does: The server reads each data point as a number and calculates an impact score based on a formula.

[0444] Step 3: Save the impact score

[0445] 3. The server stores the calculated impact score in a database and uses it for subsequent analysis and processing. The input is the calculated impact score, and the output is the result stored in the database.

[0446] Specific behavior: The server executes an SQL query such as INSERT INTO impact_scores (timestamp, score) VALUES (current_timestamp, impact_score).

[0447] Step 4: Image acquisition and analysis

[0448] 4. The server acquires satellite images and drone images and analyzes them using an image recognition algorithm based on deep learning. The input is the satellite images and drone images, and the output is the analysis results (data showing road conditions and the location of obstacles).

[0449] Specific operation: The server accesses the GET / satellite_images and GET / drone_images endpoints to obtain image data, analyzes it using TensorFlow, PyTorch, etc., and detects damaged roads and obstacles.

[0450] Step 5: Optimize delivery routes

[0451] 5. The server uses GIS to calculate the optimal delivery route. Here, a shortest path algorithm (e.g., Dijkstra algorithm) is used. The input is the analyzed road condition data, and the output is the optimal delivery route.

[0452] Specific operation: The server analyzes road network data using tools such as QGIS and ArcGIS, calculates the shortest route, and stores it in a database.

[0453] Step 6: Collect and analyze population data

[0454] 6. The server obtains population density and demographic data from government and statistical bureaus, and analyzes it to estimate the age, gender, and health status of disaster victims. Based on this information, it determines the type and amount of relief supplies. The input is the obtained population data, and the output is a list of needed relief supplies.

[0455] Specific operation: The server accesses the GET / population_data endpoint to collect data, analyzes the data using analysis tools such as Pandas, and generates a supply list based on the estimated results.

[0456] Step 7: Collect feedback and sentiment data

[0457] 7. The device provides an accessible communication platform for disaster victims and relief organizations, allowing them to input their needs and feedback. The emotion engine recognizes their emotional state from these inputs. The input is user feedback and emotion data, and the output is analyzed data.

[0458] Specific operation: The device provides a web form or mobile app, collects user input data, and sends it to the server. The emotion engine analyzes the input text and generates emotion data. When a user inputs "I need fresh water. I'm anxious," the emotion engine analyzes the text and recognizes the emotion "anxiety."

[0459] Step 8: Update the supply plan

[0460] 8. The server analyzes the collected feedback and emotion data in real time and reflects it in the supply plan. It determines the urgency and priority of supplies with high accuracy. The input is feedback and emotion data, and the output is an updated supply plan.

[0461] Specific operation: The server integrates feedback data and emotion data, and applies a priority algorithm to optimize supply planning. Based on feedback, it increases the supply of fresh water, and prioritizes the delivery of some supplies based on emotion data.

[0462] Through the above steps, the system of the present invention quickly collects and analyzes various data and the user's emotional state, and realizes optimal provision and delivery of relief supplies.

[0463] (Application example 2)

[0464] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0465] In the event of a disaster, it is important to provide appropriate relief supplies to affected areas quickly. However, it is difficult to grasp the state of the affected area and the needs of the victims in real time, making it difficult to optimize the type and quantity of supplies and delivery routes. It is also necessary to update supply plans that take into account the psychological state and urgency of the victims.

[0466] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0467] In this invention, the server includes means for acquiring seismic intensity data and damage extent data, means for analyzing the acquired seismic intensity data and damage extent data and calculating an impact score, means for saving the analysis results in a database, means for acquiring satellite images and drone images, means for analyzing the acquired images and identifying road conditions in the disaster area, means for calculating an optimal delivery route and saving the results in a database, means for acquiring population density and population pyramid data, means for generating estimated information on disaster victims based on the acquired data, means for determining the type and amount of relief supplies, means for identifying and notifying regions and companies that can cooperate, means for collecting feedback from disaster victims and relief organizations, means for analyzing the collected feedback and updating the supply plan, means for recognizing the emotional state of disaster victims using an emotion engine, and means for predicting the supplies needed by disaster victims using a generative AI model. This allows the situation in the disaster area and the needs of disaster victims to be grasped in real time, enabling the provision and delivery of optimal relief supplies.

[0468] "Seismic intensity data" is numerical information that indicates the strength of earthquake shaking.

[0469] "Damage extent data" is information indicating the size and extent of the area affected by a disaster.

[0470] The "impact score" is a number that indicates the degree of impact of the damage, calculated based on data such as seismic intensity, area of ​​damage, and number of victims.

[0471] A "satellite image" is an image of the Earth's surface taken by an artificial satellite.

[0472] "Drone imagery" is a detailed image of the Earth's surface taken by an unmanned aerial vehicle (drone).

[0473] "Road conditions" is information indicating the state of road damage and whether the road is passable.

[0474] A "delivery route" is the route along which goods are delivered to their destination.

[0475] "Population density" is a statistical information that indicates the number of people per certain area.

[0476] "Population pyramid data" is statistical information that shows the distribution of age groups and gender in a particular area.

[0477] "Estimated information on victims" refers to information such as the age group, gender, and health status of victims estimated based on population data.

[0478] "Relief supplies" are goods provided to disaster victims in the event of a disaster, including food, water, medicine, etc.

[0479] "Regions and companies that can cooperate" refers to regions and companies that can provide support in the event of a disaster.

[0480] "Feedback" refers to information such as requests, opinions, and reports from disaster victims and relief organizations.

[0481] An "emotion engine" is an algorithm or software that recognizes a user's emotional state.

[0482] A "generative AI model" is a model that uses artificial intelligence to predict needed supplies and information from input data.

[0483] The system for carrying out the present invention will now be described in detail. This system is a smartphone application for quickly and appropriately supplying relief supplies to disaster-stricken areas, particularly in the event of a disaster.

[0484] First, the server obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. The server uses an HTTP request to access a specified API and obtains data such as the time of the earthquake, epicenter, damage extent, and number of victims. Examples include "seismic intensity data: seismic intensity 7," "damage extent: 50 square kilometers," and "number of victims: 20,000." Based on the obtained data, the server calculates an impact score and stores the results in a database.

[0485] Next, the server acquires satellite and drone images and uses deep learning models (e.g., TensorFlow, PyTorch) to identify road conditions in the affected areas. The server analyzes these images to detect damaged roads and obstacles, calculates optimal delivery routes, and stores them in a database. For example, if a main road is blocked, the server can calculate a detour route to determine the shortest delivery route.

[0486] The server also obtains population density and population pyramid data from governments and statistical bureaus, and uses this data to estimate the age, gender, and health status of disaster victims. The server uses this information to determine the type and amount of relief supplies needed. Specific examples include identifying "medicine for the elderly," "food for children," and "water supplies."

[0487] The device also provides a communication platform accessible to disaster victims and relief organizations. This platform is equipped with an emotion engine that analyzes the emotional state of disaster victims from text input. For example, disaster victims can input their required supplies via a web form or mobile app, and data such as "emotional state: anxiety, emergency" can be collected.

[0488] The server analyzes the collected feedback and emotion data in real time and reflects it in the current supply plan. For example, it can increase the supply of fresh water and add more types of medical supplies based on the collected feedback. It can also prioritize the delivery of certain supplies based on emotion data.

[0489] Finally, the generative AI model is used to predict what supplies the disaster victims need. This is done using prompts, such as "What supplies do the disaster victims need?", which allows the generative AI model to return predictions such as "Clean water, non-perishable food, blankets, and medicines."

[0490] In this way, the system according to the present invention can realize efficient and appropriate provision and delivery of relief supplies in the event of a disaster.

[0491] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0492] Step 1:

[0493] The server obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. To do this, the server uses an HTTP request to access a specified API and obtains data such as the time of the earthquake, epicenter, damage extent, and number of victims. The input is the data from the API, and the output is to store this data in an internal structure.

[0494] Step 2:

[0495] The server analyzes the seismic intensity data and damage extent data acquired in step 1 and calculates the impact score. Specifically, the impact score is calculated using the formula: seismic intensity x 1.5, damage extent x 0.3, number of victims x 0.2. The input data are seismic intensity, damage extent, and number of victims, and the output is the calculated impact score.

[0496] Step 3:

[0497] The server stores the calculated impact score in a database. Using the analysis results, important information such as the impact score is stored in the database for later analysis and processing. The input is the impact score, and the output is the result stored in the database.

[0498] Step 4:

[0499] The server acquires satellite and drone images. To do this, the server accesses the corresponding data acquisition APIs and data streams to acquire image data. The input is image data from satellites and drones, and the output is the acquired image data.

[0500] Step 5:

[0501] The server analyzes the acquired images and identifies the road conditions in the disaster area. Deep learning models (e.g., TensorFlow, PyTorch) are used to detect damaged roads and obstacles in the images. The input is satellite and drone images, and the output is road condition data as the analysis result.

[0502] Step 6:

[0503] The server calculates the optimal delivery route based on the analysis results and stores the results in a database. It uses a GIS (geographic information system) to run a shortest path algorithm to identify the most efficient route to evacuation shelters and collection points. The input is road condition data, and the output is the optimal delivery route.

[0504] Step 7:

[0505] The server retrieves population density and population pyramid data from governments and statistical offices. To do this, the server uses an API request to retrieve the specified data. The input is the data from governments and statistical offices, and the output is the retrieved population data.

[0506] Step 8:

[0507] The server analyzes the acquired population data and estimates the age group, gender, and health status of the victims. The data is analyzed using Python's Pandas library, etc. The input is population density and population pyramid data, and the output is estimated victim information.

[0508] Step 9:

[0509] The server determines the type and amount of relief supplies needed based on the estimated information. It identifies the supplies needed based on the age group and health condition of the victims. Specific examples include "medicine for the elderly" and "food for children." The input is victim information, and the output is the type and amount of relief supplies.

[0510] Step 10:

[0511] The device provides an accessible communication platform for disaster victims and relief organizations to input their needs and feedback. This platform incorporates an emotion engine (e.g., Microsoft Azure's Text Analytics API) to analyze the user's emotional state from their text input. The input is text input from the user, and the output is analyzed emotional data.

[0512] Step 11:

[0513] The server analyzes the collected feedback and emotion data in real time and reflects it in the supply plan. By analyzing the feedback and emotion data, the urgency and priority of supplies can be determined with greater precision. Specific examples include "increase the supply of fresh water" and "add new types of medical supplies." The input is feedback and emotion data, and the output is an updated supply plan.

[0514] Step 12:

[0515] The server uses a generative AI model to predict the supplies needed by disaster victims. A prompt is input into the generative AI model to predict the supplies needed. For example, by inputting a prompt such as "Please tell us what supplies the disaster victims need," a prediction such as "Clean water, non-perishable food, blankets, and medicines are needed" can be obtained. The input is the prompt, and the output is the prediction result from the generative AI model.

[0516] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0517] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0518] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0519] [Second embodiment]

[0520] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0521] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0522] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0523] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0524] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0525] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0526] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0527] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0528] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0529] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0530] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0531] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0532] This section describes a system for implementing the present invention. This system collects and analyzes a wide variety of data in order to quickly and appropriately supply relief supplies to disaster-stricken areas in the event of a disaster, and then optimizes the type and quantity of supplies and delivery routes based on the results.

[0533] Program processing and specific examples

[0534] Data acquisition and analysis

[0535] 1. Server: Obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. This data includes the time of the earthquake, epicenter, damage extent, number of victims, etc.

[0536] Examples:

[0537] Seismic intensity data: 7

[0538] Area of ​​damage: 50 square kilometers

[0539] Number of victims: 20,000

[0540] 2. Server: Analyzes the acquired data and calculates the impact score. The impact score is calculated based on the seismic intensity, the extent of the damage, the number of victims, etc.

[0541] Examples:

[0542] Seismic intensity x 1.5 + damage area x 0.3 + number of affected people x 0.2 = impact score 9.5

[0543] 3. Server: The analysis results are stored in a database and used for subsequent processing.

[0544] Image analysis and delivery route optimization

[0545] 1. Server: Acquires satellite and drone images and performs image analysis to identify road conditions and the location of obstacles in the affected areas.

[0546] Examples:

[0547] Satellite image: Wide range of the affected area

[0548] Drone imagery: detailed road conditions

[0549] 2. Server: Uses GIS (geographic information systems) to calculate optimal delivery routes, avoiding blocked roads and identifying routes that will deliver goods quickly.

[0550] Examples:

[0551] The main road is closed, so a detour route is calculated to determine the shortest delivery route.

[0552] Population data analysis and supply decisions

[0553] 1. Server: Obtains population density and population pyramid data from government and statistical bureaus.

[0554] Examples:

[0555] Population density: 1,000 people per square kilometer

[0556] Population pyramid: 30% elderly, 20% children

[0557] 2. Server: Analyzes the acquired data and estimates the age, gender, and health status of the victims. Based on this information, the type and amount of relief supplies needed are determined.

[0558] Examples:

[0559] Identify supplies of medicine for the elderly, food for children, and water

[0560] Identifying and notifying cooperating regions and companies

[0561] 1. Server: Identifies neighboring areas and cooperating companies from a database based on the type and quantity of relief supplies obtained and their necessity.

[0562] Examples:

[0563] Supply of medicines from neighboring cities and delivery support from logistics companies

[0564] 2. Server: Notify the identified partners and promptly begin supplying supplies.

[0565] Gather feedback and update supply plans

[0566] 1. Terminal: Providing a platform that disaster victims and relief organizations can access and input their needs and emergency requests.

[0567] Examples:

[0568] Victims can enter their supply needs using a web form or mobile app.

[0569] 2. Users: Victims and relief organizations input their actual needs and feedback.

[0570] Examples:

[0571] Necessary supplies: fresh water, food, blankets

[0572] 3. Server: Analyzes the collected feedback and reflects it in the current supply plan, thereby ensuring more accurate supply of supplies to disaster-stricken areas.

[0573] Examples:

[0574] Based on feedback, we have increased the supply of fresh water and added more types of medical supplies.

[0575] In this way, by using the system according to the present invention, it is possible to quickly collect and analyze a variety of data and realize optimal provision and delivery of relief supplies.

[0576] The processing flow will be explained below.

[0577] Step 1:

[0578] Server: Obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. This includes information such as the time of the earthquake, epicenter, damage extent, and number of victims. For example, an HTTP request is used to access a specified API and obtain the required data.

[0579] Step 2:

[0580] Server: The acquired seismic intensity data and damage extent data are integrated to calculate the impact score. The impact score is calculated by combining the seismic intensity, damage extent, number of victims, etc. For example, the formula used is seismic intensity x 1.5 + damage extent x 0.3 + number of victims x 0.2.

[0581] Step 3:

[0582] Server: The calculated impact scores are stored in a database for later analysis and processing.

[0583] Step 4:

[0584] Server: Acquires satellite and drone imagery. This includes retrieving data from imagery providers and drone operators. For example, downloading image data from an AWS S3 bucket.

[0585] Step 5:

[0586] Server: Analyzes acquired satellite and drone images to identify road conditions and transportation options in the affected areas. Utilizes deep learning-based image recognition algorithms to detect damaged roads and obstacles.

[0587] Step 6:

[0588] Server: Based on the analysis results, the GIS system is used to calculate the optimal delivery route. A shortest path algorithm is used to identify the most efficient route to evacuation shelters and collection points.

[0589] Step 7:

[0590] Server: Stores the generated delivery route information in a database, allowing logistics planning to proceed efficiently.

[0591] Step 8:

[0592] Server: Obtain population density and population pyramid data from governments and statistical agencies. This includes the number of people in a particular area and their age distribution. Obtain the data using API requests.

[0593] Step 9:

[0594] Server: Analyzes acquired population data to estimate the age, gender, and health status of disaster victims. Performs statistical analysis to determine the type and amount of relief supplies based on specific needs.

[0595] Step 10:

[0596] Server: Based on the estimated number of victims, calculates the type and amount of relief supplies needed. Selects supplies according to specific needs, such as medicines for the elderly or nutritional foods for children.

[0597] Step 11:

[0598] Server: Searches the database for regions and companies that can cooperate, and identifies the most suitable partner based on the type and quantity of supplies and speed of supply.

[0599] Step 12:

[0600] Server: Notify the identified collaborators. This is done using an automatic email sending system or emergency contact tool.

[0601] Step 13:

[0602] Server: Receives responses from partners and reflects them in the supply plan. This updates the supply status of materials in real time.

[0603] Step 14:

[0604] Terminal: Provide an accessible communication platform for disaster victims and relief organizations to gather supply needs and feedback through web forms and mobile apps.

[0605] Step 15:

[0606] Users: Victims and relief organizations enter their required supplies and urgent requests through the platform, including information on the type, quantity, and urgency of the supplies.

[0607] Step 16:

[0608] Server: Analyzes collected feedback in real time and reflects it in supply plans. Based on the feedback, the type and quantity of supplies can be adjusted, enabling efficient delivery to disaster-stricken areas.

[0609] This concrete step will ensure the prompt and appropriate delivery of relief supplies to disaster-stricken areas.

[0610] Example 1

[0611] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0612] In recent years, natural disasters have become more frequent, creating a need for the rapid and effective delivery of relief supplies to affected areas. Conventional methods make it difficult to quickly grasp the disaster situation, secure optimal delivery routes to affected areas, and accurately deliver supplies that meet the needs of disaster victims. Furthermore, there is a lack of a system that reflects the supply status and feedback from disaster victims in real time. This results in delays and imbalances in the supply of supplies, leading to problems with insufficient relief for disaster victims. The purpose of this invention is to solve these problems.

[0613] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0614] In this invention, the server includes means for acquiring weather data and disaster impact data, means for analyzing the acquired weather data and disaster impact data and calculating an impact score, means for saving the analysis results in a database, means for acquiring remote image data, means for analyzing the acquired images and identifying road conditions in the disaster area, means for calculating an optimal delivery route and saving the results in a database, means for acquiring demographic data, means for generating estimated information on disaster victims based on the acquired data, means for determining the type and amount of relief supplies, means for identifying and notifying regions and companies that can cooperate, means for providing a platform for collecting feedback from disaster victims and support organizations, and means for analyzing the collected feedback and updating the supply plan, thereby enabling rapid and appropriate disaster response and relief supply.

[0615] "Weather data" refers to information about weather, temperature, precipitation, wind speed, and other meteorological information.

[0616] "Disaster impact data" refers to data that shows the impact of natural disasters, such as seismic intensity, damage area, and number of victims.

[0617] The "impact score" is a numerical indicator of the impact of a disaster, calculated based on factors such as seismic intensity, area of ​​damage, and number of victims.

[0618] "Remote image data" refers to image data acquired by remotely controlled devices such as satellites and drones.

[0619] "Demographic data" refers to statistical information about age groups, gender, population density, population composition, etc. in a particular area.

[0620] "Estimated information" refers to information such as the age group, gender, and health status of victims that is estimated based on the data obtained.

[0621] "Relief supplies" refer to items such as food, drinking water, medicine, and evacuation supplies that are provided to victims of natural disasters.

[0622] "Regions and companies that can cooperate" refers to regions and companies that can cooperate in supplying relief supplies.

[0623] "Feedback" refers to opinions and requests received from disaster victims and aid organizations regarding the needs and supply status of supplies.

[0624] A "supply plan" is a plan for appropriately supplying relief supplies to disaster-stricken areas, and is updated based on collected data and feedback.

[0625] The system for implementing this invention collects and analyzes various data in order to quickly and appropriately supply relief supplies to disaster-stricken areas in the event of a disaster, and optimizes the type and quantity of supplies and delivery routes based on the results.

[0626] Data acquisition and analysis

[0627] 1. Server: The server obtains weather data and disaster impact data from disaster information providers such as the Japan Meteorological Agency through API requests, including seismic intensity data and damage extent data.

[0628] Hardware and software used: curl command, MongoDB database

[0629] Example: Seismic intensity 7, damage area 50 square kilometers, number of affected people 20,000

[0630] 2. Server: The server analyzes the acquired data using a Python program and calculates an impact score based on factors such as the seismic intensity, the extent of the damage, and the number of victims.

[0631] Hardware and software used: Python, pandas library

[0632] Example: Seismic intensity x 1.5 + damage area x 0.3 + number of affected people x 0.2 = impact score 9.5

[0633] 3. Server: The server stores the analysis results in the MongoDB database again and uses them for further processing.

[0634] Image analysis and delivery route optimization

[0635] 1. Server: The server acquires remote image data obtained from satellites and drones and stores it in NAS (Network Attached Storage).

[0636] Hardware and software used: FTP client, NAS

[0637] Examples: Satellite images (wide area of ​​the affected area), drone images (detailed road conditions)

[0638] 2. Server: The server performs image analysis using the OpenCV library to identify road conditions and obstacles in the affected area.

[0639] Hardware and software used: OpenCV library, GeoJSON format

[0640] Example: Identifying road closures and detour routes

[0641] 3. Server: The server uses GIS (Geographic Information System) tools to calculate the optimal delivery route. For this, we use QGIS software.

[0642] Hardware and software used: QGIS Python API

[0643] Example: A main road is blocked, so a detour route is calculated to determine the shortest delivery route.

[0644] Population data analysis and supply decisions

[0645] 1. Server: The server retrieves population density and population pyramid data from government statistical sites and saves them in CSV format.

[0646] Hardware and software used: crawler, CSV format

[0647] Example: Population density 1000 people / km2, population pyramid (30% elderly, 20% children)

[0648] 2. Server: The server analyzes the acquired demographic data to estimate the age group, gender, and health status of the victims, and determines the type and amount of relief supplies needed.

[0649] Hardware and software used: Python, pandas library

[0650] Example: Identifying medicines for the elderly, food for children, and water supplies

[0651] Identifying and notifying cooperating regions and companies

[0652] 1. Server: The server identifies regions and companies that can cooperate from the database based on the type and quantity of relief supplies acquired and their necessity.

[0653] Hardware and software used: SQL queries, database

[0654] Example: Supply of medicines from neighboring cities, delivery support by logistics companies

[0655] 2. Server: The server notifies the identified partners via email or SMS and promptly begins supplying supplies.

[0656] Hardware and software used: SMTP server

[0657] Example: Automatically send emails to partner companies requesting the supply of supplies

[0658] Gather feedback and update supply plans

[0659] 1. Terminals: The terminals provide accessible web forms and mobile apps for disaster victims and relief organizations to input their supply needs and emergency requests.

[0660] Hardware and software used: React, Node.js

[0661] Example: Evacuees enter their required supplies via a web form or mobile app.

[0662] 2. Users: Victims and relief organizations input their actual needs and feedback.

[0663] Example: Necessary supplies (fresh water, food, blankets)

[0664] 3. Server: The server analyzes the collected feedback and reflects it in real time in the current supply plan, thereby improving the efficiency of supply.

[0665] Hardware and software used: Machine learning model

[0666] Example: Based on feedback, we increased the supply of fresh water and added more types of medical supplies.

[0667] With this mechanism, the system of the present invention can quickly collect and analyze a variety of data, enabling optimal provision and delivery of relief supplies.

[0668] Prompt Sentence Examples

[0669] "Get the current seismic intensity data and calculate the impact score."

[0670] "Analyze the latest satellite and drone images of the affected area and calculate the optimal delivery route."

[0671] "Get the latest population data from government statistical sites and estimate the age range of the affected people."

[0672] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0673] Step 1:

[0674] Server: The server sends API requests from disaster information providers such as the Japan Meteorological Agency to obtain weather data and disaster impact data (seismic intensity data and damage extent data). The API request is used as input, and the output is disaster data in JSON format. Specifically, the server executes the API request using the curl command and stores the obtained data in a MongoDB database.

[0675] Step 2:

[0676] Server: The server analyzes the acquired disaster data using a Python program and calculates the impact score. The input is disaster data in JSON format, and the output is a numerical impact score. For data processing, the Python pandas library is used to import the data into a data frame and calculate the impact score based on a formula. Specifically, the server calculates the impact score and stores it again in the MongoDB database.

[0677] Step 3:

[0678] Server: The server obtains remote image data from satellite data providers and drone operators. The input is image data in response to a request, and the output is an image file stored in a NAS (Network Attached Storage). Specifically, the server downloads the image data using an FTP client and stores it in the NAS.

[0679] Step 4:

[0680] Server: The server uses the OpenCV library to analyze the acquired remote image data and identify road conditions and obstacles in the disaster area. The input is image data, and the output is analysis data including road conditions and obstacle location information. For data processing, OpenCV is used to extract important features from the image and save them in GeoJSON format. Specifically, the server identifies road closures and detour routes in the image.

[0681] Step 5:

[0682] Server: The server uses GIS (geographic information system) tools to calculate the optimal delivery route. The input is road condition analysis data, and the output is coordinate information for the delivery route. For data processing, the QGIS Python API is used to calculate the optimal route that avoids closed roads, and the results are saved in a database. Specifically, the server creates the shortest delivery route and saves it in GeoJSON format.

[0683] Step 6:

[0684] Server: The server retrieves population density and population pyramid data from government statistical websites. The input is a request from the crawler, and the output is population data in CSV format. Specifically, the server runs the crawler periodically, downloads CSV data from government statistical websites, and uses it for analysis.

[0685] Step 7:

[0686] Server: The server analyzes the acquired population data and estimates the age group, gender, and health status of the victims. The input is population data in CSV format, and the output is estimated information about the victims. For data processing, the server aggregates the data using Python's pandas library, and determines the type and amount of relief supplies needed based on the results. Specific operations include creating lists of medicines for the elderly, food for children, etc.

[0687] Step 8:

[0688] Server: The server identifies regions and companies that can cooperate based on the type and amount of relief supplies from a database. The input is the required relief supply data, and the output is a list of cooperation partners. Data processing involves extracting the necessary information from the database using SQL queries. Specifically, the server obtains contact information for cooperation regions and companies.

[0689] Step 9:

[0690] Server: The server notifies the partners via email or SMS. The input is the list of partners and the notification content, and the output is the status of completion of the transmission. Specifically, the server automatically sends an email requesting the supply of supplies via the SMTP server.

[0691] Step 10:

[0692] Terminal: The terminal provides a web form and a mobile app that can be accessed by disaster victims and relief organizations, allowing them to enter their supply needs and emergency requests. The input is feedback information entered by the user, and the output is feedback data stored in a database. To implement the specific operation, the web form and mobile app are implemented using React and Node.js.

[0693] Step 11:

[0694] User: Victims and relief organizations enter their actual needs and feedback. The input is information about the supplies they need (e.g., fresh water, food, blankets), and the output is saved in the database as feedback. Specific actions involve the user entering information into a form and submitting it.

[0695] Step 12:

[0696] Server: The server analyzes the collected feedback and updates the supply plan in real time. The input is the feedback data, and the output is the updated supply plan. For data processing, a machine learning model is used to analyze the feedback data and dynamically adjust the supply plan. Specifically, the server updates the supply volume and type of materials based on the analysis results.

[0697] The above processing steps enable rapid collection and analysis of a variety of data, enabling the provision and delivery of appropriate relief supplies.

[0698] (Application example 1)

[0699] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0700] In the event of a disaster, it is extremely important to quickly and appropriately supply relief supplies to affected areas. However, in conventional systems, the collection and analysis of various data tended to be done manually, resulting in supply delays and shortages or surpluses of supplies. It was also difficult to grasp road and traffic conditions in affected areas in real time, making it difficult to select appropriate delivery routes. Furthermore, there was a lack of efficient means to collect real-time needs from disaster victims, which often led to supply plans deviating from actual needs. The present invention is provided to solve these problems.

[0701] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0702] In this invention, the server includes means for acquiring seismic intensity data and damage extent data, means for analyzing the acquired seismic intensity data and damage extent data and calculating an impact score, means for storing the analysis results in a database, means for acquiring satellite images and unmanned aerial vehicle images, means for analyzing the acquired images and identifying road conditions in the disaster area, means for calculating optimal delivery routes using a geographic information system and storing the results in a database, means for acquiring population density and demographic data, means for generating estimated information on disaster victims based on the acquired data, means for determining the type and amount of relief supplies, means for identifying and notifying regions and organizations that can cooperate, means for collecting feedback from disaster victims and support organizations, means for analyzing the collected feedback and updating a supply plan, means for optimizing delivery routes using a generative AI model, and means for collecting information on the need for supplies from disaster victims in real time using a smartphone application. This allows for rapid collection and analysis of a wide variety of data, enabling optimal provision and delivery of relief supplies.

[0703] "Seismic intensity data" is data that indicates the strength of shaking when an earthquake occurs.

[0704] "Damage extent data" is data that indicates the extent of the area affected by a disaster.

[0705] The "impact score" is a quantitative score that indicates the impact of a disaster, calculated based on factors such as seismic intensity, the extent of damage, and the number of victims.

[0706] "Satellite imagery" refers to images of the Earth's surface taken by an artificial satellite located in space.

[0707] "Unmanned aerial vehicle images" are images taken from the sky by unmanned aerial vehicles such as drones.

[0708] A "geographic information system" is a system for collecting, displaying, and analyzing geographic information.

[0709] "Population density data" is data that indicates the number of people per unit area within a specific region.

[0710] "Demographic data" refers to data that shows the distribution of age groups, gender, and other demographic attributes in a certain region or group.

[0711] "Estimated information" refers to information such as the age group, gender, and health status of victims estimated based on the data obtained.

[0712] "Relief supplies" are essential items such as food, water, and medical supplies that are supplied to disaster-stricken areas during disasters.

[0713] A "generative AI model" is a model that uses artificial intelligence to perform data analysis and inference.

[0714] A "smartphone application" is a software program that runs on a smartphone and is used to collect data from users.

[0715] "Feedback" is information collected from disaster victims and aid organizations regarding an assessment of current needs and supplies.

[0716] MODE FOR CARRYING OUT THE INVENTION

[0717] This section describes a system for implementing the present invention. This system collects and analyzes a wide variety of data to quickly and appropriately supply relief supplies to disaster-stricken areas in the event of a disaster, and uses that data to optimize the type and quantity of supplies and delivery routes.

[0718] 1. Data acquisition and analysis

[0719] The server obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency, analyzes this data, and calculates an impact score. This impact score is calculated based on indicators such as seismic intensity, damage extent, and number of victims, and the analysis results are stored in a database. For example, if an earthquake with a seismic intensity of 7 occurs, the damage extent is 50 square kilometers, and the number of victims is 20,000, the impact score is calculated as follows:

[0720] Examples:

[0721] Seismic intensity data: 7

[0722] Area of ​​damage: 50 square kilometers

[0723] Number of victims: 20,000

[0724] Impact score: Seismic intensity x 1.5 + damage area x 0.3 + number of victims x 0.2 = impact score 9.5

[0725] 2. Image analysis and delivery route optimization

[0726] The server acquires satellite and drone imagery and performs image analysis to identify road conditions and obstacles in the affected area, then uses a geographic information system (GIS) to calculate optimal delivery routes and identify routes that avoid blocked roads.

[0727] Examples:

[0728] Satellite image: Wide range of the affected area

[0729] Unmanned aerial vehicle imagery: detailed road conditions

[0730] Delivery route optimization: When a main road is blocked, a detour route is calculated to determine the shortest delivery route.

[0731] 3. Population data analysis and supply decisions

[0732] The server obtains population density and demographic data from government and statistical agencies, and uses this information to estimate the age, gender, and health status of disaster victims. This information is then used to determine the type and quantity of relief supplies needed.

[0733] Examples:

[0734] Population density: 1,000 people per square kilometer

[0735] Demographics: 30% elderly, 20% children

[0736] Relief supply determination: Identify supplies of medicine for the elderly, food for children, and water.

[0737] 4. Identifying and notifying cooperating regions and organizations

[0738] The server identifies regions and organizations that can cooperate based on the type and quantity of relief supplies received and the need for them, and notifies them. At this time, notifications are sent to quickly begin supplying supplies.

[0739] Examples:

[0740] Medicine supplies from nearby cities

[0741] Delivery support by logistics companies

[0742] 5. Gather feedback and update supply plans

[0743] The user provides a smartphone application that disaster victims and relief organizations can access, allowing them to input their needs and urgent requests for supplies. The feedback is analyzed by the server and reflected in the current supply plan.

[0744] Examples:

[0745] Using a web form or smartphone app, disaster victims can input the supplies they need.

[0746] Feedback: Request for fresh water, food, and blankets

[0747] Updated supply plan based on feedback: Increased fresh water supply and added more medical supplies

[0748] 6. Use of generative AI models

[0749] The server uses a generative AI model to optimize delivery routes and generate estimated information. By inputting prompt statements to the AI ​​model, efficient analysis becomes possible.

[0750] Examples:

[0751] Disaster data: intensity: 7, affected_area: 50, affected_population: 20000

[0752] What is the best delivery route?

[0753] Also, what types and amounts of food do you need?

[0754] In this way, the system of the present invention can quickly collect and analyze a wide variety of data to provide and deliver optimal relief supplies, thereby significantly improving support for disaster victims.

[0755] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0756] Step 1:

[0757] The server receives real-time seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. Based on the input seismic intensity data and damage extent data, the server records them in a database. During this process, the latest information is collected using APIs and data feeds.

[0758] Step 2:

[0759] The server analyzes the acquired seismic intensity data and damage extent data and calculates an impact score. This weights each data point and derives an impact score. Specifically, the impact score is calculated by multiplying the seismic intensity by 1.5, the damage extent by 0.3, and the number of victims by 0.2.

[0760] Input: Seismic intensity data, damage area data, number of victims

[0761] Output: Impact score

[0762] Step 3:

[0763] The server stores the calculated impact score and the analysis results in a database. The impact score is important data to be used in the future analysis process.

[0764] Step 4:

[0765] The server acquires various satellite and unmanned aerial vehicle images, analyzes the image data, and uses machine learning algorithms to identify road conditions and obstacle locations throughout the disaster area.

[0766] Input: Satellite images, unmanned aerial vehicle images

[0767] Output: Road conditions in the affected area, location of obstacles

[0768] Step 5:

[0769] The server uses a geographic information system (GIS) to calculate the optimal delivery route, identifying routes that avoid road blocks and obstacles, and stores the optimized delivery route in a database.

[0770] Input: Road conditions, obstacle locations

[0771] Output: Optimized delivery route

[0772] Step 6:

[0773] The server obtains population density and demographic data from government and statistical agencies, analyzes it, estimates the age, gender, and health status of disaster victims, and uses this information to determine the type and amount of relief supplies needed.

[0774] Input: population density data, population composition data

[0775] Output: Type and quantity of relief supplies

[0776] Step 7:

[0777] The server identifies and notifies regions and organizations that can cooperate based on the type and amount of relief supplies, implementing protocols to quickly contact nearby cities and logistics companies.

[0778] Input: Type and quantity of relief supplies

[0779] Output: Notification to cooperating regions and organizations

[0780] Step 8:

[0781] The device uses a smartphone application to collect feedback from disaster victims and relief organizations, through which disaster victims input their needs and emergency requests.

[0782] Input: Feedback from victims and relief organizations

[0783] Output: Collected feedback data

[0784] Step 9:

[0785] The server analyzes the collected feedback and updates the supply plan, reevaluating the types and quantities of supplies to be supplied and adjusting them as necessary.

[0786] Input: Feedback data

[0787] Output: Updated supply plan

[0788] Step 10:

[0789] The server uses the generative AI model to optimize delivery routes and generate estimated information. It inputs prompts to the AI ​​model to achieve efficient analysis.

[0790] Input: prompt statement, real-time data

[0791] Output: Optimized delivery route, estimated information

[0792] As a concrete example, the following prompt sentence is input to the AI ​​model:

[0793] Disaster data: intensity: 7, affected_area: 50, affected_population: 20000

[0794] What is the best delivery route?

[0795] Also, what types and amounts of food do you need?

[0796] This will enable us to respond effectively and quickly to any disasters that occur.

[0797] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0798] This article describes a system for implementing the present invention. This system collects and analyzes a variety of data, and combines it with an emotion engine that recognizes the user's emotions to optimize the type and quantity of supplies and delivery routes in order to quickly and appropriately provide relief supplies to disaster-stricken areas in the event of a disaster.

[0799] Program processing and specific examples

[0800] Data acquisition and analysis

[0801] 1. The server obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. This data includes the time of the earthquake, epicenter, damage extent, number of victims, etc. For example, it accesses a specified API using an HTTP request to obtain the required data.

[0802] Examples:

[0803] Seismic intensity data: 7

[0804] Area of ​​damage: 50 square kilometers

[0805] Number of victims: 20,000

[0806] 2. The server integrates the acquired seismic intensity data and damage extent data to calculate an impact score. The impact score is calculated by combining the seismic intensity, damage extent, number of victims, etc. For example, the formula used is seismic intensity x 1.5 + damage extent x 0.3 + number of victims x 0.2.

[0807] Examples:

[0808] Seismic intensity x 1.5 + damage area x 0.3 + number of affected people x 0.2 = impact score 9.5

[0809] 3. The server stores the calculated impact score in a database and uses it for subsequent analysis and processing.

[0810] Image analysis and delivery route optimization

[0811] 1. The server acquires satellite and drone images and performs image analysis to identify road conditions and the location of obstacles in the affected area. It uses an image recognition algorithm based on deep learning to detect damaged roads and obstacles.

[0812] Examples:

[0813] Satellite image: Wide range of the affected area

[0814] Drone imagery: detailed road conditions

[0815] 2. The server uses a GIS (geographic information system) to calculate the optimal delivery route, using a shortest path algorithm to identify the most efficient route to evacuation shelters or collection points.

[0816] Examples:

[0817] The main road is closed, so a detour route is calculated to determine the shortest delivery route.

[0818] Population data analysis and supply decisions

[0819] 1. The server retrieves population density and population pyramid data from government or statistical agencies, including the number of people in a particular area and their age distribution. It retrieves the data using an API request.

[0820] Examples:

[0821] Population density: 1,000 people per square kilometer

[0822] Population pyramid: 30% elderly, 20% children

[0823] 2. The server analyzes the population data and estimates the age, gender, and health status of the victims. Based on this information, it determines the type and amount of relief supplies needed.

[0824] Examples:

[0825] Identify supplies of medicine for the elderly, food for children, and water

[0826] Use of emotion engine

[0827] 1. The device provides an accessible communication platform for disaster victims and relief organizations to input their needs and feedback. The platform is equipped with an emotion engine that recognizes the user's emotional state as they input their needs.

[0828] Examples:

[0829] Victims enter the supplies they need through a web form or mobile app, and the emotion engine analyzes the emotions from the text input.

[0830] 2. Users, as disaster victims or relief organizations, input their needed supplies and urgent requests through the platform, and the emotion engine recognizes emotions in real time and collects the data.

[0831] Examples:

[0832] Supplies needed: Fresh water, food, blankets. Emotional state: "Anxious" "Urgent".

[0833] 3. The server analyzes the collected feedback and emotion data in real time and reflects it in the current supply plan. By analyzing the feedback and emotion data together, the urgency and priority of supplies can be determined with even greater accuracy.

[0834] Examples:

[0835] Based on feedback, we've increased the supply of fresh water, added new types of medical supplies, and prioritized the delivery of some supplies based on emotion data.

[0836] 4. The server updates supply plans in real time and addresses the psychological needs of disaster victims, ensuring more accurate distribution of supplies.

[0837] In this way, by using the system according to the present invention, it is possible to quickly collect and analyze various data and the emotional state of the user, and to provide and deliver optimal relief supplies.

[0838] The processing flow will be explained below.

[0839] Step 1:

[0840] Server: Obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. This includes information such as the time of the earthquake, epicenter, damage extent, and number of victims. For example, an HTTP request is used to access a specified API and obtain the required data.

[0841] Step 2:

[0842] Server: The acquired seismic intensity data and damage extent data are integrated to calculate the impact score. The impact score is calculated by combining the seismic intensity, damage extent, number of victims, etc. For example, the formula used is seismic intensity x 1.5 + damage extent x 0.3 + number of victims x 0.2.

[0843] Step 3:

[0844] Server: The calculated impact scores are stored in a database for later analysis and processing.

[0845] Step 4:

[0846] Server: Acquires satellite and drone imagery. This includes retrieving data from imagery providers and drone operators. For example, downloading image data from an AWS S3 bucket.

[0847] Step 5:

[0848] Server: Analyzes acquired satellite and drone images to identify road conditions and transportation options in the affected areas. Utilizes deep learning-based image recognition algorithms to detect damaged roads and obstacles.

[0849] Step 6:

[0850] Server: Based on the analysis results, the GIS system is used to calculate the optimal delivery route. A shortest path algorithm is used to identify the most efficient route to evacuation shelters and collection points.

[0851] Step 7:

[0852] Server: Stores the generated delivery route information in a database, allowing logistics planning to proceed efficiently.

[0853] Step 8:

[0854] Server: Obtain population density and population pyramid data from governments and statistical agencies. This includes the number of people in a particular area and their age distribution. Obtain the data using API requests.

[0855] Step 9:

[0856] Server: Analyzes acquired population data to estimate the age, gender, and health status of disaster victims. Performs statistical analysis to determine the type and amount of relief supplies based on specific needs.

[0857] Step 10:

[0858] Server: Based on the estimated number of victims, calculates the type and amount of relief supplies needed. Selects supplies according to specific needs, such as medicines for the elderly or nutritional foods for children.

[0859] Step 11:

[0860] Server: Searches the database for regions and companies that can cooperate, and identifies the most suitable partner based on the type and quantity of supplies and speed of supply.

[0861] Step 12:

[0862] Server: Notify the identified collaborators. This is done using an automatic email sending system or emergency contact tool.

[0863] Step 13:

[0864] Server: Receives responses from partners and reflects them in the supply plan. This updates the supply status of materials in real time.

[0865] Step 14:

[0866] Terminal: Provides an accessible communication platform for disaster victims and relief organizations to input their needs and feedback. The platform is equipped with an emotion engine that recognizes the user's emotional state as they input.

[0867] Step 15:

[0868] Users: Victims and relief organizations input their needs and urgent requests through the platform. The emotion engine recognizes emotions in real time and collects the data.

[0869] Step 16:

[0870] Server: Analyzes collected feedback and sentiment data in real time and reflects it in the current supply plan. By analyzing feedback and sentiment data together, the urgency and priority of supplies can be determined with even greater accuracy.

[0871] Step 17:

[0872] Server: Updates supply plans in real time and addresses the psychological needs of disaster victims, ensuring more accurate distribution of supplies.

[0873] Example 2

[0874] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0875] Conventional disaster relief systems have struggled to collect and analyze diverse data quickly and accurately, and efficiently provide and deliver relief supplies. Furthermore, they lack the ability to incorporate feedback from disaster victims and relief organizations, making it difficult to formulate supply plans that respond to actual needs. Furthermore, supply plans do not take into account the psychological state of disaster victims, making it difficult to improve disaster satisfaction.

[0876] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0877] In this invention, the server includes means for acquiring seismic intensity data and damage extent data, means for analyzing the acquired seismic intensity data and damage extent data to calculate an impact score, means for saving the analysis results in a database, means for acquiring satellite images and aerial images, means for analyzing the acquired images to identify road conditions and the locations of obstacles in the disaster area, means for calculating an optimal delivery route using an optimal route algorithm and saving the results in a database, means for acquiring population density and population composition data, means for generating estimated information on disaster victims based on the acquired data, means for determining the type and quantity of relief supplies, means for identifying and notifying regions and organizations that can cooperate, means for collecting feedback from disaster victims and relief organizations, means for analyzing the collected feedback and updating the supply plan, means for recognizing emotional states from user input and collecting that data, and means for analyzing the emotional data to determine the urgency and priority of supplies and reflecting the results in the supply plan.

[0878] This will enable the rapid collection of diverse data, highly accurate analysis, and optimization of relief supply taking into account user emotions.

[0879] "Seismic intensity data" is a numerical value that indicates the strength of earthquake shaking and quantitatively represents the impact of an earthquake.

[0880] "Damage extent data" means data that indicates the extent of damage in a disaster-affected region and identifies the geographic area affected.

[0881] The "impact score" is a numerical value calculated by combining multiple data elements, such as seismic intensity data, damage extent data, and number of victims, and is used to comprehensively evaluate the impact of a disaster.

[0882] "Satellite imagery" means an image of a geographic area taken from a satellite that has a wide field of view and shows the ground in detail.

[0883] "Airborne imagery" means images of a geographic area taken from a drone or other aircraft that provide a detailed view from a low altitude.

[0884] An "optimal route algorithm" is an algorithm that calculates the shortest distance to a destination or the most efficient route, and is used to optimize delivery routes.

[0885] "Population density data" is data that indicates the density of people living in a particular area and is used to understand the population distribution of the area.

[0886] "Demographic data" refers to data that shows the population distribution of age, gender, etc. in a specific area, and is used to understand the characteristics of disaster victims.

[0887] "Estimated information on victims" is information used to estimate the age group, gender, health status, etc. of victims based on acquired population data, etc., and to assess the need for relief.

[0888] "Relief supplies" are items needed when a disaster occurs, such as food, medicine, clothing, and water, to support the lives of disaster victims.

[0889] "Regions and organizations that can cooperate" refers to regions and cooperative organizations that can support relief efforts in the event of a disaster, including the provision of relief supplies and human support.

[0890] "Feedback" refers to information collected from disaster victims and relief organizations, and indicates the needs for supplies, the situation on the ground, and the level of urgency.

[0891] "Emotional state" indicates the emotional state the user is feeling, and recognizes emotions such as anxiety, urgency, and relief in real time.

[0892] The system of the present invention collects and analyzes a variety of data, and further combines it with an emotion engine that recognizes the user's emotions to optimize the type and quantity of supplies and delivery routes in order to quickly and appropriately supply relief supplies to disaster-stricken areas in the event of a disaster. Specific embodiments of the system are described below.

[0893] Hardware and Software Configuration

[0894] This system uses the following hardware and software:

[0895] Server: A computer system with high-performance computing power that collects, analyzes, stores, and calculates routes.

[0896] Terminal: A device that provides a web browser and mobile apps for use by disaster victims and relief organizations.

[0897] Emotion engine: A software module for recognizing the emotional state from user input, such as a sentiment analysis tool that uses natural language processing (NLP) techniques.

[0898] GIS (Geographic Information System): A tool that handles geographic data and calculates optimal delivery routes. Examples include QGIS and ArcGIS.

[0899] Image recognition algorithm: A deep learning algorithm used to analyze satellite and aerial images. TensorFlow and PyTorch are used.

[0900] Data collection and analysis

[0901] 1. The server obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. This data includes the time of the earthquake, epicenter, damage extent, and number of victims. As a specific example, it accesses the API using an HTTP request and obtains a response in JSON format.

[0902] For example, send a request like GET / earthquake / data.

[0903] 2. The server integrates the acquired seismic intensity data and damage extent data to calculate the impact score. The impact score is calculated using the formula: seismic intensity x 1.5 + damage extent x 0.3 + number of victims x 0.2.

[0904] Example: If the earthquake has a magnitude of 7, the damage area is 50 square kilometers, and the number of affected people is 20,000, the impact score is 7 x 1.5 + 50 x 0.3 + 20,000 x 0.2 = 9.5.

[0905] Image analysis and delivery route optimization

[0906] 1. The server acquires satellite and aerial imagery and analyzes them using an image recognition algorithm based on deep learning, thereby identifying road damage and the location of obstacles.

[0907] Example: Access the GET / satellite_images and GET / drone_images endpoints to retrieve and analyze image data.

[0908] 2. The server uses GIS to calculate the optimal delivery route, using a shortest path algorithm (e.g., Dijkstra algorithm) to identify the most efficient route to a shelter or collection point.

[0909] Example: If a major road is blocked, calculate a detour route to create the shortest route.

[0910] Population data analysis and material decisions

[0911] 1. The server obtains population density and demographic data from government and statistical agencies, including the number of people in a particular area and their age distribution.

[0912] Example: Access the GET / population_data endpoint to retrieve data.

[0913] 2. The server analyzes the acquired population data to estimate the age, gender, and health status of the victims. Based on this information, it determines the type and amount of relief supplies needed.

[0914] Examples: Identifying supplies of medicine for the elderly, food for children, and water.

[0915] Use of emotion engine

[0916] 1. The device provides a communication platform for disaster victims and relief organizations, allowing them to input their needs and feedback. It also has a built-in emotion engine that recognizes the user's emotional state from their input.

[0917] Example: When a disaster victim enters "I need fresh water. I'm anxious" through a web form or mobile app, the emotion engine recognizes the emotion "anxiety."

[0918] 2. The server analyzes the collected feedback and emotion data in real time and reflects it in the supply plan, thereby determining the urgency and priority of supplies with even greater accuracy.

[0919] Example: Increase the supply of fresh water based on feedback, and prioritize the delivery of certain supplies based on sentiment data.

[0920] Prompt Sentence Examples

[0921] By inputting the following prompt sentence into the generative AI model, it is possible to generate a list of supplies appropriate for the needs of the disaster-stricken area.

[0922] Please create a list of supplies needed for elderly people in the disaster-stricken areas. There has been a magnitude 7 earthquake, affecting an area of ​​50 square kilometers and affecting 20,000 people. Many of the victims are feeling anxious and in a state of emergency.

[0923] This invention enables rapid collection and highly accurate analysis of various data during disasters, as well as optimization of relief supplies that take into account the emotions of users, thereby enabling rapid and accurate support for disaster victims.

[0924] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0925] Step 1: Obtain disaster information

[0926] 1. The server obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. Specifically, it sends an HTTP request to access a specified API and obtains data such as the time of the earthquake, epicenter, damage extent, and number of victims. The input is a request to the API endpoint, and the output is the obtained seismic intensity data and damage extent data in JSON format.

[0927] What it does: The server sends a request to an API endpoint such as GET / earthquake / data, parses the data received in response, and stores it in an internal database.

[0928] Step 2: Calculating the impact score

[0929] 2. The server integrates the acquired seismic intensity data and damage extent data to calculate an impact score. The impact score is calculated using the formula: seismic intensity x 1.5 + damage extent x 0.3 + number of victims x 0.2. The input is the acquired seismic intensity data and damage extent data, and the output is the impact score.

[0930] What it does: The server reads each data point as a number and calculates an impact score based on a formula.

[0931] Step 3: Save the impact score

[0932] 3. The server stores the calculated impact score in a database and uses it for subsequent analysis and processing. The input is the calculated impact score, and the output is the result stored in the database.

[0933] Specific behavior: The server executes an SQL query such as INSERT INTO impact_scores (timestamp, score) VALUES (current_timestamp, impact_score).

[0934] Step 4: Image acquisition and analysis

[0935] 4. The server acquires satellite images and drone images and analyzes them using an image recognition algorithm based on deep learning. The input is the satellite images and drone images, and the output is the analysis results (data showing road conditions and the location of obstacles).

[0936] Specific operation: The server accesses the GET / satellite_images and GET / drone_images endpoints to obtain image data, analyzes it using TensorFlow, PyTorch, etc., and detects damaged roads and obstacles.

[0937] Step 5: Optimize delivery routes

[0938] 5. The server uses GIS to calculate the optimal delivery route. Here, a shortest path algorithm (e.g., Dijkstra algorithm) is used. The input is the analyzed road condition data, and the output is the optimal delivery route.

[0939] Specific operation: The server analyzes road network data using tools such as QGIS and ArcGIS, calculates the shortest route, and stores it in a database.

[0940] Step 6: Collect and analyze population data

[0941] 6. The server obtains population density and demographic data from government and statistical bureaus, and analyzes it to estimate the age, gender, and health status of disaster victims. Based on this information, it determines the type and amount of relief supplies. The input is the obtained population data, and the output is a list of needed relief supplies.

[0942] Specific operation: The server accesses the GET / population_data endpoint to collect data, analyzes the data using analysis tools such as Pandas, and generates a supply list based on the estimated results.

[0943] Step 7: Collect feedback and sentiment data

[0944] 7. The device provides an accessible communication platform for disaster victims and relief organizations, allowing them to input their needs and feedback. The emotion engine recognizes their emotional state from these inputs. The input is user feedback and emotion data, and the output is analyzed data.

[0945] Specific operation: The device provides a web form or mobile app, collects user input data, and sends it to the server. The emotion engine analyzes the input text and generates emotion data. When a user inputs "I need fresh water. I'm anxious," the emotion engine analyzes the text and recognizes the emotion "anxiety."

[0946] Step 8: Update the supply plan

[0947] 8. The server analyzes the collected feedback and emotion data in real time and reflects it in the supply plan. It determines the urgency and priority of supplies with high accuracy. The input is feedback and emotion data, and the output is an updated supply plan.

[0948] Specific operation: The server integrates feedback data and emotion data, and applies a priority algorithm to optimize supply planning. Based on feedback, it increases the supply of fresh water, and prioritizes the delivery of some supplies based on emotion data.

[0949] Through the above steps, the system of the present invention quickly collects and analyzes various data and the user's emotional state, and realizes optimal provision and delivery of relief supplies.

[0950] (Application example 2)

[0951] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0952] In the event of a disaster, it is important to provide appropriate relief supplies to affected areas quickly. However, it is difficult to grasp the state of the affected area and the needs of the victims in real time, making it difficult to optimize the type and quantity of supplies and delivery routes. It is also necessary to update supply plans that take into account the psychological state and urgency of the victims.

[0953] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0954] In this invention, the server includes means for acquiring seismic intensity data and damage extent data, means for analyzing the acquired seismic intensity data and damage extent data and calculating an impact score, means for saving the analysis results in a database, means for acquiring satellite images and drone images, means for analyzing the acquired images and identifying road conditions in the disaster area, means for calculating an optimal delivery route and saving the results in a database, means for acquiring population density and population pyramid data, means for generating estimated information on disaster victims based on the acquired data, means for determining the type and amount of relief supplies, means for identifying and notifying regions and companies that can cooperate, means for collecting feedback from disaster victims and relief organizations, means for analyzing the collected feedback and updating the supply plan, means for recognizing the emotional state of disaster victims using an emotion engine, and means for predicting the supplies needed by disaster victims using a generative AI model. This allows the situation in the disaster area and the needs of disaster victims to be grasped in real time, enabling the provision and delivery of optimal relief supplies.

[0955] "Seismic intensity data" is numerical information that indicates the strength of earthquake shaking.

[0956] "Damage extent data" is information indicating the size and extent of the area affected by a disaster.

[0957] The "impact score" is a number that indicates the degree of impact of the damage, calculated based on data such as seismic intensity, area of ​​damage, and number of victims.

[0958] A "satellite image" is an image of the Earth's surface taken by an artificial satellite.

[0959] "Drone imagery" is a detailed image of the Earth's surface taken by an unmanned aerial vehicle (drone).

[0960] "Road conditions" is information indicating the state of road damage and whether the road is passable.

[0961] A "delivery route" is the route along which goods are delivered to their destination.

[0962] "Population density" is a statistical information that indicates the number of people per certain area.

[0963] "Population pyramid data" is statistical information that shows the distribution of age groups and gender in a particular area.

[0964] "Estimated information on victims" refers to information such as the age group, gender, and health status of victims estimated based on population data.

[0965] "Relief supplies" are goods provided to disaster victims in the event of a disaster, including food, water, medicine, etc.

[0966] "Regions and companies that can cooperate" refers to regions and companies that can provide support in the event of a disaster.

[0967] "Feedback" refers to information such as requests, opinions, and reports from disaster victims and relief organizations.

[0968] An "emotion engine" is an algorithm or software that recognizes a user's emotional state.

[0969] A "generative AI model" is a model that uses artificial intelligence to predict needed supplies and information from input data.

[0970] The system for carrying out the present invention will now be described in detail. This system is a smartphone application for quickly and appropriately supplying relief supplies to disaster-stricken areas, particularly in the event of a disaster.

[0971] First, the server obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. The server uses an HTTP request to access a specified API and obtains data such as the time of the earthquake, epicenter, damage extent, and number of victims. Examples include "seismic intensity data: seismic intensity 7," "damage extent: 50 square kilometers," and "number of victims: 20,000." Based on the obtained data, the server calculates an impact score and stores the results in a database.

[0972] Next, the server acquires satellite and drone images and uses deep learning models (e.g., TensorFlow, PyTorch) to identify road conditions in the affected areas. The server analyzes these images to detect damaged roads and obstacles, calculates optimal delivery routes, and stores them in a database. For example, if a main road is blocked, the server can calculate a detour route to determine the shortest delivery route.

[0973] The server also obtains population density and population pyramid data from governments and statistical bureaus, and uses this data to estimate the age, gender, and health status of disaster victims. The server uses this information to determine the type and amount of relief supplies needed. Specific examples include identifying "medicine for the elderly," "food for children," and "water supplies."

[0974] The device also provides a communication platform accessible to disaster victims and relief organizations. This platform is equipped with an emotion engine that analyzes the emotional state of disaster victims from text input. For example, disaster victims can input their required supplies via a web form or mobile app, and data such as "emotional state: anxiety, emergency" can be collected.

[0975] The server analyzes the collected feedback and emotion data in real time and reflects it in the current supply plan. For example, it can increase the supply of fresh water and add more types of medical supplies based on the collected feedback. It can also prioritize the delivery of certain supplies based on emotion data.

[0976] Finally, the generative AI model is used to predict what supplies the disaster victims need. This is done using prompts, such as "What supplies do the disaster victims need?", which allows the generative AI model to return predictions such as "Clean water, non-perishable food, blankets, and medicines."

[0977] In this way, the system according to the present invention can realize efficient and appropriate provision and delivery of relief supplies in the event of a disaster.

[0978] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0979] Step 1:

[0980] The server obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. To do this, the server uses an HTTP request to access a specified API and obtains data such as the time of the earthquake, epicenter, damage extent, and number of victims. The input is the data from the API, and the output is to store this data in an internal structure.

[0981] Step 2:

[0982] The server analyzes the seismic intensity data and damage extent data acquired in step 1 and calculates the impact score. Specifically, the impact score is calculated using the formula: seismic intensity x 1.5, damage extent x 0.3, number of victims x 0.2. The input data are seismic intensity, damage extent, and number of victims, and the output is the calculated impact score.

[0983] Step 3:

[0984] The server stores the calculated impact score in a database. Using the analysis results, important information such as the impact score is stored in the database for later analysis and processing. The input is the impact score, and the output is the result stored in the database.

[0985] Step 4:

[0986] The server acquires satellite and drone images. To do this, the server accesses the corresponding data acquisition APIs and data streams to acquire image data. The input is image data from satellites and drones, and the output is the acquired image data.

[0987] Step 5:

[0988] The server analyzes the acquired images and identifies the road conditions in the disaster area. Deep learning models (e.g., TensorFlow, PyTorch) are used to detect damaged roads and obstacles in the images. The input is satellite and drone images, and the output is road condition data as the analysis result.

[0989] Step 6:

[0990] The server calculates the optimal delivery route based on the analysis results and stores the results in a database. It uses a GIS (geographic information system) to run a shortest path algorithm to identify the most efficient route to evacuation shelters and collection points. The input is road condition data, and the output is the optimal delivery route.

[0991] Step 7:

[0992] The server retrieves population density and population pyramid data from governments and statistical offices. To do this, the server uses an API request to retrieve the specified data. The input is the data from governments and statistical offices, and the output is the retrieved population data.

[0993] Step 8:

[0994] The server analyzes the acquired population data and estimates the age group, gender, and health status of the victims. The data is analyzed using Python's Pandas library, etc. The input is population density and population pyramid data, and the output is estimated victim information.

[0995] Step 9:

[0996] The server determines the type and amount of relief supplies needed based on the estimated information. It identifies the supplies needed based on the age group and health condition of the victims. Specific examples include "medicine for the elderly" and "food for children." The input is victim information, and the output is the type and amount of relief supplies.

[0997] Step 10:

[0998] The device provides an accessible communication platform for disaster victims and relief organizations to input their needs and feedback. This platform incorporates an emotion engine (e.g., Microsoft Azure's Text Analytics API) to analyze the user's emotional state from their text input. The input is text input from the user, and the output is analyzed emotional data.

[0999] Step 11:

[1000] The server analyzes the collected feedback and emotion data in real time and reflects it in the supply plan. By analyzing the feedback and emotion data, the urgency and priority of supplies can be determined with greater precision. Specific examples include "increase the supply of fresh water" and "add new types of medical supplies." The input is feedback and emotion data, and the output is an updated supply plan.

[1001] Step 12:

[1002] The server uses a generative AI model to predict the supplies needed by disaster victims. A prompt is input into the generative AI model to predict the supplies needed. For example, by inputting a prompt such as "Please tell us what supplies the disaster victims need," a prediction such as "Clean water, non-perishable food, blankets, and medicines are needed" can be obtained. The input is the prompt, and the output is the prediction result from the generative AI model.

[1003] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1004] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1005] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1006] [Third embodiment]

[1007] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1008] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1009] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1010] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1011] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1012] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1013] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1014] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1015] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1016] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1017] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1018] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1019] This section describes a system for implementing the present invention. This system collects and analyzes a wide variety of data in order to quickly and appropriately supply relief supplies to disaster-stricken areas in the event of a disaster, and then optimizes the type and quantity of supplies and delivery routes based on the results.

[1020] Program processing and specific examples

[1021] Data acquisition and analysis

[1022] 1. Server: Obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. This data includes the time of the earthquake, epicenter, damage extent, number of victims, etc.

[1023] Examples:

[1024] Seismic intensity data: 7

[1025] Area of ​​damage: 50 square kilometers

[1026] Number of victims: 20,000

[1027] 2. Server: Analyzes the acquired data and calculates the impact score. The impact score is calculated based on the seismic intensity, the extent of the damage, the number of victims, etc.

[1028] Examples:

[1029] Seismic intensity x 1.5 + damage area x 0.3 + number of affected people x 0.2 = impact score 9.5

[1030] 3. Server: The analysis results are stored in a database and used for subsequent processing.

[1031] Image analysis and delivery route optimization

[1032] 1. Server: Acquires satellite and drone images and performs image analysis to identify road conditions and the location of obstacles in the affected areas.

[1033] Examples:

[1034] Satellite image: Wide range of the affected area

[1035] Drone imagery: detailed road conditions

[1036] 2. Server: Uses GIS (geographic information systems) to calculate optimal delivery routes, avoiding blocked roads and identifying routes that will deliver goods quickly.

[1037] Examples:

[1038] The main road is closed, so a detour route is calculated to determine the shortest delivery route.

[1039] Population data analysis and supply decisions

[1040] 1. Server: Obtains population density and population pyramid data from government and statistical bureaus.

[1041] Examples:

[1042] Population density: 1,000 people per square kilometer

[1043] Population pyramid: 30% elderly, 20% children

[1044] 2. Server: Analyzes the acquired data and estimates the age, gender, and health status of the victims. Based on this information, the type and amount of relief supplies needed are determined.

[1045] Examples:

[1046] Identify supplies of medicine for the elderly, food for children, and water

[1047] Identifying and notifying cooperating regions and companies

[1048] 1. Server: Identifies neighboring areas and cooperating companies from a database based on the type and quantity of relief supplies obtained and their necessity.

[1049] Examples:

[1050] Supply of medicines from neighboring cities and delivery support from logistics companies

[1051] 2. Server: Notify the identified partners and promptly begin supplying supplies.

[1052] Gather feedback and update supply plans

[1053] 1. Terminal: Providing a platform that disaster victims and relief organizations can access and input their needs and emergency requests.

[1054] Examples:

[1055] Victims can enter their supply needs using a web form or mobile app.

[1056] 2. Users: Victims and relief organizations input their actual needs and feedback.

[1057] Examples:

[1058] Necessary supplies: fresh water, food, blankets

[1059] 3. Server: Analyzes the collected feedback and reflects it in the current supply plan, thereby ensuring more accurate supply of supplies to disaster-stricken areas.

[1060] Examples:

[1061] Based on feedback, we have increased the supply of fresh water and added more types of medical supplies.

[1062] In this way, by using the system according to the present invention, it is possible to quickly collect and analyze a variety of data and realize optimal provision and delivery of relief supplies.

[1063] The processing flow will be explained below.

[1064] Step 1:

[1065] Server: Obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. This includes information such as the time of the earthquake, epicenter, damage extent, and number of victims. For example, an HTTP request is used to access a specified API and obtain the required data.

[1066] Step 2:

[1067] Server: The acquired seismic intensity data and damage extent data are integrated to calculate the impact score. The impact score is calculated by combining the seismic intensity, damage extent, number of victims, etc. For example, the formula used is seismic intensity x 1.5 + damage extent x 0.3 + number of victims x 0.2.

[1068] Step 3:

[1069] Server: The calculated impact scores are stored in a database for later analysis and processing.

[1070] Step 4:

[1071] Server: Acquires satellite and drone imagery. This includes retrieving data from imagery providers and drone operators. For example, downloading image data from an AWS S3 bucket.

[1072] Step 5:

[1073] Server: Analyzes acquired satellite and drone images to identify road conditions and transportation options in the affected areas. Utilizes deep learning-based image recognition algorithms to detect damaged roads and obstacles.

[1074] Step 6:

[1075] Server: Based on the analysis results, the GIS system is used to calculate the optimal delivery route. A shortest path algorithm is used to identify the most efficient route to evacuation shelters and collection points.

[1076] Step 7:

[1077] Server: Stores the generated delivery route information in a database, allowing logistics planning to proceed efficiently.

[1078] Step 8:

[1079] Server: Obtain population density and population pyramid data from governments and statistical agencies. This includes the number of people in a particular area and their age distribution. Obtain the data using API requests.

[1080] Step 9:

[1081] Server: Analyzes acquired population data to estimate the age, gender, and health status of disaster victims. Performs statistical analysis to determine the type and amount of relief supplies based on specific needs.

[1082] Step 10:

[1083] Server: Based on the estimated number of victims, calculates the type and amount of relief supplies needed. Selects supplies according to specific needs, such as medicines for the elderly or nutritional foods for children.

[1084] Step 11:

[1085] Server: Searches the database for regions and companies that can cooperate, and identifies the most suitable partner based on the type and quantity of supplies and speed of supply.

[1086] Step 12:

[1087] Server: Notify the identified collaborators. This is done using an automatic email sending system or emergency contact tool.

[1088] Step 13:

[1089] Server: Receives responses from partners and reflects them in the supply plan. This updates the supply status of materials in real time.

[1090] Step 14:

[1091] Terminal: Provide an accessible communication platform for disaster victims and relief organizations to gather supply needs and feedback through web forms and mobile apps.

[1092] Step 15:

[1093] Users: Victims and relief organizations enter their required supplies and urgent requests through the platform, including information on the type, quantity, and urgency of the supplies.

[1094] Step 16:

[1095] Server: Analyzes collected feedback in real time and reflects it in supply plans. Based on the feedback, the type and quantity of supplies can be adjusted, enabling efficient delivery to disaster-stricken areas.

[1096] This concrete step will ensure the prompt and appropriate delivery of relief supplies to disaster-stricken areas.

[1097] Example 1

[1098] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1099] In recent years, natural disasters have become more frequent, creating a need for the rapid and effective delivery of relief supplies to affected areas. Conventional methods make it difficult to quickly grasp the disaster situation, secure optimal delivery routes to affected areas, and accurately deliver supplies that meet the needs of disaster victims. Furthermore, there is a lack of a system that reflects the supply status and feedback from disaster victims in real time. This results in delays and imbalances in the supply of supplies, leading to problems with insufficient relief for disaster victims. The purpose of this invention is to solve these problems.

[1100] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1101] In this invention, the server includes means for acquiring weather data and disaster impact data, means for analyzing the acquired weather data and disaster impact data and calculating an impact score, means for saving the analysis results in a database, means for acquiring remote image data, means for analyzing the acquired images and identifying road conditions in the disaster area, means for calculating an optimal delivery route and saving the results in a database, means for acquiring demographic data, means for generating estimated information on disaster victims based on the acquired data, means for determining the type and amount of relief supplies, means for identifying and notifying regions and companies that can cooperate, means for providing a platform for collecting feedback from disaster victims and support organizations, and means for analyzing the collected feedback and updating the supply plan, thereby enabling rapid and appropriate disaster response and relief supply.

[1102] "Weather data" refers to information about weather, temperature, precipitation, wind speed, and other meteorological information.

[1103] "Disaster impact data" refers to data that shows the impact of natural disasters, such as seismic intensity, damage area, and number of victims.

[1104] The "impact score" is a numerical indicator of the impact of a disaster, calculated based on factors such as seismic intensity, area of ​​damage, and number of victims.

[1105] "Remote image data" refers to image data acquired by remotely controlled devices such as satellites and drones.

[1106] "Demographic data" refers to statistical information about age groups, gender, population density, population composition, etc. in a particular area.

[1107] "Estimated information" refers to information such as the age group, gender, and health status of victims that is estimated based on the data obtained.

[1108] "Relief supplies" refer to items such as food, drinking water, medicine, and evacuation supplies that are provided to victims of natural disasters.

[1109] "Regions and companies that can cooperate" refers to regions and companies that can cooperate in supplying relief supplies.

[1110] "Feedback" refers to opinions and requests received from disaster victims and aid organizations regarding the needs and supply status of supplies.

[1111] A "supply plan" is a plan for appropriately supplying relief supplies to disaster-stricken areas, and is updated based on collected data and feedback.

[1112] The system for implementing this invention collects and analyzes various data in order to quickly and appropriately supply relief supplies to disaster-stricken areas in the event of a disaster, and optimizes the type and quantity of supplies and delivery routes based on the results.

[1113] Data acquisition and analysis

[1114] 1. Server: The server obtains weather data and disaster impact data from disaster information providers such as the Japan Meteorological Agency through API requests, including seismic intensity data and damage extent data.

[1115] Hardware and software used: curl command, MongoDB database

[1116] Example: Seismic intensity 7, damage area 50 square kilometers, number of affected people 20,000

[1117] 2. Server: The server analyzes the acquired data using a Python program and calculates an impact score based on factors such as the seismic intensity, the extent of the damage, and the number of victims.

[1118] Hardware and software used: Python, pandas library

[1119] Example: Seismic intensity x 1.5 + damage area x 0.3 + number of affected people x 0.2 = impact score 9.5

[1120] 3. Server: The server stores the analysis results in the MongoDB database again and uses them for further processing.

[1121] Image analysis and delivery route optimization

[1122] 1. Server: The server acquires remote image data obtained from satellites and drones and stores it in NAS (Network Attached Storage).

[1123] Hardware and software used: FTP client, NAS

[1124] Examples: Satellite images (wide area of ​​the affected area), drone images (detailed road conditions)

[1125] 2. Server: The server performs image analysis using the OpenCV library to identify road conditions and obstacles in the affected area.

[1126] Hardware and software used: OpenCV library, GeoJSON format

[1127] Example: Identifying road closures and detour routes

[1128] 3. Server: The server uses GIS (Geographic Information System) tools to calculate the optimal delivery route. For this, we use QGIS software.

[1129] Hardware and software used: QGIS Python API

[1130] Example: A main road is blocked, so a detour route is calculated to determine the shortest delivery route.

[1131] Population data analysis and supply decisions

[1132] 1. Server: The server retrieves population density and population pyramid data from government statistical sites and saves them in CSV format.

[1133] Hardware and software used: crawler, CSV format

[1134] Example: Population density 1000 people / km2, population pyramid (30% elderly, 20% children)

[1135] 2. Server: The server analyzes the acquired demographic data to estimate the age group, gender, and health status of the victims, and determines the type and amount of relief supplies needed.

[1136] Hardware and software used: Python, pandas library

[1137] Example: Identifying medicines for the elderly, food for children, and water supplies

[1138] Identifying and notifying cooperating regions and companies

[1139] 1. Server: The server identifies regions and companies that can cooperate from the database based on the type and quantity of relief supplies acquired and their necessity.

[1140] Hardware and software used: SQL queries, database

[1141] Example: Supply of medicines from neighboring cities, delivery support by logistics companies

[1142] 2. Server: The server notifies the identified partners via email or SMS and promptly begins supplying supplies.

[1143] Hardware and software used: SMTP server

[1144] Example: Automatically send emails to partner companies requesting the supply of supplies

[1145] Gather feedback and update supply plans

[1146] 1. Terminals: The terminals provide accessible web forms and mobile apps for disaster victims and relief organizations to input their supply needs and emergency requests.

[1147] Hardware and software used: React, Node.js

[1148] Example: Evacuees enter their required supplies via a web form or mobile app.

[1149] 2. Users: Victims and relief organizations input their actual needs and feedback.

[1150] Example: Necessary supplies (fresh water, food, blankets)

[1151] 3. Server: The server analyzes the collected feedback and reflects it in real time in the current supply plan, thereby improving the efficiency of supply.

[1152] Hardware and software used: Machine learning model

[1153] Example: Based on feedback, we increased the supply of fresh water and added more types of medical supplies.

[1154] With this mechanism, the system of the present invention can quickly collect and analyze a variety of data, enabling optimal provision and delivery of relief supplies.

[1155] Prompt Sentence Examples

[1156] "Get the current seismic intensity data and calculate the impact score."

[1157] "Analyze the latest satellite and drone images of the affected area and calculate the optimal delivery route."

[1158] "Get the latest population data from government statistical sites and estimate the age range of the affected people."

[1159] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1160] Step 1:

[1161] Server: The server sends API requests from disaster information providers such as the Japan Meteorological Agency to obtain weather data and disaster impact data (seismic intensity data and damage extent data). The API request is used as input, and the output is disaster data in JSON format. Specifically, the server executes the API request using the curl command and stores the obtained data in a MongoDB database.

[1162] Step 2:

[1163] Server: The server analyzes the acquired disaster data using a Python program and calculates the impact score. The input is disaster data in JSON format, and the output is a numerical impact score. For data processing, the Python pandas library is used to import the data into a data frame and calculate the impact score based on a formula. Specifically, the server calculates the impact score and stores it again in the MongoDB database.

[1164] Step 3:

[1165] Server: The server obtains remote image data from satellite data providers and drone operators. The input is image data in response to a request, and the output is an image file stored in a NAS (Network Attached Storage). Specifically, the server downloads the image data using an FTP client and stores it in the NAS.

[1166] Step 4:

[1167] Server: The server uses the OpenCV library to analyze the acquired remote image data and identify road conditions and obstacles in the disaster area. The input is image data, and the output is analysis data including road conditions and obstacle location information. For data processing, OpenCV is used to extract important features from the image and save them in GeoJSON format. Specifically, the server identifies road closures and detour routes in the image.

[1168] Step 5:

[1169] Server: The server uses GIS (geographic information system) tools to calculate the optimal delivery route. The input is road condition analysis data, and the output is coordinate information for the delivery route. For data processing, the QGIS Python API is used to calculate the optimal route that avoids closed roads, and the results are saved in a database. Specifically, the server creates the shortest delivery route and saves it in GeoJSON format.

[1170] Step 6:

[1171] Server: The server retrieves population density and population pyramid data from government statistical websites. The input is a request from the crawler, and the output is population data in CSV format. Specifically, the server runs the crawler periodically, downloads CSV data from government statistical websites, and uses it for analysis.

[1172] Step 7:

[1173] Server: The server analyzes the acquired population data and estimates the age group, gender, and health status of the victims. The input is population data in CSV format, and the output is estimated information about the victims. For data processing, the server aggregates the data using Python's pandas library, and determines the type and amount of relief supplies needed based on the results. Specific operations include creating lists of medicines for the elderly, food for children, etc.

[1174] Step 8:

[1175] Server: The server identifies regions and companies that can cooperate based on the type and amount of relief supplies from a database. The input is the required relief supply data, and the output is a list of cooperation partners. Data processing involves extracting the necessary information from the database using SQL queries. Specifically, the server obtains contact information for cooperation regions and companies.

[1176] Step 9:

[1177] Server: The server notifies the partners via email or SMS. The input is the list of partners and the notification content, and the output is the status of completion of the transmission. Specifically, the server automatically sends an email requesting the supply of supplies via the SMTP server.

[1178] Step 10:

[1179] Terminal: The terminal provides a web form and a mobile app that can be accessed by disaster victims and relief organizations, allowing them to enter their supply needs and emergency requests. The input is feedback information entered by the user, and the output is feedback data stored in a database. To implement the specific operation, the web form and mobile app are implemented using React and Node.js.

[1180] Step 11:

[1181] User: Victims and relief organizations enter their actual needs and feedback. The input is information about the supplies they need (e.g., fresh water, food, blankets), and the output is saved in the database as feedback. Specific actions involve the user entering information into a form and submitting it.

[1182] Step 12:

[1183] Server: The server analyzes the collected feedback and updates the supply plan in real time. The input is the feedback data, and the output is the updated supply plan. For data processing, a machine learning model is used to analyze the feedback data and dynamically adjust the supply plan. Specifically, the server updates the supply volume and type of materials based on the analysis results.

[1184] The above processing steps enable rapid collection and analysis of a variety of data, enabling the provision and delivery of appropriate relief supplies.

[1185] (Application example 1)

[1186] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1187] In the event of a disaster, it is extremely important to quickly and appropriately supply relief supplies to affected areas. However, in conventional systems, the collection and analysis of various data tended to be done manually, resulting in supply delays and shortages or surpluses of supplies. It was also difficult to grasp road and traffic conditions in affected areas in real time, making it difficult to select appropriate delivery routes. Furthermore, there was a lack of efficient means to collect real-time needs from disaster victims, which often led to supply plans deviating from actual needs. The present invention is provided to solve these problems.

[1188] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1189] In this invention, the server includes means for acquiring seismic intensity data and damage extent data, means for analyzing the acquired seismic intensity data and damage extent data and calculating an impact score, means for storing the analysis results in a database, means for acquiring satellite images and unmanned aerial vehicle images, means for analyzing the acquired images and identifying road conditions in the disaster area, means for calculating optimal delivery routes using a geographic information system and storing the results in a database, means for acquiring population density and demographic data, means for generating estimated information on disaster victims based on the acquired data, means for determining the type and amount of relief supplies, means for identifying and notifying regions and organizations that can cooperate, means for collecting feedback from disaster victims and support organizations, means for analyzing the collected feedback and updating a supply plan, means for optimizing delivery routes using a generative AI model, and means for collecting information on the need for supplies from disaster victims in real time using a smartphone application. This allows for rapid collection and analysis of a wide variety of data, enabling optimal provision and delivery of relief supplies.

[1190] "Seismic intensity data" is data that indicates the strength of shaking when an earthquake occurs.

[1191] "Damage extent data" is data that indicates the extent of the area affected by a disaster.

[1192] The "impact score" is a quantitative score that indicates the impact of a disaster, calculated based on factors such as seismic intensity, the extent of damage, and the number of victims.

[1193] "Satellite imagery" refers to images of the Earth's surface taken by an artificial satellite located in space.

[1194] "Unmanned aerial vehicle images" are images taken from the sky by unmanned aerial vehicles such as drones.

[1195] A "geographic information system" is a system for collecting, displaying, and analyzing geographic information.

[1196] "Population density data" is data that indicates the number of people per unit area within a specific region.

[1197] "Demographic data" refers to data that shows the distribution of age groups, gender, and other demographic attributes in a certain region or group.

[1198] "Estimated information" refers to information such as the age group, gender, and health status of victims estimated based on the data obtained.

[1199] "Relief supplies" are essential items such as food, water, and medical supplies that are supplied to disaster-stricken areas during disasters.

[1200] A "generative AI model" is a model that uses artificial intelligence to perform data analysis and inference.

[1201] A "smartphone application" is a software program that runs on a smartphone and is used to collect data from users.

[1202] "Feedback" is information collected from disaster victims and aid organizations regarding an assessment of current needs and supplies.

[1203] MODE FOR CARRYING OUT THE INVENTION

[1204] This section describes a system for implementing the present invention. This system collects and analyzes a wide variety of data to quickly and appropriately supply relief supplies to disaster-stricken areas in the event of a disaster, and uses that data to optimize the type and quantity of supplies and delivery routes.

[1205] 1. Data acquisition and analysis

[1206] The server obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency, analyzes this data, and calculates an impact score. This impact score is calculated based on indicators such as seismic intensity, damage extent, and number of victims, and the analysis results are stored in a database. For example, if an earthquake with a seismic intensity of 7 occurs, the damage extent is 50 square kilometers, and the number of victims is 20,000, the impact score is calculated as follows:

[1207] Examples:

[1208] Seismic intensity data: 7

[1209] Area of ​​damage: 50 square kilometers

[1210] Number of victims: 20,000

[1211] Impact score: Seismic intensity x 1.5 + damage area x 0.3 + number of victims x 0.2 = impact score 9.5

[1212] 2. Image analysis and delivery route optimization

[1213] The server acquires satellite and drone imagery and performs image analysis to identify road conditions and obstacles in the affected area, then uses a geographic information system (GIS) to calculate optimal delivery routes and identify routes that avoid blocked roads.

[1214] Examples:

[1215] Satellite image: Wide range of the affected area

[1216] Unmanned aerial vehicle imagery: detailed road conditions

[1217] Delivery route optimization: When a main road is blocked, a detour route is calculated to determine the shortest delivery route.

[1218] 3. Population data analysis and supply decisions

[1219] The server obtains population density and demographic data from government and statistical agencies, and uses this information to estimate the age, gender, and health status of disaster victims. This information is then used to determine the type and quantity of relief supplies needed.

[1220] Examples:

[1221] Population density: 1,000 people per square kilometer

[1222] Demographics: 30% elderly, 20% children

[1223] Relief supply determination: Identify supplies of medicine for the elderly, food for children, and water.

[1224] 4. Identifying and notifying cooperating regions and organizations

[1225] The server identifies regions and organizations that can cooperate based on the type and quantity of relief supplies received and the need for them, and notifies them. At this time, notifications are sent to quickly begin supplying supplies.

[1226] Examples:

[1227] Medicine supplies from nearby cities

[1228] Delivery support by logistics companies

[1229] 5. Gather feedback and update supply plans

[1230] The user provides a smartphone application that disaster victims and relief organizations can access, allowing them to input their needs and urgent requests for supplies. The feedback is analyzed by the server and reflected in the current supply plan.

[1231] Examples:

[1232] Using a web form or smartphone app, disaster victims can input the supplies they need.

[1233] Feedback: Request for fresh water, food, and blankets

[1234] Updated supply plan based on feedback: Increased fresh water supply and added more medical supplies

[1235] 6. Use of generative AI models

[1236] The server uses a generative AI model to optimize delivery routes and generate estimated information. By inputting prompt statements to the AI ​​model, efficient analysis becomes possible.

[1237] Examples:

[1238] Disaster data: intensity: 7, affected_area: 50, affected_population: 20000

[1239] What is the best delivery route?

[1240] Also, what types and amounts of food do you need?

[1241] In this way, the system of the present invention can quickly collect and analyze a wide variety of data to provide and deliver optimal relief supplies, thereby significantly improving support for disaster victims.

[1242] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1243] Step 1:

[1244] The server receives real-time seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. Based on the input seismic intensity data and damage extent data, the server records them in a database. During this process, the latest information is collected using APIs and data feeds.

[1245] Step 2:

[1246] The server analyzes the acquired seismic intensity data and damage extent data and calculates an impact score. This weights each data point and derives an impact score. Specifically, the impact score is calculated by multiplying the seismic intensity by 1.5, the damage extent by 0.3, and the number of victims by 0.2.

[1247] Input: Seismic intensity data, damage area data, number of victims

[1248] Output: Impact score

[1249] Step 3:

[1250] The server stores the calculated impact score and the analysis results in a database. The impact score is important data to be used in the future analysis process.

[1251] Step 4:

[1252] The server acquires various satellite and unmanned aerial vehicle images, analyzes the image data, and uses machine learning algorithms to identify road conditions and obstacle locations throughout the disaster area.

[1253] Input: Satellite images, unmanned aerial vehicle images

[1254] Output: Road conditions in the affected area, location of obstacles

[1255] Step 5:

[1256] The server uses a geographic information system (GIS) to calculate the optimal delivery route, identifying routes that avoid road blocks and obstacles, and stores the optimized delivery route in a database.

[1257] Input: Road conditions, obstacle locations

[1258] Output: Optimized delivery route

[1259] Step 6:

[1260] The server obtains population density and demographic data from government and statistical agencies, analyzes it, estimates the age, gender, and health status of disaster victims, and uses this information to determine the type and amount of relief supplies needed.

[1261] Input: population density data, population composition data

[1262] Output: Type and quantity of relief supplies

[1263] Step 7:

[1264] The server identifies and notifies regions and organizations that can cooperate based on the type and amount of relief supplies, implementing protocols to quickly contact nearby cities and logistics companies.

[1265] Input: Type and quantity of relief supplies

[1266] Output: Notification to cooperating regions and organizations

[1267] Step 8:

[1268] The device uses a smartphone application to collect feedback from disaster victims and relief organizations, through which disaster victims input their needs and emergency requests.

[1269] Input: Feedback from victims and relief organizations

[1270] Output: Collected feedback data

[1271] Step 9:

[1272] The server analyzes the collected feedback and updates the supply plan, reevaluating the types and quantities of supplies to be supplied and adjusting them as necessary.

[1273] Input: Feedback data

[1274] Output: Updated supply plan

[1275] Step 10:

[1276] The server uses the generative AI model to optimize delivery routes and generate estimated information. It inputs prompts to the AI ​​model to achieve efficient analysis.

[1277] Input: prompt statement, real-time data

[1278] Output: Optimized delivery route, estimated information

[1279] As a concrete example, the following prompt sentence is input to the AI ​​model:

[1280] Disaster data: intensity: 7, affected_area: 50, affected_population: 20000

[1281] What is the best delivery route?

[1282] Also, what types and amounts of food do you need?

[1283] This will enable us to respond effectively and quickly to any disasters that occur.

[1284] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1285] This article describes a system for implementing the present invention. This system collects and analyzes a variety of data, and combines it with an emotion engine that recognizes the user's emotions to optimize the type and quantity of supplies and delivery routes in order to quickly and appropriately provide relief supplies to disaster-stricken areas in the event of a disaster.

[1286] Program processing and specific examples

[1287] Data acquisition and analysis

[1288] 1. The server obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. This data includes the time of the earthquake, epicenter, damage extent, number of victims, etc. For example, it accesses a specified API using an HTTP request to obtain the required data.

[1289] Examples:

[1290] Seismic intensity data: 7

[1291] Area of ​​damage: 50 square kilometers

[1292] Number of victims: 20,000

[1293] 2. The server integrates the acquired seismic intensity data and damage extent data to calculate an impact score. The impact score is calculated by combining the seismic intensity, damage extent, number of victims, etc. For example, the formula used is seismic intensity x 1.5 + damage extent x 0.3 + number of victims x 0.2.

[1294] Examples:

[1295] Seismic intensity x 1.5 + damage area x 0.3 + number of affected people x 0.2 = impact score 9.5

[1296] 3. The server stores the calculated impact score in a database and uses it for subsequent analysis and processing.

[1297] Image analysis and delivery route optimization

[1298] 1. The server acquires satellite and drone images and performs image analysis to identify road conditions and the location of obstacles in the affected area. It uses an image recognition algorithm based on deep learning to detect damaged roads and obstacles.

[1299] Examples:

[1300] Satellite image: Wide range of the affected area

[1301] Drone imagery: detailed road conditions

[1302] 2. The server uses a GIS (geographic information system) to calculate the optimal delivery route, using a shortest path algorithm to identify the most efficient route to evacuation shelters or collection points.

[1303] Examples:

[1304] The main road is closed, so a detour route is calculated to determine the shortest delivery route.

[1305] Population data analysis and supply decisions

[1306] 1. The server retrieves population density and population pyramid data from government or statistical agencies, including the number of people in a particular area and their age distribution. It retrieves the data using an API request.

[1307] Examples:

[1308] Population density: 1,000 people per square kilometer

[1309] Population pyramid: 30% elderly, 20% children

[1310] 2. The server analyzes the population data and estimates the age, gender, and health status of the victims. Based on this information, it determines the type and amount of relief supplies needed.

[1311] Examples:

[1312] Identify supplies of medicine for the elderly, food for children, and water

[1313] Use of emotion engine

[1314] 1. The device provides an accessible communication platform for disaster victims and relief organizations to input their needs and feedback. The platform is equipped with an emotion engine that recognizes the user's emotional state as they input their needs.

[1315] Examples:

[1316] Victims enter the supplies they need through a web form or mobile app, and the emotion engine analyzes the emotions from the text input.

[1317] 2. Users, as disaster victims or relief organizations, input their needed supplies and urgent requests through the platform, and the emotion engine recognizes emotions in real time and collects the data.

[1318] Examples:

[1319] Supplies needed: Fresh water, food, blankets. Emotional state: "Anxious" "Urgent".

[1320] 3. The server analyzes the collected feedback and emotion data in real time and reflects it in the current supply plan. By analyzing the feedback and emotion data together, the urgency and priority of supplies can be determined with even greater accuracy.

[1321] Examples:

[1322] Based on feedback, we've increased the supply of fresh water, added new types of medical supplies, and prioritized the delivery of some supplies based on emotion data.

[1323] 4. The server updates supply plans in real time and addresses the psychological needs of disaster victims, ensuring more accurate distribution of supplies.

[1324] In this way, by using the system according to the present invention, it is possible to quickly collect and analyze various data and the emotional state of the user, and to provide and deliver optimal relief supplies.

[1325] The processing flow will be explained below.

[1326] Step 1:

[1327] Server: Obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. This includes information such as the time of the earthquake, epicenter, damage extent, and number of victims. For example, an HTTP request is used to access a specified API and obtain the required data.

[1328] Step 2:

[1329] Server: The acquired seismic intensity data and damage extent data are integrated to calculate the impact score. The impact score is calculated by combining the seismic intensity, damage extent, number of victims, etc. For example, the formula used is seismic intensity x 1.5 + damage extent x 0.3 + number of victims x 0.2.

[1330] Step 3:

[1331] Server: The calculated impact scores are stored in a database for later analysis and processing.

[1332] Step 4:

[1333] Server: Acquires satellite and drone imagery. This includes retrieving data from imagery providers and drone operators. For example, downloading image data from an AWS S3 bucket.

[1334] Step 5:

[1335] Server: Analyzes acquired satellite and drone images to identify road conditions and transportation options in the affected areas. Utilizes deep learning-based image recognition algorithms to detect damaged roads and obstacles.

[1336] Step 6:

[1337] Server: Based on the analysis results, the GIS system is used to calculate the optimal delivery route. A shortest path algorithm is used to identify the most efficient route to evacuation shelters and collection points.

[1338] Step 7:

[1339] Server: Stores the generated delivery route information in a database, allowing logistics planning to proceed efficiently.

[1340] Step 8:

[1341] Server: Obtain population density and population pyramid data from governments and statistical agencies. This includes the number of people in a particular area and their age distribution. Obtain the data using API requests.

[1342] Step 9:

[1343] Server: Analyzes acquired population data to estimate the age, gender, and health status of disaster victims. Performs statistical analysis to determine the type and amount of relief supplies based on specific needs.

[1344] Step 10:

[1345] Server: Based on the estimated number of victims, calculates the type and amount of relief supplies needed. Selects supplies according to specific needs, such as medicines for the elderly or nutritional foods for children.

[1346] Step 11:

[1347] Server: Searches the database for regions and companies that can cooperate, and identifies the most suitable partner based on the type and quantity of supplies and speed of supply.

[1348] Step 12:

[1349] Server: Notify the identified collaborators. This is done using an automatic email sending system or emergency contact tool.

[1350] Step 13:

[1351] Server: Receives responses from partners and reflects them in the supply plan. This updates the supply status of materials in real time.

[1352] Step 14:

[1353] Terminal: Provides an accessible communication platform for disaster victims and relief organizations to input their needs and feedback. The platform is equipped with an emotion engine that recognizes the user's emotional state as they input.

[1354] Step 15:

[1355] Users: Victims and relief organizations input their needs and urgent requests through the platform. The emotion engine recognizes emotions in real time and collects the data.

[1356] Step 16:

[1357] Server: Analyzes collected feedback and sentiment data in real time and reflects it in the current supply plan. By analyzing feedback and sentiment data together, the urgency and priority of supplies can be determined with even greater accuracy.

[1358] Step 17:

[1359] Server: Updates supply plans in real time and addresses the psychological needs of disaster victims, ensuring more accurate distribution of supplies.

[1360] Example 2

[1361] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1362] Conventional disaster relief systems have struggled to collect and analyze diverse data quickly and accurately, and efficiently provide and deliver relief supplies. Furthermore, they lack the ability to incorporate feedback from disaster victims and relief organizations, making it difficult to formulate supply plans that respond to actual needs. Furthermore, supply plans do not take into account the psychological state of disaster victims, making it difficult to improve disaster satisfaction.

[1363] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1364] In this invention, the server includes means for acquiring seismic intensity data and damage extent data, means for analyzing the acquired seismic intensity data and damage extent data to calculate an impact score, means for saving the analysis results in a database, means for acquiring satellite images and aerial images, means for analyzing the acquired images to identify road conditions and the locations of obstacles in the disaster area, means for calculating an optimal delivery route using an optimal route algorithm and saving the results in a database, means for acquiring population density and population composition data, means for generating estimated information on disaster victims based on the acquired data, means for determining the type and quantity of relief supplies, means for identifying and notifying regions and organizations that can cooperate, means for collecting feedback from disaster victims and relief organizations, means for analyzing the collected feedback and updating the supply plan, means for recognizing emotional states from user input and collecting that data, and means for analyzing the emotional data to determine the urgency and priority of supplies and reflecting the results in the supply plan.

[1365] This will enable the rapid collection of diverse data, highly accurate analysis, and optimization of relief supply taking into account user emotions.

[1366] "Seismic intensity data" is a numerical value that indicates the strength of earthquake shaking and quantitatively represents the impact of an earthquake.

[1367] "Damage extent data" means data that indicates the extent of damage in a disaster-affected region and identifies the geographic area affected.

[1368] The "impact score" is a numerical value calculated by combining multiple data elements, such as seismic intensity data, damage extent data, and number of victims, and is used to comprehensively evaluate the impact of a disaster.

[1369] "Satellite imagery" means an image of a geographic area taken from a satellite that has a wide field of view and shows the ground in detail.

[1370] "Airborne imagery" means images of a geographic area taken from a drone or other aircraft that provide a detailed view from a low altitude.

[1371] An "optimal route algorithm" is an algorithm that calculates the shortest distance to a destination or the most efficient route, and is used to optimize delivery routes.

[1372] "Population density data" is data that indicates the density of people living in a particular area and is used to understand the population distribution of the area.

[1373] "Demographic data" refers to data that shows the population distribution of age, gender, etc. in a specific area, and is used to understand the characteristics of disaster victims.

[1374] "Estimated information on victims" is information used to estimate the age group, gender, health status, etc. of victims based on acquired population data, etc., and to assess the need for relief.

[1375] "Relief supplies" are items needed when a disaster occurs, such as food, medicine, clothing, and water, to support the lives of disaster victims.

[1376] "Regions and organizations that can cooperate" refers to regions and cooperative organizations that can support relief efforts in the event of a disaster, including the provision of relief supplies and human support.

[1377] "Feedback" refers to information collected from disaster victims and relief organizations, and indicates the needs for supplies, the situation on the ground, and the level of urgency.

[1378] "Emotional state" indicates the emotional state the user is feeling, and recognizes emotions such as anxiety, urgency, and relief in real time.

[1379] The system of the present invention collects and analyzes a variety of data, and further combines it with an emotion engine that recognizes the user's emotions to optimize the type and quantity of supplies and delivery routes in order to quickly and appropriately supply relief supplies to disaster-stricken areas in the event of a disaster. Specific embodiments of the system are described below.

[1380] Hardware and Software Configuration

[1381] This system uses the following hardware and software:

[1382] Server: A computer system with high-performance computing power that collects, analyzes, stores, and calculates routes.

[1383] Terminal: A device that provides a web browser and mobile apps for use by disaster victims and relief organizations.

[1384] Emotion engine: A software module for recognizing the emotional state from user input, such as a sentiment analysis tool that uses natural language processing (NLP) techniques.

[1385] GIS (Geographic Information System): A tool that handles geographic data and calculates optimal delivery routes. Examples include QGIS and ArcGIS.

[1386] Image recognition algorithm: A deep learning algorithm used to analyze satellite and aerial images. TensorFlow and PyTorch are used.

[1387] Data collection and analysis

[1388] 1. The server obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. This data includes the time of the earthquake, epicenter, damage extent, and number of victims. As a specific example, it accesses the API using an HTTP request and obtains a response in JSON format.

[1389] For example, send a request like GET / earthquake / data.

[1390] 2. The server integrates the acquired seismic intensity data and damage extent data to calculate the impact score. The impact score is calculated using the formula: seismic intensity x 1.5 + damage extent x 0.3 + number of victims x 0.2.

[1391] Example: If the earthquake has a magnitude of 7, the damage area is 50 square kilometers, and the number of affected people is 20,000, the impact score is 7 x 1.5 + 50 x 0.3 + 20,000 x 0.2 = 9.5.

[1392] Image analysis and delivery route optimization

[1393] 1. The server acquires satellite and aerial imagery and analyzes them using an image recognition algorithm based on deep learning, thereby identifying road damage and the location of obstacles.

[1394] Example: Access the GET / satellite_images and GET / drone_images endpoints to retrieve and analyze image data.

[1395] 2. The server uses GIS to calculate the optimal delivery route, using a shortest path algorithm (e.g., Dijkstra algorithm) to identify the most efficient route to a shelter or collection point.

[1396] Example: If a major road is blocked, calculate a detour route to create the shortest route.

[1397] Population data analysis and material decisions

[1398] 1. The server obtains population density and demographic data from government and statistical agencies, including the number of people in a particular area and their age distribution.

[1399] Example: Access the GET / population_data endpoint to retrieve data.

[1400] 2. The server analyzes the acquired population data to estimate the age, gender, and health status of the victims. Based on this information, it determines the type and amount of relief supplies needed.

[1401] Examples: Identifying supplies of medicine for the elderly, food for children, and water.

[1402] Use of emotion engine

[1403] 1. The device provides a communication platform for disaster victims and relief organizations, allowing them to input their needs and feedback. It also has a built-in emotion engine that recognizes the user's emotional state from their input.

[1404] Example: When a disaster victim enters "I need fresh water. I'm anxious" through a web form or mobile app, the emotion engine recognizes the emotion "anxiety."

[1405] 2. The server analyzes the collected feedback and emotion data in real time and reflects it in the supply plan, thereby determining the urgency and priority of supplies with even greater accuracy.

[1406] Example: Increase the supply of fresh water based on feedback, and prioritize the delivery of certain supplies based on sentiment data.

[1407] Prompt Sentence Examples

[1408] By inputting the following prompt sentence into the generative AI model, it is possible to generate a list of supplies appropriate for the needs of the disaster-stricken area.

[1409] Please create a list of supplies needed for elderly people in the disaster-stricken areas. There has been a magnitude 7 earthquake, affecting an area of ​​50 square kilometers and affecting 20,000 people. Many of the victims are feeling anxious and in a state of emergency.

[1410] This invention enables rapid collection and highly accurate analysis of various data during disasters, as well as optimization of relief supplies that take into account the emotions of users, thereby enabling rapid and accurate support for disaster victims.

[1411] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1412] Step 1: Obtain disaster information

[1413] 1. The server obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. Specifically, it sends an HTTP request to access a specified API and obtains data such as the time of the earthquake, epicenter, damage extent, and number of victims. The input is a request to the API endpoint, and the output is the obtained seismic intensity data and damage extent data in JSON format.

[1414] What it does: The server sends a request to an API endpoint such as GET / earthquake / data, parses the data received in response, and stores it in an internal database.

[1415] Step 2: Calculating the impact score

[1416] 2. The server integrates the acquired seismic intensity data and damage extent data to calculate an impact score. The impact score is calculated using the formula: seismic intensity x 1.5 + damage extent x 0.3 + number of victims x 0.2. The input is the acquired seismic intensity data and damage extent data, and the output is the impact score.

[1417] What it does: The server reads each data point as a number and calculates an impact score based on a formula.

[1418] Step 3: Save the impact score

[1419] 3. The server stores the calculated impact score in a database and uses it for subsequent analysis and processing. The input is the calculated impact score, and the output is the result stored in the database.

[1420] Specific behavior: The server executes an SQL query such as INSERT INTO impact_scores (timestamp, score) VALUES (current_timestamp, impact_score).

[1421] Step 4: Image acquisition and analysis

[1422] 4. The server acquires satellite images and drone images and analyzes them using an image recognition algorithm based on deep learning. The input is the satellite images and drone images, and the output is the analysis results (data showing road conditions and the location of obstacles).

[1423] Specific operation: The server accesses the GET / satellite_images and GET / drone_images endpoints to obtain image data, analyzes it using TensorFlow, PyTorch, etc., and detects damaged roads and obstacles.

[1424] Step 5: Optimize delivery routes

[1425] 5. The server uses GIS to calculate the optimal delivery route. Here, a shortest path algorithm (e.g., Dijkstra algorithm) is used. The input is the analyzed road condition data, and the output is the optimal delivery route.

[1426] Specific operation: The server analyzes road network data using tools such as QGIS and ArcGIS, calculates the shortest route, and stores it in a database.

[1427] Step 6: Collect and analyze population data

[1428] 6. The server obtains population density and demographic data from government and statistical bureaus, and analyzes it to estimate the age, gender, and health status of disaster victims. Based on this information, it determines the type and amount of relief supplies. The input is the obtained population data, and the output is a list of needed relief supplies.

[1429] Specific operation: The server accesses the GET / population_data endpoint to collect data, analyzes the data using analysis tools such as Pandas, and generates a supply list based on the estimated results.

[1430] Step 7: Collect feedback and sentiment data

[1431] 7. The device provides an accessible communication platform for disaster victims and relief organizations, allowing them to input their needs and feedback. The emotion engine recognizes their emotional state from these inputs. The input is user feedback and emotion data, and the output is analyzed data.

[1432] Specific operation: The device provides a web form or mobile app, collects user input data, and sends it to the server. The emotion engine analyzes the input text and generates emotion data. When a user inputs "I need fresh water. I'm anxious," the emotion engine analyzes the text and recognizes the emotion "anxiety."

[1433] Step 8: Update the supply plan

[1434] 8. The server analyzes the collected feedback and emotion data in real time and reflects it in the supply plan. It determines the urgency and priority of supplies with high accuracy. The input is feedback and emotion data, and the output is an updated supply plan.

[1435] Specific operation: The server integrates feedback data and emotion data, and applies a priority algorithm to optimize supply planning. Based on feedback, it increases the supply of fresh water, and prioritizes the delivery of some supplies based on emotion data.

[1436] Through the above steps, the system of the present invention quickly collects and analyzes various data and the user's emotional state, and realizes optimal provision and delivery of relief supplies.

[1437] (Application example 2)

[1438] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1439] In the event of a disaster, it is important to provide appropriate relief supplies to affected areas quickly. However, it is difficult to grasp the state of the affected area and the needs of the victims in real time, making it difficult to optimize the type and quantity of supplies and delivery routes. It is also necessary to update supply plans that take into account the psychological state and urgency of the victims.

[1440] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1441] In this invention, the server includes means for acquiring seismic intensity data and damage extent data, means for analyzing the acquired seismic intensity data and damage extent data and calculating an impact score, means for saving the analysis results in a database, means for acquiring satellite images and drone images, means for analyzing the acquired images and identifying road conditions in the disaster area, means for calculating an optimal delivery route and saving the results in a database, means for acquiring population density and population pyramid data, means for generating estimated information on disaster victims based on the acquired data, means for determining the type and amount of relief supplies, means for identifying and notifying regions and companies that can cooperate, means for collecting feedback from disaster victims and relief organizations, means for analyzing the collected feedback and updating the supply plan, means for recognizing the emotional state of disaster victims using an emotion engine, and means for predicting the supplies needed by disaster victims using a generative AI model. This allows the situation in the disaster area and the needs of disaster victims to be grasped in real time, enabling the provision and delivery of optimal relief supplies.

[1442] "Seismic intensity data" is numerical information that indicates the strength of earthquake shaking.

[1443] "Damage extent data" is information indicating the size and extent of the area affected by a disaster.

[1444] The "impact score" is a number that indicates the degree of impact of the damage, calculated based on data such as seismic intensity, area of ​​damage, and number of victims.

[1445] A "satellite image" is an image of the Earth's surface taken by an artificial satellite.

[1446] "Drone imagery" is a detailed image of the Earth's surface taken by an unmanned aerial vehicle (drone).

[1447] "Road conditions" is information indicating the state of road damage and whether the road is passable.

[1448] A "delivery route" is the route along which goods are delivered to their destination.

[1449] "Population density" is a statistical information that indicates the number of people per certain area.

[1450] "Population pyramid data" is statistical information that shows the distribution of age groups and gender in a particular area.

[1451] "Estimated information on victims" refers to information such as the age group, gender, and health status of victims estimated based on population data.

[1452] "Relief supplies" are goods provided to disaster victims in the event of a disaster, including food, water, medicine, etc.

[1453] "Regions and companies that can cooperate" refers to regions and companies that can provide support in the event of a disaster.

[1454] "Feedback" refers to information such as requests, opinions, and reports from disaster victims and relief organizations.

[1455] An "emotion engine" is an algorithm or software that recognizes a user's emotional state.

[1456] A "generative AI model" is a model that uses artificial intelligence to predict needed supplies and information from input data.

[1457] The system for carrying out the present invention will now be described in detail. This system is a smartphone application for quickly and appropriately supplying relief supplies to disaster-stricken areas, particularly in the event of a disaster.

[1458] First, the server obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. The server uses an HTTP request to access a specified API and obtains data such as the time of the earthquake, epicenter, damage extent, and number of victims. Examples include "seismic intensity data: seismic intensity 7," "damage extent: 50 square kilometers," and "number of victims: 20,000." Based on the obtained data, the server calculates an impact score and stores the results in a database.

[1459] Next, the server acquires satellite and drone images and uses deep learning models (e.g., TensorFlow, PyTorch) to identify road conditions in the affected areas. The server analyzes these images to detect damaged roads and obstacles, calculates optimal delivery routes, and stores them in a database. For example, if a main road is blocked, the server can calculate a detour route to determine the shortest delivery route.

[1460] The server also obtains population density and population pyramid data from governments and statistical bureaus, and uses this data to estimate the age, gender, and health status of disaster victims. The server uses this information to determine the type and amount of relief supplies needed. Specific examples include identifying "medicine for the elderly," "food for children," and "water supplies."

[1461] The device also provides a communication platform accessible to disaster victims and relief organizations. This platform is equipped with an emotion engine that analyzes the emotional state of disaster victims from text input. For example, disaster victims can input their required supplies via a web form or mobile app, and data such as "emotional state: anxiety, emergency" can be collected.

[1462] The server analyzes the collected feedback and emotion data in real time and reflects it in the current supply plan. For example, it can increase the supply of fresh water and add more types of medical supplies based on the collected feedback. It can also prioritize the delivery of certain supplies based on emotion data.

[1463] Finally, the generative AI model is used to predict what supplies the disaster victims need. This is done using prompts, such as "What supplies do the disaster victims need?", which allows the generative AI model to return predictions such as "Clean water, non-perishable food, blankets, and medicines."

[1464] In this way, the system according to the present invention can realize efficient and appropriate provision and delivery of relief supplies in the event of a disaster.

[1465] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1466] Step 1:

[1467] The server obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. To do this, the server uses an HTTP request to access a specified API and obtains data such as the time of the earthquake, epicenter, damage extent, and number of victims. The input is the data from the API, and the output is to store this data in an internal structure.

[1468] Step 2:

[1469] The server analyzes the seismic intensity data and damage extent data acquired in step 1 and calculates the impact score. Specifically, the impact score is calculated using the formula: seismic intensity x 1.5, damage extent x 0.3, number of victims x 0.2. The input data are seismic intensity, damage extent, and number of victims, and the output is the calculated impact score.

[1470] Step 3:

[1471] The server stores the calculated impact score in a database. Using the analysis results, important information such as the impact score is stored in the database for later analysis and processing. The input is the impact score, and the output is the result stored in the database.

[1472] Step 4:

[1473] The server acquires satellite and drone images. To do this, the server accesses the corresponding data acquisition APIs and data streams to acquire image data. The input is image data from satellites and drones, and the output is the acquired image data.

[1474] Step 5:

[1475] The server analyzes the acquired images and identifies the road conditions in the disaster area. Deep learning models (e.g., TensorFlow, PyTorch) are used to detect damaged roads and obstacles in the images. The input is satellite and drone images, and the output is road condition data as the analysis result.

[1476] Step 6:

[1477] The server calculates the optimal delivery route based on the analysis results and stores the results in a database. It uses a GIS (geographic information system) to run a shortest path algorithm to identify the most efficient route to evacuation shelters and collection points. The input is road condition data, and the output is the optimal delivery route.

[1478] Step 7:

[1479] The server retrieves population density and population pyramid data from governments and statistical offices. To do this, the server uses an API request to retrieve the specified data. The input is the data from governments and statistical offices, and the output is the retrieved population data.

[1480] Step 8:

[1481] The server analyzes the acquired population data and estimates the age group, gender, and health status of the victims. The data is analyzed using Python's Pandas library, etc. The input is population density and population pyramid data, and the output is estimated victim information.

[1482] Step 9:

[1483] The server determines the type and amount of relief supplies needed based on the estimated information. It identifies the supplies needed based on the age group and health condition of the victims. Specific examples include "medicine for the elderly" and "food for children." The input is victim information, and the output is the type and amount of relief supplies.

[1484] Step 10:

[1485] The device provides an accessible communication platform for disaster victims and relief organizations to input their needs and feedback. This platform incorporates an emotion engine (e.g., Microsoft Azure's Text Analytics API) to analyze the user's emotional state from their text input. The input is text input from the user, and the output is analyzed emotional data.

[1486] Step 11:

[1487] The server analyzes the collected feedback and emotion data in real time and reflects it in the supply plan. By analyzing the feedback and emotion data, the urgency and priority of supplies can be determined with greater precision. Specific examples include "increase the supply of fresh water" and "add new types of medical supplies." The input is feedback and emotion data, and the output is an updated supply plan.

[1488] Step 12:

[1489] The server uses a generative AI model to predict the supplies needed by disaster victims. A prompt is input into the generative AI model to predict the supplies needed. For example, by inputting a prompt such as "Please tell us what supplies the disaster victims need," a prediction such as "Clean water, non-perishable food, blankets, and medicines are needed" can be obtained. The input is the prompt, and the output is the prediction result from the generative AI model.

[1490] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1491] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1492] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1493] [Fourth embodiment]

[1494] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1495] 7, a 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.

[1496] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1497] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1498] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1499] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1500] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1501] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1502] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1503] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1504] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1505] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1506] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1507] This section describes a system for implementing the present invention. This system collects and analyzes a wide variety of data in order to quickly and appropriately supply relief supplies to disaster-stricken areas in the event of a disaster, and then optimizes the type and quantity of supplies and delivery routes based on the results.

[1508] Program processing and specific examples

[1509] Data acquisition and analysis

[1510] 1. Server: Obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. This data includes the time of the earthquake, epicenter, damage extent, number of victims, etc.

[1511] Examples:

[1512] Seismic intensity data: 7

[1513] Area of ​​damage: 50 square kilometers

[1514] Number of victims: 20,000

[1515] 2. Server: Analyzes the acquired data and calculates the impact score. The impact score is calculated based on the seismic intensity, the extent of the damage, the number of victims, etc.

[1516] Examples:

[1517] Seismic intensity x 1.5 + damage area x 0.3 + number of affected people x 0.2 = impact score 9.5

[1518] 3. Server: The analysis results are stored in a database and used for subsequent processing.

[1519] Image analysis and delivery route optimization

[1520] 1. Server: Acquires satellite and drone images and performs image analysis to identify road conditions and the location of obstacles in the affected areas.

[1521] Examples:

[1522] Satellite image: Wide range of the affected area

[1523] Drone imagery: detailed road conditions

[1524] 2. Server: Uses GIS (geographic information systems) to calculate optimal delivery routes, avoiding blocked roads and identifying routes that will deliver goods quickly.

[1525] Examples:

[1526] The main road is closed, so a detour route is calculated to determine the shortest delivery route.

[1527] Population data analysis and supply decisions

[1528] 1. Server: Obtains population density and population pyramid data from government and statistical bureaus.

[1529] Examples:

[1530] Population density: 1,000 people per square kilometer

[1531] Population pyramid: 30% elderly, 20% children

[1532] 2. Server: Analyzes the acquired data and estimates the age, gender, and health status of the victims. Based on this information, the type and amount of relief supplies needed are determined.

[1533] Examples:

[1534] Identify supplies of medicine for the elderly, food for children, and water

[1535] Identifying and notifying cooperating regions and companies

[1536] 1. Server: Identifies neighboring areas and cooperating companies from a database based on the type and quantity of relief supplies obtained and their necessity.

[1537] Examples:

[1538] Supply of medicines from neighboring cities and delivery support from logistics companies

[1539] 2. Server: Notify the identified partners and promptly begin supplying supplies.

[1540] Gather feedback and update supply plans

[1541] 1. Terminal: Providing a platform that disaster victims and relief organizations can access and input their needs and emergency requests.

[1542] Examples:

[1543] Victims can enter their supply needs using a web form or mobile app.

[1544] 2. Users: Victims and relief organizations input their actual needs and feedback.

[1545] Examples:

[1546] Necessary supplies: fresh water, food, blankets

[1547] 3. Server: Analyzes the collected feedback and reflects it in the current supply plan, thereby ensuring more accurate supply of supplies to disaster-stricken areas.

[1548] Examples:

[1549] Based on feedback, we have increased the supply of fresh water and added more types of medical supplies.

[1550] In this way, by using the system according to the present invention, it is possible to quickly collect and analyze a variety of data and realize optimal provision and delivery of relief supplies.

[1551] The processing flow will be explained below.

[1552] Step 1:

[1553] Server: Obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. This includes information such as the time of the earthquake, epicenter, damage extent, and number of victims. For example, an HTTP request is used to access a specified API and obtain the required data.

[1554] Step 2:

[1555] Server: The acquired seismic intensity data and damage extent data are integrated to calculate the impact score. The impact score is calculated by combining the seismic intensity, damage extent, number of victims, etc. For example, the formula used is seismic intensity x 1.5 + damage extent x 0.3 + number of victims x 0.2.

[1556] Step 3:

[1557] Server: The calculated impact scores are stored in a database for later analysis and processing.

[1558] Step 4:

[1559] Server: Acquires satellite and drone imagery. This includes retrieving data from imagery providers and drone operators. For example, downloading image data from an AWS S3 bucket.

[1560] Step 5:

[1561] Server: Analyzes acquired satellite and drone images to identify road conditions and transportation options in the affected areas. Utilizes deep learning-based image recognition algorithms to detect damaged roads and obstacles.

[1562] Step 6:

[1563] Server: Based on the analysis results, the GIS system is used to calculate the optimal delivery route. A shortest path algorithm is used to identify the most efficient route to evacuation shelters and collection points.

[1564] Step 7:

[1565] Server: Stores the generated delivery route information in a database, allowing logistics planning to proceed efficiently.

[1566] Step 8:

[1567] Server: Obtain population density and population pyramid data from governments and statistical agencies. This includes the number of people in a particular area and their age distribution. Obtain the data using API requests.

[1568] Step 9:

[1569] Server: Analyzes acquired population data to estimate the age, gender, and health status of disaster victims. Performs statistical analysis to determine the type and amount of relief supplies based on specific needs.

[1570] Step 10:

[1571] Server: Based on the estimated number of victims, calculates the type and amount of relief supplies needed. Selects supplies according to specific needs, such as medicines for the elderly or nutritional foods for children.

[1572] Step 11:

[1573] Server: Searches the database for regions and companies that can cooperate, and identifies the most suitable partner based on the type and quantity of supplies and speed of supply.

[1574] Step 12:

[1575] Server: Notify the identified collaborators. This is done using an automatic email sending system or emergency contact tool.

[1576] Step 13:

[1577] Server: Receives responses from partners and reflects them in the supply plan. This updates the supply status of materials in real time.

[1578] Step 14:

[1579] Terminal: Provide an accessible communication platform for disaster victims and relief organizations to gather supply needs and feedback through web forms and mobile apps.

[1580] Step 15:

[1581] Users: Victims and relief organizations enter their required supplies and urgent requests through the platform, including information on the type, quantity, and urgency of the supplies.

[1582] Step 16:

[1583] Server: Analyzes collected feedback in real time and reflects it in supply plans. Based on the feedback, the type and quantity of supplies can be adjusted, enabling efficient delivery to disaster-stricken areas.

[1584] This concrete step will ensure the prompt and appropriate delivery of relief supplies to disaster-stricken areas.

[1585] Example 1

[1586] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1587] In recent years, natural disasters have become more frequent, creating a need for the rapid and effective delivery of relief supplies to affected areas. Conventional methods make it difficult to quickly grasp the disaster situation, secure optimal delivery routes to affected areas, and accurately deliver supplies that meet the needs of disaster victims. Furthermore, there is a lack of a system that reflects the supply status and feedback from disaster victims in real time. This results in delays and imbalances in the supply of supplies, leading to problems with insufficient relief for disaster victims. The purpose of this invention is to solve these problems.

[1588] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1589] In this invention, the server includes means for acquiring weather data and disaster impact data, means for analyzing the acquired weather data and disaster impact data and calculating an impact score, means for saving the analysis results in a database, means for acquiring remote image data, means for analyzing the acquired images and identifying road conditions in the disaster area, means for calculating an optimal delivery route and saving the results in a database, means for acquiring demographic data, means for generating estimated information on disaster victims based on the acquired data, means for determining the type and amount of relief supplies, means for identifying and notifying regions and companies that can cooperate, means for providing a platform for collecting feedback from disaster victims and support organizations, and means for analyzing the collected feedback and updating the supply plan, thereby enabling rapid and appropriate disaster response and relief supply.

[1590] "Weather data" refers to information about weather, temperature, precipitation, wind speed, and other meteorological information.

[1591] "Disaster impact data" refers to data that shows the impact of natural disasters, such as seismic intensity, damage area, and number of victims.

[1592] The "impact score" is a numerical indicator of the impact of a disaster, calculated based on factors such as seismic intensity, area of ​​damage, and number of victims.

[1593] "Remote image data" refers to image data acquired by remotely controlled devices such as satellites and drones.

[1594] "Demographic data" refers to statistical information about age groups, gender, population density, population composition, etc. in a particular area.

[1595] "Estimated information" refers to information such as the age group, gender, and health status of victims that is estimated based on the data obtained.

[1596] "Relief supplies" refer to items such as food, drinking water, medicine, and evacuation supplies that are provided to victims of natural disasters.

[1597] "Regions and companies that can cooperate" refers to regions and companies that can cooperate in supplying relief supplies.

[1598] "Feedback" refers to opinions and requests received from disaster victims and aid organizations regarding the needs and supply status of supplies.

[1599] A "supply plan" is a plan for appropriately supplying relief supplies to disaster-stricken areas, and is updated based on collected data and feedback.

[1600] The system for implementing this invention collects and analyzes various data in order to quickly and appropriately supply relief supplies to disaster-stricken areas in the event of a disaster, and optimizes the type and quantity of supplies and delivery routes based on the results.

[1601] Data acquisition and analysis

[1602] 1. Server: The server obtains weather data and disaster impact data from disaster information providers such as the Japan Meteorological Agency through API requests, including seismic intensity data and damage extent data.

[1603] Hardware and software used: curl command, MongoDB database

[1604] Example: Seismic intensity 7, damage area 50 square kilometers, number of affected people 20,000

[1605] 2. Server: The server analyzes the acquired data using a Python program and calculates an impact score based on factors such as the seismic intensity, the extent of the damage, and the number of victims.

[1606] Hardware and software used: Python, pandas library

[1607] Example: Seismic intensity x 1.5 + damage area x 0.3 + number of affected people x 0.2 = impact score 9.5

[1608] 3. Server: The server stores the analysis results in the MongoDB database again and uses them for further processing.

[1609] Image analysis and delivery route optimization

[1610] 1. Server: The server acquires remote image data obtained from satellites and drones and stores it in NAS (Network Attached Storage).

[1611] Hardware and software used: FTP client, NAS

[1612] Examples: Satellite images (wide area of ​​the affected area), drone images (detailed road conditions)

[1613] 2. Server: The server performs image analysis using the OpenCV library to identify road conditions and obstacles in the affected area.

[1614] Hardware and software used: OpenCV library, GeoJSON format

[1615] Example: Identifying road closures and detour routes

[1616] 3. Server: The server uses GIS (Geographic Information System) tools to calculate the optimal delivery route. For this, we use QGIS software.

[1617] Hardware and software used: QGIS Python API

[1618] Example: A main road is blocked, so a detour route is calculated to determine the shortest delivery route.

[1619] Population data analysis and supply decisions

[1620] 1. Server: The server retrieves population density and population pyramid data from government statistical sites and saves them in CSV format.

[1621] Hardware and software used: crawler, CSV format

[1622] Example: Population density 1000 people / km2, population pyramid (30% elderly, 20% children)

[1623] 2. Server: The server analyzes the acquired demographic data to estimate the age group, gender, and health status of the victims, and determines the type and amount of relief supplies needed.

[1624] Hardware and software used: Python, pandas library

[1625] Example: Identifying medicines for the elderly, food for children, and water supplies

[1626] Identifying and notifying cooperating regions and companies

[1627] 1. Server: The server identifies regions and companies that can cooperate from the database based on the type and quantity of relief supplies acquired and their necessity.

[1628] Hardware and software used: SQL queries, database

[1629] Example: Supply of medicines from neighboring cities, delivery support by logistics companies

[1630] 2. Server: The server notifies the identified partners via email or SMS and promptly begins supplying supplies.

[1631] Hardware and software used: SMTP server

[1632] Example: Automatically send emails to partner companies requesting the supply of supplies

[1633] Gather feedback and update supply plans

[1634] 1. Terminals: The terminals provide accessible web forms and mobile apps for disaster victims and relief organizations to input their supply needs and emergency requests.

[1635] Hardware and software used: React, Node.js

[1636] Example: Evacuees enter their required supplies via a web form or mobile app.

[1637] 2. Users: Victims and relief organizations input their actual needs and feedback.

[1638] Example: Necessary supplies (fresh water, food, blankets)

[1639] 3. Server: The server analyzes the collected feedback and reflects it in real time in the current supply plan, thereby improving the efficiency of supply.

[1640] Hardware and software used: Machine learning model

[1641] Example: Based on feedback, we increased the supply of fresh water and added more types of medical supplies.

[1642] With this mechanism, the system of the present invention can quickly collect and analyze a variety of data, enabling optimal provision and delivery of relief supplies.

[1643] Prompt Sentence Examples

[1644] "Get the current seismic intensity data and calculate the impact score."

[1645] "Analyze the latest satellite and drone images of the affected area and calculate the optimal delivery route."

[1646] "Get the latest population data from government statistical sites and estimate the age range of the affected people."

[1647] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1648] Step 1:

[1649] Server: The server sends API requests from disaster information providers such as the Japan Meteorological Agency to obtain weather data and disaster impact data (seismic intensity data and damage extent data). The API request is used as input, and the output is disaster data in JSON format. Specifically, the server executes the API request using the curl command and stores the obtained data in a MongoDB database.

[1650] Step 2:

[1651] Server: The server analyzes the acquired disaster data using a Python program and calculates the impact score. The input is disaster data in JSON format, and the output is a numerical impact score. For data processing, the Python pandas library is used to import the data into a data frame and calculate the impact score based on a formula. Specifically, the server calculates the impact score and stores it again in the MongoDB database.

[1652] Step 3:

[1653] Server: The server obtains remote image data from satellite data providers and drone operators. The input is image data in response to a request, and the output is an image file stored in a NAS (Network Attached Storage). Specifically, the server downloads the image data using an FTP client and stores it in the NAS.

[1654] Step 4:

[1655] Server: The server uses the OpenCV library to analyze the acquired remote image data and identify road conditions and obstacles in the disaster area. The input is image data, and the output is analysis data including road conditions and obstacle location information. For data processing, OpenCV is used to extract important features from the image and save them in GeoJSON format. Specifically, the server identifies road closures and detour routes in the image.

[1656] Step 5:

[1657] Server: The server uses GIS (geographic information system) tools to calculate the optimal delivery route. The input is road condition analysis data, and the output is coordinate information for the delivery route. For data processing, the QGIS Python API is used to calculate the optimal route that avoids closed roads, and the results are saved in a database. Specifically, the server creates the shortest delivery route and saves it in GeoJSON format.

[1658] Step 6:

[1659] Server: The server retrieves population density and population pyramid data from government statistical websites. The input is a request from the crawler, and the output is population data in CSV format. Specifically, the server runs the crawler periodically, downloads CSV data from government statistical websites, and uses it for analysis.

[1660] Step 7:

[1661] Server: The server analyzes the acquired population data and estimates the age group, gender, and health status of the victims. The input is population data in CSV format, and the output is estimated information about the victims. For data processing, the server aggregates the data using Python's pandas library, and determines the type and amount of relief supplies needed based on the results. Specific operations include creating lists of medicines for the elderly, food for children, etc.

[1662] Step 8:

[1663] Server: The server identifies regions and companies that can cooperate based on the type and amount of relief supplies from a database. The input is the required relief supply data, and the output is a list of cooperation partners. Data processing involves extracting the necessary information from the database using SQL queries. Specifically, the server obtains contact information for cooperation regions and companies.

[1664] Step 9:

[1665] Server: The server notifies the partners via email or SMS. The input is the list of partners and the notification content, and the output is the status of completion of the transmission. Specifically, the server automatically sends an email requesting the supply of supplies via the SMTP server.

[1666] Step 10:

[1667] Terminal: The terminal provides a web form and a mobile app that can be accessed by disaster victims and relief organizations, allowing them to enter their supply needs and emergency requests. The input is feedback information entered by the user, and the output is feedback data stored in a database. To implement the specific operation, the web form and mobile app are implemented using React and Node.js.

[1668] Step 11:

[1669] User: Victims and relief organizations enter their actual needs and feedback. The input is information about the supplies they need (e.g., fresh water, food, blankets), and the output is saved in the database as feedback. Specific actions involve the user entering information into a form and submitting it.

[1670] Step 12:

[1671] Server: The server analyzes the collected feedback and updates the supply plan in real time. The input is the feedback data, and the output is the updated supply plan. For data processing, a machine learning model is used to analyze the feedback data and dynamically adjust the supply plan. Specifically, the server updates the supply volume and type of materials based on the analysis results.

[1672] The above processing steps enable rapid collection and analysis of a variety of data, enabling the provision and delivery of appropriate relief supplies.

[1673] (Application example 1)

[1674] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1675] In the event of a disaster, it is extremely important to quickly and appropriately supply relief supplies to affected areas. However, in conventional systems, the collection and analysis of various data tended to be done manually, resulting in supply delays and shortages or surpluses of supplies. It was also difficult to grasp road and traffic conditions in affected areas in real time, making it difficult to select appropriate delivery routes. Furthermore, there was a lack of efficient means to collect real-time needs from disaster victims, which often led to supply plans deviating from actual needs. The present invention is provided to solve these problems.

[1676] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1677] In this invention, the server includes means for acquiring seismic intensity data and damage extent data, means for analyzing the acquired seismic intensity data and damage extent data and calculating an impact score, means for storing the analysis results in a database, means for acquiring satellite images and unmanned aerial vehicle images, means for analyzing the acquired images and identifying road conditions in the disaster area, means for calculating optimal delivery routes using a geographic information system and storing the results in a database, means for acquiring population density and demographic data, means for generating estimated information on disaster victims based on the acquired data, means for determining the type and amount of relief supplies, means for identifying and notifying regions and organizations that can cooperate, means for collecting feedback from disaster victims and support organizations, means for analyzing the collected feedback and updating a supply plan, means for optimizing delivery routes using a generative AI model, and means for collecting information on the need for supplies from disaster victims in real time using a smartphone application. This allows for rapid collection and analysis of a wide variety of data, enabling optimal provision and delivery of relief supplies.

[1678] "Seismic intensity data" is data that indicates the strength of shaking when an earthquake occurs.

[1679] "Damage extent data" is data that indicates the extent of the area affected by a disaster.

[1680] The "impact score" is a quantitative score that indicates the impact of a disaster, calculated based on factors such as seismic intensity, the extent of damage, and the number of victims.

[1681] "Satellite imagery" refers to images of the Earth's surface taken by an artificial satellite located in space.

[1682] "Unmanned aerial vehicle images" are images taken from the sky by unmanned aerial vehicles such as drones.

[1683] A "geographic information system" is a system for collecting, displaying, and analyzing geographic information.

[1684] "Population density data" is data that indicates the number of people per unit area within a specific region.

[1685] "Demographic data" refers to data that shows the distribution of age groups, gender, and other demographic attributes in a certain region or group.

[1686] "Estimated information" refers to information such as the age group, gender, and health status of victims estimated based on the data obtained.

[1687] "Relief supplies" are essential items such as food, water, and medical supplies that are supplied to disaster-stricken areas during disasters.

[1688] A "generative AI model" is a model that uses artificial intelligence to perform data analysis and inference.

[1689] A "smartphone application" is a software program that runs on a smartphone and is used to collect data from users.

[1690] "Feedback" is information collected from disaster victims and aid organizations regarding an assessment of current needs and supplies.

[1691] MODE FOR CARRYING OUT THE INVENTION

[1692] This section describes a system for implementing the present invention. This system collects and analyzes a wide variety of data to quickly and appropriately supply relief supplies to disaster-stricken areas in the event of a disaster, and uses that data to optimize the type and quantity of supplies and delivery routes.

[1693] 1. Data acquisition and analysis

[1694] The server obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency, analyzes this data, and calculates an impact score. This impact score is calculated based on indicators such as seismic intensity, damage extent, and number of victims, and the analysis results are stored in a database. For example, if an earthquake with a seismic intensity of 7 occurs, the damage extent is 50 square kilometers, and the number of victims is 20,000, the impact score is calculated as follows:

[1695] Examples:

[1696] Seismic intensity data: 7

[1697] Area of ​​damage: 50 square kilometers

[1698] Number of victims: 20,000

[1699] Impact score: Seismic intensity x 1.5 + damage area x 0.3 + number of victims x 0.2 = impact score 9.5

[1700] 2. Image analysis and delivery route optimization

[1701] The server acquires satellite and drone imagery and performs image analysis to identify road conditions and obstacles in the affected area, then uses a geographic information system (GIS) to calculate optimal delivery routes and identify routes that avoid blocked roads.

[1702] Examples:

[1703] Satellite image: Wide range of the affected area

[1704] Unmanned aerial vehicle imagery: detailed road conditions

[1705] Delivery route optimization: When a main road is blocked, a detour route is calculated to determine the shortest delivery route.

[1706] 3. Population data analysis and supply decisions

[1707] The server obtains population density and demographic data from government and statistical agencies, and uses this information to estimate the age, gender, and health status of disaster victims. This information is then used to determine the type and quantity of relief supplies needed.

[1708] Examples:

[1709] Population density: 1,000 people per square kilometer

[1710] Demographics: 30% elderly, 20% children

[1711] Relief supply determination: Identify supplies of medicine for the elderly, food for children, and water.

[1712] 4. Identifying and notifying cooperating regions and organizations

[1713] The server identifies regions and organizations that can cooperate based on the type and quantity of relief supplies received and the need for them, and notifies them. At this time, notifications are sent to quickly begin supplying supplies.

[1714] Examples:

[1715] Medicine supplies from nearby cities

[1716] Delivery support by logistics companies

[1717] 5. Gather feedback and update supply plans

[1718] The user provides a smartphone application that disaster victims and relief organizations can access, allowing them to input their needs and urgent requests for supplies. The feedback is analyzed by the server and reflected in the current supply plan.

[1719] Examples:

[1720] Using a web form or smartphone app, disaster victims can input the supplies they need.

[1721] Feedback: Request for fresh water, food, and blankets

[1722] Updated supply plan based on feedback: Increased fresh water supply and added more medical supplies

[1723] 6. Use of generative AI models

[1724] The server uses a generative AI model to optimize delivery routes and generate estimated information. By inputting prompt statements to the AI ​​model, efficient analysis becomes possible.

[1725] Examples:

[1726] Disaster data: intensity: 7, affected_area: 50, affected_population: 20000

[1727] What is the best delivery route?

[1728] Also, what types and amounts of food do you need?

[1729] In this way, the system of the present invention can quickly collect and analyze a wide variety of data to provide and deliver optimal relief supplies, thereby significantly improving support for disaster victims.

[1730] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1731] Step 1:

[1732] The server receives real-time seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. Based on the input seismic intensity data and damage extent data, the server records them in a database. During this process, the latest information is collected using APIs and data feeds.

[1733] Step 2:

[1734] The server analyzes the acquired seismic intensity data and damage extent data and calculates an impact score. This weights each data point and derives an impact score. Specifically, the impact score is calculated by multiplying the seismic intensity by 1.5, the damage extent by 0.3, and the number of victims by 0.2.

[1735] Input: Seismic intensity data, damage area data, number of victims

[1736] Output: Impact score

[1737] Step 3:

[1738] The server stores the calculated impact score and the analysis results in a database. The impact score is important data to be used in the future analysis process.

[1739] Step 4:

[1740] The server acquires various satellite and unmanned aerial vehicle images, analyzes the image data, and uses machine learning algorithms to identify road conditions and obstacle locations throughout the disaster area.

[1741] Input: Satellite images, unmanned aerial vehicle images

[1742] Output: Road conditions in the affected area, location of obstacles

[1743] Step 5:

[1744] The server uses a geographic information system (GIS) to calculate the optimal delivery route, identifying routes that avoid road blocks and obstacles, and stores the optimized delivery route in a database.

[1745] Input: Road conditions, obstacle locations

[1746] Output: Optimized delivery route

[1747] Step 6:

[1748] The server obtains population density and demographic data from government and statistical agencies, analyzes it, estimates the age, gender, and health status of disaster victims, and uses this information to determine the type and amount of relief supplies needed.

[1749] Input: population density data, population composition data

[1750] Output: Type and quantity of relief supplies

[1751] Step 7:

[1752] The server identifies and notifies regions and organizations that can cooperate based on the type and amount of relief supplies, implementing protocols to quickly contact nearby cities and logistics companies.

[1753] Input: Type and quantity of relief supplies

[1754] Output: Notification to cooperating regions and organizations

[1755] Step 8:

[1756] The device uses a smartphone application to collect feedback from disaster victims and relief organizations, through which disaster victims input their needs and emergency requests.

[1757] Input: Feedback from victims and relief organizations

[1758] Output: Collected feedback data

[1759] Step 9:

[1760] The server analyzes the collected feedback and updates the supply plan, reevaluating the types and quantities of supplies to be supplied and adjusting them as necessary.

[1761] Input: Feedback data

[1762] Output: Updated supply plan

[1763] Step 10:

[1764] The server uses the generative AI model to optimize delivery routes and generate estimated information. It inputs prompts to the AI ​​model to achieve efficient analysis.

[1765] Input: prompt statement, real-time data

[1766] Output: Optimized delivery route, estimated information

[1767] As a concrete example, the following prompt sentence is input to the AI ​​model:

[1768] Disaster data: intensity: 7, affected_area: 50, affected_population: 20000

[1769] What is the best delivery route?

[1770] Also, what types and amounts of food do you need?

[1771] This will enable us to respond effectively and quickly to any disasters that occur.

[1772] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1773] This article describes a system for implementing the present invention. This system collects and analyzes a variety of data, and combines it with an emotion engine that recognizes the user's emotions to optimize the type and quantity of supplies and delivery routes in order to quickly and appropriately provide relief supplies to disaster-stricken areas in the event of a disaster.

[1774] Program processing and specific examples

[1775] Data acquisition and analysis

[1776] 1. The server obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. This data includes the time of the earthquake, epicenter, damage extent, number of victims, etc. For example, it accesses a specified API using an HTTP request to obtain the required data.

[1777] Examples:

[1778] Seismic intensity data: 7

[1779] Area of ​​damage: 50 square kilometers

[1780] Number of victims: 20,000

[1781] 2. The server integrates the acquired seismic intensity data and damage extent data to calculate an impact score. The impact score is calculated by combining the seismic intensity, damage extent, number of victims, etc. For example, the formula used is seismic intensity x 1.5 + damage extent x 0.3 + number of victims x 0.2.

[1782] Examples:

[1783] Seismic intensity x 1.5 + damage area x 0.3 + number of affected people x 0.2 = impact score 9.5

[1784] 3. The server stores the calculated impact score in a database and uses it for subsequent analysis and processing.

[1785] Image analysis and delivery route optimization

[1786] 1. The server acquires satellite and drone images and performs image analysis to identify road conditions and the location of obstacles in the affected area. It uses an image recognition algorithm based on deep learning to detect damaged roads and obstacles.

[1787] Examples:

[1788] Satellite image: Wide range of the affected area

[1789] Drone imagery: detailed road conditions

[1790] 2. The server uses a GIS (geographic information system) to calculate the optimal delivery route, using a shortest path algorithm to identify the most efficient route to evacuation shelters or collection points.

[1791] Examples:

[1792] The main road is closed, so a detour route is calculated to determine the shortest delivery route.

[1793] Population data analysis and supply decisions

[1794] 1. The server retrieves population density and population pyramid data from government or statistical agencies, including the number of people in a particular area and their age distribution. It retrieves the data using an API request.

[1795] Examples:

[1796] Population density: 1,000 people per square kilometer

[1797] Population pyramid: 30% elderly, 20% children

[1798] 2. The server analyzes the population data and estimates the age, gender, and health status of the victims. Based on this information, it determines the type and amount of relief supplies needed.

[1799] Examples:

[1800] Identify supplies of medicine for the elderly, food for children, and water

[1801] Use of emotion engine

[1802] 1. The device provides an accessible communication platform for disaster victims and relief organizations to input their needs and feedback. The platform is equipped with an emotion engine that recognizes the user's emotional state as they input their needs.

[1803] Examples:

[1804] Victims enter the supplies they need through a web form or mobile app, and the emotion engine analyzes the emotions from the text input.

[1805] 2. Users, as disaster victims or relief organizations, input their needed supplies and urgent requests through the platform, and the emotion engine recognizes emotions in real time and collects the data.

[1806] Examples:

[1807] Supplies needed: Fresh water, food, blankets. Emotional state: "Anxious" "Urgent".

[1808] 3. The server analyzes the collected feedback and emotion data in real time and reflects it in the current supply plan. By analyzing the feedback and emotion data together, the urgency and priority of supplies can be determined with even greater accuracy.

[1809] Examples:

[1810] Based on feedback, we've increased the supply of fresh water, added new types of medical supplies, and prioritized the delivery of some supplies based on emotion data.

[1811] 4. The server updates supply plans in real time and addresses the psychological needs of disaster victims, ensuring more accurate distribution of supplies.

[1812] In this way, by using the system according to the present invention, it is possible to quickly collect and analyze various data and the emotional state of the user, and to provide and deliver optimal relief supplies.

[1813] The processing flow will be explained below.

[1814] Step 1:

[1815] Server: Obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. This includes information such as the time of the earthquake, epicenter, damage extent, and number of victims. For example, an HTTP request is used to access a specified API and obtain the required data.

[1816] Step 2:

[1817] Server: The acquired seismic intensity data and damage extent data are integrated to calculate the impact score. The impact score is calculated by combining the seismic intensity, damage extent, number of victims, etc. For example, the formula used is seismic intensity x 1.5 + damage extent x 0.3 + number of victims x 0.2.

[1818] Step 3:

[1819] Server: The calculated impact scores are stored in a database for later analysis and processing.

[1820] Step 4:

[1821] Server: Acquires satellite and drone imagery. This includes retrieving data from imagery providers and drone operators. For example, downloading image data from an AWS S3 bucket.

[1822] Step 5:

[1823] Server: Analyzes acquired satellite and drone images to identify road conditions and transportation options in the affected areas. Utilizes deep learning-based image recognition algorithms to detect damaged roads and obstacles.

[1824] Step 6:

[1825] Server: Based on the analysis results, the GIS system is used to calculate the optimal delivery route. A shortest path algorithm is used to identify the most efficient route to evacuation shelters and collection points.

[1826] Step 7:

[1827] Server: Stores the generated delivery route information in a database, allowing logistics planning to proceed efficiently.

[1828] Step 8:

[1829] Server: Obtain population density and population pyramid data from governments and statistical agencies. This includes the number of people in a particular area and their age distribution. Obtain the data using API requests.

[1830] Step 9:

[1831] Server: Analyzes acquired population data to estimate the age, gender, and health status of disaster victims. Performs statistical analysis to determine the type and amount of relief supplies based on specific needs.

[1832] Step 10:

[1833] Server: Based on the estimated number of victims, calculates the type and amount of relief supplies needed. Selects supplies according to specific needs, such as medicines for the elderly or nutritional foods for children.

[1834] Step 11:

[1835] Server: Searches the database for regions and companies that can cooperate, and identifies the most suitable partner based on the type and quantity of supplies and speed of supply.

[1836] Step 12:

[1837] Server: Notify the identified collaborators. This is done using an automatic email sending system or emergency contact tool.

[1838] Step 13:

[1839] Server: Receives responses from partners and reflects them in the supply plan. This updates the supply status of materials in real time.

[1840] Step 14:

[1841] Terminal: Provides an accessible communication platform for disaster victims and relief organizations to input their needs and feedback. The platform is equipped with an emotion engine that recognizes the user's emotional state as they input.

[1842] Step 15:

[1843] Users: Victims and relief organizations input their needs and urgent requests through the platform. The emotion engine recognizes emotions in real time and collects the data.

[1844] Step 16:

[1845] Server: Analyzes collected feedback and sentiment data in real time and reflects it in the current supply plan. By analyzing feedback and sentiment data together, the urgency and priority of supplies can be determined with even greater accuracy.

[1846] Step 17:

[1847] Server: Updates supply plans in real time and addresses the psychological needs of disaster victims, ensuring more accurate distribution of supplies.

[1848] Example 2

[1849] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1850] Conventional disaster relief systems have struggled to collect and analyze diverse data quickly and accurately, and efficiently provide and deliver relief supplies. Furthermore, they lack the ability to incorporate feedback from disaster victims and relief organizations, making it difficult to formulate supply plans that respond to actual needs. Furthermore, supply plans do not take into account the psychological state of disaster victims, making it difficult to improve disaster satisfaction.

[1851] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1852] In this invention, the server includes means for acquiring seismic intensity data and damage extent data, means for analyzing the acquired seismic intensity data and damage extent data to calculate an impact score, means for saving the analysis results in a database, means for acquiring satellite images and aerial images, means for analyzing the acquired images to identify road conditions and the locations of obstacles in the disaster area, means for calculating an optimal delivery route using an optimal route algorithm and saving the results in a database, means for acquiring population density and population composition data, means for generating estimated information on disaster victims based on the acquired data, means for determining the type and quantity of relief supplies, means for identifying and notifying regions and organizations that can cooperate, means for collecting feedback from disaster victims and relief organizations, means for analyzing the collected feedback and updating the supply plan, means for recognizing emotional states from user input and collecting that data, and means for analyzing the emotional data to determine the urgency and priority of supplies and reflecting the results in the supply plan.

[1853] This will enable the rapid collection of diverse data, highly accurate analysis, and optimization of relief supply taking into account user emotions.

[1854] "Seismic intensity data" is a numerical value that indicates the strength of earthquake shaking and quantitatively represents the impact of an earthquake.

[1855] "Damage extent data" means data that indicates the extent of damage in a disaster-affected region and identifies the geographic area affected.

[1856] The "impact score" is a numerical value calculated by combining multiple data elements, such as seismic intensity data, damage extent data, and number of victims, and is used to comprehensively evaluate the impact of a disaster.

[1857] "Satellite imagery" means an image of a geographic area taken from a satellite that has a wide field of view and shows the ground in detail.

[1858] "Airborne imagery" means images of a geographic area taken from a drone or other aircraft that provide a detailed view from a low altitude.

[1859] An "optimal route algorithm" is an algorithm that calculates the shortest distance to a destination or the most efficient route, and is used to optimize delivery routes.

[1860] "Population density data" is data that indicates the density of people living in a particular area and is used to understand the population distribution of the area.

[1861] "Demographic data" refers to data that shows the population distribution of age, gender, etc. in a specific area, and is used to understand the characteristics of disaster victims.

[1862] "Estimated information on victims" is information used to estimate the age group, gender, health status, etc. of victims based on acquired population data, etc., and to assess the need for relief.

[1863] "Relief supplies" are items needed when a disaster occurs, such as food, medicine, clothing, and water, to support the lives of disaster victims.

[1864] "Regions and organizations that can cooperate" refers to regions and cooperative organizations that can support relief efforts in the event of a disaster, including the provision of relief supplies and human support.

[1865] "Feedback" refers to information collected from disaster victims and relief organizations, and indicates the needs for supplies, the situation on the ground, and the level of urgency.

[1866] "Emotional state" indicates the emotional state the user is feeling, and recognizes emotions such as anxiety, urgency, and relief in real time.

[1867] The system of the present invention collects and analyzes a variety of data, and further combines it with an emotion engine that recognizes the user's emotions to optimize the type and quantity of supplies and delivery routes in order to quickly and appropriately supply relief supplies to disaster-stricken areas in the event of a disaster. Specific embodiments of the system are described below.

[1868] Hardware and Software Configuration

[1869] This system uses the following hardware and software:

[1870] Server: A computer system with high-performance computing power that collects, analyzes, stores, and calculates routes.

[1871] Terminal: A device that provides a web browser and mobile apps for use by disaster victims and relief organizations.

[1872] Emotion engine: A software module for recognizing the emotional state from user input, such as a sentiment analysis tool that uses natural language processing (NLP) techniques.

[1873] GIS (Geographic Information System): A tool that handles geographic data and calculates optimal delivery routes. Examples include QGIS and ArcGIS.

[1874] Image recognition algorithm: A deep learning algorithm used to analyze satellite and aerial images. TensorFlow and PyTorch are used.

[1875] Data collection and analysis

[1876] 1. The server obtains seismic intensity data and damage extent data from disaster information providers such as the Japan Meteorological Agency. This data includes the time of the earthquake, epicenter, damage extent, and number of victims. As a specific example, it accesses the API using an HTTP request and obtains a response in JSON format.

[1877] For example, send a request like GET / earthquake / data.

[1878] 2. The server integrates the acquired seismic intensity data and damage extent data to calculate the impact score. The impact score is calculated using the formula: seismic intensity x 1.5 + damage extent x 0.3 + number of victims x 0.2.

[1879] Example: If the earthquake has a magnitude of 7, the damage area is 50 square kilometers, and the number of affected people is 20,000, the impact score is 7 x 1.5 + 50 x 0.3 + 20,000 x 0.2 = 9.5.

[1880] Image analysis and delivery route optimization

[1881] 1. The server acquires satellite and aerial imagery and analyzes them using an image recognition algorithm based on deep learning, thereby identifying road damage and the location of obstacles.

[1882] Example: Access the GET / satellite_images and GET / drone_images endpoints to retrieve and analyze image data.

[1883] 2. The server uses GIS to calculate the optimal delivery route, using a shortest path algorithm (e.g., Dijkstra algorithm) to identify the most efficient route to a shelter or collection point.

[1884] Example: If a major road is blocked, calculate a detour route to create the shortest route.

[1885] Population data analysis and material decisions

[1886] 1. The server obtains population density and demographic data from government and statistical agencies, including the number of people in a particular area and their age distribution.

[1887] Example: Access the GET / population_data endpoint to retrieve data.

[1888] 2. The server analyzes the acquired population data to estimate the age, gender, and health status of the victims. Based on this information, it determines the type and amount of relief supplies needed.

[1889] Examples: Identifying supplies of medicine for the elderly, food for children, and water.

[1890] Use of emotion engine

[1891] 1. The device provides a communication platform for disaster victims and relief organizations, allowing them to input their needs and feedback. It also has a built-in emotion engine that recognizes the user's emotional state from their input.

[1892] Example: When a disaster victim enters "I need fresh water. I'm anxious" through a web form or mobile app, the emotion engine recognizes the emotion "anxiety."

[1893] 2. The server analyzes the collected feedback and emotion data in real time and reflects it in the supply plan, thereby determining t...

Claims

1. A means for acquiring seismic intensity data and damage extent data; A means for analyzing the acquired seismic intensity data and damage range data and calculating an impact score; a means for storing the analysis results in a database; means for acquiring satellite and drone imagery; A means for analyzing the acquired images and identifying road conditions in the affected areas; A means for calculating and storing optimal delivery routes in a database; means for obtaining population density and population pyramid data; A means for generating estimated information on disaster victims based on the acquired data; A means for determining the type and quantity of relief supplies; A means of identifying and informing potential regions and companies; A means of gathering feedback from disaster victims and relief organizations; A system that includes a means to analyze the collected feedback and update the supply plan.

2. The system of claim 1 , wherein satellite and drone images are analyzed to identify road conditions in disaster areas.

3. The system according to claim 1, which estimates the age group and health condition of disaster victims and determines the type and amount of relief supplies required.

Images