System

Abstract

A system is provided.SOLUTION: Means for collecting big data and satellite data related to disasters such as earthquakes, tsunamis, and heavy rains, means for analyzing the collected data and calculating a disaster risk of an area where the user lives, means for analyzing stockpile information input by the user and specifying a stockpile that is in short supply based on the disaster risk, means for notifying the user of the stockpile that is in short supply and a necessary countermeasure, means for providing a link for purchasing the stockpile that is in short supply and a request form to a specialist, and means for periodically checking a new countermeasure according to a best-before date of the stockpile and a season. Means for notifying a user.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] Currently, disaster risks in the area where a user lives are visualized to a certain extent using hazard maps and other methods, but individuals must research and consider for themselves "what specific measures should be taken" and "what preparations should be made for." As a result, many people feel that disaster prevention is a hurdle, and as a result, major disasters occur without sufficient stockpiles and countermeasures, which can increase the burden on governments. To solve this problem, a system is needed that automatically suggests specific measures and stockpiles for disaster risks, allowing users to easily implement disaster prevention measures. [Means for solving the problem]

[0005] The present invention solves the above problems by providing a system including the following means.

[0006] First, we have established a means to collect big data and satellite data related to disasters such as earthquakes, tsunamis, and heavy rains, which will enable us to obtain the latest disaster risk information.

[0007] Next, the system analyzes the collected data and provides a means to calculate the disaster risk in the area where the user lives, allowing for a detailed assessment of the risk in the user's area.

[0008] Furthermore, the system analyzes the stockpile information entered by the user and provides a means to identify shortages of stockpile items based on disaster risk, making it possible to clarify what exactly is lacking.

[0009] We have added a means to notify users of shortages of supplies and necessary measures, as well as a means to provide links to purchase supplies and request forms from specialist suppliers, making it easier for users to arrange for the necessary supplies and services.

[0010] Finally, the system provides a means to periodically check the expiration dates of stockpiled items and new seasonal countermeasures and notify users, allowing users to maintain the latest stockpiles and countermeasures.

[0011] "Data collection means" refers to a device or system for collecting big data and satellite data related to disasters such as earthquakes, tsunamis, and heavy rains.

[0012] "Data analysis means" refers to a device or algorithm that calculates and assesses the disaster risk in the area where the user lives based on collected disaster data.

[0013] The "stockpile information input means" is an interface or device for inputting information about stockpiles currently held by the user.

[0014] The "stockpile information analysis means" is a device or algorithm that analyzes stockpile information entered by the user and identifies stockpile items that are in short supply based on disaster risk.

[0015] "Notification means" refers to a device or system that notifies users of shortages of stockpiles and necessary measures.

[0016] The "purchase link providing means" is a device or system that provides a link for purchasing shortage stockpiles or a request form to a specialist supplier.

[0017] The "regular check means" is a device or algorithm that periodically checks the expiration dates of stockpiled items and new seasonal countermeasures and notifies the user. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] The present invention relates to a disaster risk analysis and countermeasure proposal system. This system operates in cooperation with a server, terminals, and users to analyze the disaster risk in the area where the user lives and propose appropriate countermeasures and emergency supplies.

[0040] System Operation Overview

[0041] Data collection methods

[0042] server

[0043] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rains, etc. This data is used as basic data for disaster risk analysis. The data is obtained from reliable data sources via the Internet and stored in a database.

[0044] Data Analysis Methods

[0045] server

[0046] The server analyzes the collected data and calculates the disaster risk for the area the user lives in. The analysis is performed using existing disaster models and machine learning algorithms. For example, it predicts the risk level for the area based on data from past earthquakes and tsunamis.

[0047] Stockpile information input method

[0048] Terminal

[0049] The device provides an interface for users to input information about emergency supplies stored at home. Users can enter this information using a smartphone app or a web app. Input items include the type and quantity of emergency supplies, as well as their expiration date.

[0050] Stockpile information analysis method

[0051] server

[0052] The server identifies shortages based on the stockpile information entered by the user, and analyses it against disaster risk assessments to determine whether stockpiles are sufficient for high-risk disasters.

[0053] Notification means

[0054] server

[0055] The server notifies users of shortages of supplies and necessary measures via push notifications or email. For example, it may send a specific message such as, "Your water supply is low."

[0056] Terminal

[0057] The device receives notifications from the server and displays them to the user. When a notification is received, the user is notified via a pop-up or banner.

[0058] Purchase link provision method

[0059] Terminal

[0060] The device provides links to purchase supplies that are in short supply and a request form to specialists, allowing users to easily purchase needed supplies or request assistance from specialists within the app.

[0061] User

[0062] Users can view notifications and click links to purchase supplies they need, such as ordering water or food directly from within the app.

[0063] Regular check method

[0064] server

[0065] The server periodically checks expiration dates of stockpiled items and measures to address new seasonal risks, such as heatstroke prevention measures in summer and cold weather measures in winter.

[0066] Terminal

[0067] The device receives periodic notifications sent from the server and displays them to the user, allowing the user to periodically update their stockpiles and take new measures.

[0068] Specific example of system operation

[0069] Risk notification after entering address

[0070] 1. User: Launches the app and enters their address.

[0071] 2. Terminal: Sends address information to the server.

[0072] 3. Server: Analyzes the disaster risk in the area based on the address information and sends the results to the device.

[0073] 4. Device: Notify the user that "There is a high risk of tsunami in your area."

[0074] Check for shortages of emergency supplies and purchase them

[0075] 1. User: Enters current stockpile list into the app.

[0076] 2. Terminal: Sends stockpile information to the server.

[0077] 3. Server: Identify shortages of supplies based on disaster risk information.

[0078] 4. On the device: Notify the user that their water supply is low and provide a link to purchase water.

[0079] 5. User: Clicks on the link to purchase the scarce water.

[0080] In this way, the present invention provides a system that combines multiple means to support users in taking effective disaster countermeasures, allowing users to take appropriate disaster countermeasures themselves and reducing the burden on government agencies.

[0081] The processing flow will be explained below.

[0082] Step 1: Data collection

[0083] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rain, etc. from reliable data sources. This data is stored in a database as basic data for disaster risk analysis.

[0084] Step 2: Enter user information

[0085] Users launch the app and enter initial information such as their address, family composition, and current list of emergency supplies.

[0086] The terminal receives the information entered by the user, checks the format, and then sends it to the server.

[0087] Step 3: Risk analysis

[0088] The server calculates the disaster risk for the area based on the address information sent by the user, analyzes collected big data and satellite data, and evaluates the risk level.

[0089] Step 4: Risk notification

[0090] Based on the results of the risk analysis, the server generates risk information customized for each user and transmits this information to the terminal.

[0091] The device will notify the user of the received risk information via pop-ups or notification banners.

[0092] Step 5: Gather information on emergency supplies

[0093] Users use a smartphone app or web app to enter information about the emergency supplies they have at home.

[0094] The terminal receives the stockpile information and transmits it to the server.

[0095] Step 6: Stockpile information analysis

[0096] The server evaluates what is lacking based on the stockpile information entered by the user, compares it with disaster risk, and generates a list of stockpiles that are lacking.

[0097] Step 7: Stockpile shortage notification

[0098] The server generates data to notify users about shortages of stockpiles and necessary measures, and sends this data to the terminal.

[0099] The device will then display the received notification to the user, for example, a message saying "Water stocks are running low."

[0100] Step 8: Provide a purchase link

[0101] The device displays links to purchase supplies that are in short supply and a request form to contact a specialist supplier.

[0102] Users can click on the link to purchase the supplies they need.

[0103] Step 9: Regular checks

[0104] The server regularly checks the expiration dates of stockpiled goods and measures to be taken in response to new seasonal risks.

[0105] If the user needs new measures, the server generates the information and sends it to the terminal.

[0106] Step 10: Periodic Notifications

[0107] The terminal receives the periodic notifications sent from the server and displays them to the user, who can then check the notifications and take the necessary action.

[0108] In this way, the system supports users in taking effective disaster prevention measures through each step.

[0109] Example 1

[0110] 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."

[0111] In disaster risk analysis and countermeasure proposals, there is a lack of systems that can accurately assess the specific risks in the area where a user lives and identify shortages of stockpiles based on that assessment.In addition, there is a lack of notifications and purchasing links that allow users to take effective disaster countermeasures, making it difficult to respond quickly and appropriately.

[0112] 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.

[0113] In this invention, the server includes means for collecting large-scale data and satellite data related to disasters such as earthquakes, tsunamis, and heavy rains, means for analyzing the collected data and calculating the disaster risk in the area where the user lives, and means for collecting emergency stockpile information entered by the user. This makes it possible to evaluate the specific disaster risk in the area where the user lives, identify emergency stockpile shortages based on the evaluation, and provide the user with prompt and appropriate countermeasures.

[0114] A "disaster" is an event that includes adverse effects caused by natural phenomena such as earthquakes, tsunamis, and heavy rains.

[0115] "Big data" refers to a vast amount of information, also known as big data, and includes data related to natural disasters and satellite image data.

[0116] "Satellite data" refers to observation data obtained from artificial satellites, including meteorological and topographical information.

[0117] "Analysis" refers to the process of organizing, calculating, and evaluating collected data to arrive at a specific conclusion.

[0118] "Disaster risk" refers to an assessment value that indicates the possibility of a disaster occurring in a particular area and the extent of damage caused by that disaster.

[0119] "Stockpiles" refer to supplies stored in advance in preparation for emergencies such as disasters. These include food, water, medicine, etc.

[0120] "Notification" means a message or alert intended to inform the User of important information.

[0121] "Purchase Link" means a direct link to an online shopping site provided to enable Users to easily purchase shortage supplies.

[0122] "Vendor Request Form" means an electronic form for entering and submitting a request to a vendor for a particular service or supply.

[0123] "Expiration date" refers to the date by which stockpiled items are recommended for use, after which their quality may deteriorate.

[0124] "Regularly" means repeated at regular intervals, including monthly, seasonal, and other cycles.

[0125] "Residence information" refers to information about the area and address where the user lives.

[0126] MODE FOR CARRYING OUT THE INVENTION

[0127] The present invention relates to a system for disaster risk analysis and countermeasure proposals. This system operates in cooperation with a server, terminals, and users to analyze the disaster risk in the area where the user resides and propose appropriate countermeasures and stockpiles based on the analysis. This specification describes a specific implementation method of the system.

[0128] Hardware and software used

[0129] server

[0130] The server is the central component that collects and analyzes large-scale data related to earthquakes, tsunamis, heavy rain, and other events, as well as satellite data. Data is collected from reliable data sources via the Internet. Specifically, data is obtained from APIs such as those of the Japan Meteorological Agency and NASA, and the data is stored in a MySQL database. Machine learning algorithms using Python's Scikit-Learn library are used for data analysis.

[0131] Terminal

[0132] The terminal provides an interface for users to input emergency stockpile information and receive notifications. The smartphone app was created with React Native and features a user-friendly input form and notification function. The terminal also receives data sent from the server and displays alerts to the user.

[0133] User

[0134] Users use the app to enter their information, including their address and a list of supplies, and view notifications and suggestions from the system.

[0135] System Operation Overview

[0136] Data collection and analysis

[0137] server

[0138] The server periodically collects large-scale data on natural disasters and satellite data, storing it in a MySQL database. The data is analyzed using machine learning algorithms using Python's Scikit-Learn to calculate disaster risk for each region. This analysis then predicts risk levels, such as the probability of earthquakes and tsunamis occurring.

[0139] Management of emergency stockpile information

[0140] Terminal

[0141] The terminal provides a form for users to enter stockpile information, which is then sent over the Internet to a server. The data is encrypted using the HTTPS protocol, ensuring secure transmission.

[0142] server

[0143] The server stores the received stockpile information in a database, compares it with disaster risk information, and identifies shortages of stockpile items. From the analysis results, specific information such as "water stocks are insufficient" is generated.

[0144] User Notifications and Actions

[0145] server

[0146] The server then sends notifications to users based on the analysis results, including messages such as "Tsunami risk in your area is increasing. Water reserves are running low," and these notifications are sent to devices via a push notification API.

[0147] Terminal

[0148] The device receives notifications sent from the server and displays them to the user, often as popups or banners within the app, allowing the user to take any necessary action immediately.

[0149] User

[0150] Users can click on the purchase link in the notification to purchase the supplies they need online, for example, by going directly to an e-commerce site such as Amazon or Rakuten Ichiba and ordering the supplies they need.

[0151] Regular checks and update notifications

[0152] server

[0153] The server periodically checks the expiration dates and seasonal risks of stockpiled items. The results of the check are notified to the user. For example, the server may notify the user that "The expiration date of the stockpiled food is approaching. Please purchase new stockpiles."

[0154] Terminal

[0155] The terminal receives periodic update notifications sent from the server and displays them to the user, allowing the user to periodically update their stockpiles and take new measures.

[0156] Prompt Sentence Examples

[0157] 1. "I would like to know the disaster risk in the area where I live. I live in Shibuya Ward, Tokyo."

[0158] 2. "I just typed in the list of food items I have stored at home. Is this list sufficient for disaster preparedness?"

[0159] 3. "Please let me know if there is anything missing from our summer stockpile."

[0160] 4. "I'd like to purchase water within the app. Can you provide a link?"

[0161] The above is an embodiment of the present invention. This system allows users to take effective disaster countermeasures and reduces the burden on government agencies.

[0162] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0163] Step 1: Data collection

[0164] server

[0165] The server collects large-scale disaster-related data and satellite data at a specified time every day. The data is acquired using APIs from, for example, the Japan Meteorological Agency or NASA. The API endpoint and authentication information of each data source are used as input, and the acquired data is stored in the server's MySQL database as output. Specifically, the data acquisition script is executed periodically using the Python requests library.

[0166] Step 2: Data analysis

[0167] server

[0168] The server analyzes the acquired dataset and calculates the disaster risk for each region. The data collected in step 1 is used as input, and the output is an assessment value indicating the risk level for each region. Specifically, a machine learning algorithm using Python's Scikit-Learn library processes the data and performs a risk assessment. The analysis results are then stored in a MySQL database.

[0169] Step 3: Enter emergency supply information

[0170] Terminal

[0171] The terminal provides an interface for users to input emergency supply information. Input includes the type, quantity, and expiration date of emergency supplies, which the user enters through the app. This data is sent to the server as output. Specifically, a form created with React Native is displayed, and the information entered by the user is encrypted via HTTPS and sent to the server.

[0172] Step 4: Sending and receiving stockpile information

[0173] Terminal

[0174] The terminal sends the stockpile information entered by the user to the server in real time. The input includes the data entered by the user in the form, and this data is sent to the server as output. Specifically, the user enters stockpile information and clicks the "Send" button, and the data reaches the server.

[0175] server

[0176] The server receives the stockpile information sent from the terminal and stores it in a database. The input includes the stockpile information sent, and the output is information in a format that can be stored in the database. Specifically, it analyzes the received information and stores the data in the appropriate table.

[0177] Step 5: Analyze stockpile information

[0178] server

[0179] The server analyzes the received stockpile information and compares it with disaster risk information to identify shortages. Input includes stored stockpile information and the local risk level. The output generates a list of shortages. Specifically, a Python script retrieves the necessary information from the database and uses an analysis algorithm to identify shortages.

[0180] Step 6: Create and send notifications

[0181] server

[0182] The server creates and sends notifications based on shortages of stockpiles and disaster risks. The input includes the analysis results of stockpile information, and the output is a specific notification message. Specifically, the notification generation script creates the notification content based on the analysis results and sends it to the device via the push notification API.

[0183] Terminal

[0184] The device receives notifications from the server and displays them to the user. The input includes the notification message sent from the server, and the output is the notification displayed on the device screen. Specifically, the notification is displayed as a popup or banner within the app.

[0185] Step 7: Provide a purchase link and take action

[0186] Terminal

[0187] The device displays links to purchase supplies and a request form to specialist vendors for supplies that are in short supply. The input includes notification content from the server, and the output is a purchase link that is displayed to the user. Specifically, the link is displayed on a screen within the app, and the user can click it.

[0188] User

[0189] The user clicks on the purchase link in the notification to purchase the missing supplies. The input includes the displayed purchase link, and the output is access to an e-commerce site, etc. The specific operation is that the user clicks the link, a browser opens, and the purchase page is displayed.

[0190] Step 8: Regular checks and update notifications

[0191] server

[0192] The server periodically checks the expiration dates and seasonal risks of stockpiled items and notifies users of the results. The input includes stockpile information and time information in the database, and the output is an update notification message. Specifically, a periodic script scans the database and generates and sends the necessary notifications.

[0193] Terminal

[0194] The device receives periodic update notifications from the server and displays them to the user. The input includes the update notification message from the server, and the output is the update notification displayed on the device screen. Specifically, the notification is displayed as a pop-up or banner within the app, allowing the user to check it periodically.

[0195] In this way, by clarifying the input and output at each step and describing the specific operations, the operation of the entire system and its specific processing procedures become clear.

[0196] (Application example 1)

[0197] 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."

[0198] In modern society, large-scale natural disasters occur frequently, making it important for individuals and households to prepare appropriate emergency supplies. However, it is not easy for individuals to assess disaster risk and manage the necessary supplies themselves. In addition, there is a lack of systems for properly managing emergency food and other supplies needed in the event of a disaster, and for quickly replenishing supplies when shortages occur. This can result in insufficient supplies in emergencies, significantly impacting the lives of disaster victims. Furthermore, conventional systems do not integrate disaster risk assessment and emergency supply management, making it difficult to implement efficient countermeasures.

[0199] 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.

[0200] In this invention, the server includes means for collecting big data and satellite data related to disasters such as earthquakes, tsunamis, and heavy rains; means for analyzing the collected data and calculating the disaster risk in the user's area; and means for analyzing stockpile information entered by the user and identifying shortages of stockpile items based on the disaster risk. This enables the server to analyze the disaster risk in the user's area, manage the emergency food stockpile status in the event of a disaster, suggest emergency food items to the user, and enable easy ordering. Furthermore, by allowing the user to enter address information and receive confirmation of shortages of emergency food and other items according to the disaster risk and a link to purchase them, the server allows the user to quickly and reliably prepare stockpiles.

[0201] "Big data" refers to a large amount of data in a variety of formats, and is data that is so large that it is difficult to process using conventional data processing applications.

[0202] "Satellite data" refers to information obtained from Earth observation satellites and satellite images, and is data used to understand changes in terrain and climate.

[0203] "Disaster risk" is an index that evaluates the likelihood of natural disasters such as earthquakes, tsunamis, and heavy rains occurring and their impact.

[0204] "Stocked goods information" refers to information such as the type, quantity, and expiration date of disaster supplies that the user keeps in their own home or facility.

[0205] "Emergency food" refers to food that can be consumed immediately in the event of a disaster, can be stored for a long period of time, and is pre-cooked or can be eaten with simple cooking procedures.

[0206] A "notification" is the act of conveying warnings or information to a user, such as a message sent via push notification or email.

[0207] A "purchase link" is a hyperlink to a web page provided for users to quickly purchase the missing supplies.

[0208] The "specialist vendor request form" is an input form for a user to request the necessary goods or services from a specialist vendor.

[0209] The "best before" date is the date by which the quality of food is guaranteed, and after this date the taste and quality may deteriorate.

[0210] "Regular checks" are a process of checking the status of stockpiled goods and any new risks at regular intervals, and notifying users as necessary.

[0211] "Management of emergency food stockpiles in the event of a disaster" is the process of determining the types and quantities of emergency food that users possess and checking to see if there are any shortages.

[0212] "Easy ordering means" is a function that allows users to quickly and easily purchase shortages of stockpiled items through the application.

[0213] This invention relates to a system for disaster risk analysis and countermeasure proposals. This system, which operates in cooperation with a server, terminals, and users, analyzes the disaster risk in the user's residential area and proposes appropriate countermeasures and emergency supplies. It also has a function that allows users to input address information and emergency supply information, and provides a confirmation of shortages of emergency supplies and a link to purchase them.

[0214] Data collection methods

[0215] server

[0216] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rain, etc. This data is used as basic data for disaster risk analysis. The data is obtained from reliable data sources via the Internet and stored in a database. For example, data is collected from public disaster information APIs and satellite data provision services.

[0217] Data Analysis Methods

[0218] server

[0219] The server analyzes the collected data and calculates the disaster risk in the area where the user lives. The analysis uses existing disaster models and machine learning algorithms. For example, it predicts the risk level of the area based on data from past earthquakes and tsunamis, and notifies the user that "there is a high risk of tsunami in your area."

[0220] Stockpile information input method

[0221] Terminal

[0222] Users use a smartphone app or web app to input information about their home emergency supplies, including water, emergency food, batteries, etc., and are provided with an interface for entering information such as the type, quantity, and expiration date of each item.

[0223] Stockpile information analysis method

[0224] server

[0225] The server identifies shortages based on the stockpile information entered by the user. Analysis is performed against disaster risk assessments to confirm whether stockpiles are sufficient for high-risk disasters. For example, in areas with a high risk of tsunamis, the server will notify users that "water stockpiles are insufficient."

[0226] Notification means

[0227] Server and terminal

[0228] The server notifies users of shortages of supplies and necessary measures via push notifications and emails. Devices receive notifications from the server and have the ability to notify users via pop-ups and banners.

[0229] Purchase link provision method

[0230] Terminal

[0231] The device provides links to purchase supplies that are in short supply and a request form to specialists, allowing users to easily purchase the supplies they need or request assistance from specialists within the app.

[0232] Regular check method

[0233] Server and terminal

[0234] The server periodically checks expiration dates of stockpiled items and measures to respond to new seasonal risks, and notifies the user. The terminal has the function to receive periodic notifications sent from the server and display them to the user.

[0235] Example of a system

[0236] This system uses Python to write data analysis scripts and collects data from external APIs using the HTTP request module (requests library). It also uses the smtplib library to send emails. The system analyzes disaster risks in the user's area, identifies shortages of emergency supplies based on the results, and sends notifications, allowing users to take appropriate measures quickly.

[0237] Specific example prompt

[0238] Below are some example prompts to use when inputting a generative AI model:

[0239] You are the developer of a food delivery app. You want to incorporate a disaster risk analysis and countermeasure proposal system into this app. Design a disaster prevention function that works on the smartphone app based on the following specifications:

[0240] 1. Analyze disaster risk based on address information entered by the user.

[0241] 2. Analyze the stockpile information entered by the user and identify any shortages.

[0242] 3. Notify users via push notification or email about shortages and necessary precautions.

[0243] 4. Include a function to provide links to purchase supplies that are in short supply.

[0244] The output should include a detailed process flow and a description of the software and hardware used.

[0245] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0246] Step 1:

[0247] Data collection

[0248] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rain, etc. from reliable data sources via the Internet. This data includes information on past disasters and meteorological data. The server uses this data as basic data for assessing disaster risk and stores it in a database. The input is disaster data from an external API, and the output is the data to be analyzed that is stored in the server's database.

[0249] Step 2:

[0250] Data analysis

[0251] The server analyzes the collected data and calculates the disaster risk for the residential area based on the address information entered by the user. Existing disaster models and machine learning algorithms are used here. For example, the risk level for the area is predicted based on data from past earthquakes and tsunamis. The inputs are the collected data and the user's address information, and the output is the disaster risk assessment result for the area where the user lives.

[0252] Step 3:

[0253] Enter emergency stockpile information

[0254] Users use a smartphone app or web app to input information about their home emergency supplies. This includes the type, quantity, and expiration date of items such as water, emergency food, and batteries. The input is done by the user through the app interface. The input data is sent to the server and stored in a database as emergency supply information. The input is the emergency supply information entered by the user, and the output is the emergency supply information stored in the server's database.

[0255] Step 4:

[0256] Stockpile information analysis

[0257] The server identifies what is lacking based on the stockpile information entered by the user. This is done in conjunction with disaster risk assessments to ensure that stockpiles are sufficient for high-risk disasters. For example, in areas with a high risk of tsunamis, it identifies that "water stockpiles are insufficient." The inputs are stockpile information and disaster risk assessment results, and the output is a list of stockpiles that are lacking.

[0258] Step 5:

[0259] notification

[0260] The server notifies the user of the identified shortages and necessary measures. This notification is done via push notification or email. Based on the acquired information on shortages, a specific message is generated and sent to the user. The input is the information on shortages, and the output is the notification message sent to the user.

[0261] Step 6:

[0262] Purchase link provided

[0263] The device provides users with links to purchase supplies they are running low on and a request form to specialist suppliers. Users can easily purchase the supplies they need from within the app, allowing users to quickly and reliably replenish their supplies. The input is a list of supplies they are running low on, and the output is links to purchase the supplies and a request form.

[0264] Step 7:

[0265] Regular checks

[0266] The server periodically checks the expiration dates of stockpiled items and measures to be taken in response to new seasonal risks, and notifies the user. This allows users to continuously maintain appropriate disaster prevention measures. The input is stockpiled item information and seasonal data, and the output is notification messages as a result of the regular checks.

[0267] 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.

[0268] This invention combines an emotion engine with a disaster risk analysis and countermeasure proposal system. This system works in conjunction with a server, terminal, and user to analyze the disaster risk in the user's area and propose appropriate countermeasures and emergency supplies. It also has a function to recognize the user's emotions and adjust the notification content and countermeasure priority.

[0269] System Operation Overview

[0270] Data collection methods

[0271] server

[0272] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rain, etc. from reliable data sources. This data is stored in a database as basic data for disaster risk analysis.

[0273] Data Analysis Methods

[0274] server

[0275] The server analyzes the collected data and calculates the disaster risk for the area the user lives in. The analysis is performed using existing disaster models and machine learning algorithms. For example, it predicts the risk level for the area based on data from past earthquakes and tsunamis.

[0276] Stockpile information input method

[0277] Terminal

[0278] The device provides an interface for users to input information about emergency supplies stored at home. Users can enter this information using a smartphone app or a web app. Input items include the type and quantity of emergency supplies, as well as their expiration date.

[0279] Stockpile information analysis method

[0280] server

[0281] The server identifies shortages based on the stockpile information entered by the user, and analyses it against disaster risk assessments to determine whether stockpiles are sufficient for high-risk disasters.

[0282] Notification means

[0283] server

[0284] The server notifies users of shortages of supplies and necessary measures via push notifications or email. For example, it may send a specific message such as, "Your water supply is low."

[0285] Terminal

[0286] The device receives notifications from the server and displays them to the user. When a notification is received, the user is notified via a pop-up or banner.

[0287] Purchase link provision method

[0288] Terminal

[0289] The device provides links to purchase supplies that are in short supply and a request form to specialists, allowing users to easily purchase needed supplies or request assistance from specialists within the app.

[0290] User

[0291] Users can view notifications and click links to purchase supplies they need, such as ordering water or food directly from within the app.

[0292] Regular check method

[0293] server

[0294] The server periodically checks expiration dates of stockpiled items and measures to address new seasonal risks, such as heatstroke prevention measures in summer and cold weather measures in winter.

[0295] Terminal

[0296] The device receives periodic notifications sent from the server and displays them to the user, allowing the user to periodically update their stockpiles and take new measures.

[0297] emotion recognition means

[0298] Terminal

[0299] The device provides an interface for recognizing emotions from user input and behavior, for example, estimating emotions from the user's input speed and content.

[0300] server

[0301] The server uses an emotion engine to analyze the data sent from the terminal and recognize the user's emotional state.

[0302] Emotion-based notification adjustment

[0303] server

[0304] The server then adjusts the notification content based on the perceived emotion. For example, if the user is feeling anxious, it will suggest a reassuring message or suggest measures.

[0305] Terminal

[0306] The terminal receives the adjusted notification and displays it to the user.

[0307] A method for adjusting countermeasure priorities according to emotions

[0308] server

[0309] The server adjusts the priority of suggested supplies and measures according to the user's emotions. For example, if the user is feeling stressed, it will prioritize suggested measures that require urgent action.

[0310] Terminal

[0311] The device displays the adjusted measures to the user and encourages them to take specific actions.

[0312] Specific example of system operation

[0313] Risk notification after entering address

[0314] 1. User: Launches the app and enters their address.

[0315] 2. Terminal: Sends address information to the server.

[0316] 3. Server: Analyzes the disaster risk in the area based on the address information and sends the results to the device.

[0317] 4. Device: Notify the user that "There is a high risk of tsunami in your area."

[0318] Check for shortages of emergency supplies and purchase them

[0319] 1. User: Enters current stockpile list into the app.

[0320] 2. Terminal: Sends stockpile information to the server.

[0321] 3. Server: Identify shortages of supplies based on disaster risk information.

[0322] 4. On the device: Notify the user that their water supply is low and provide a link to purchase water.

[0323] 5. User: Clicks on the link to purchase the scarce water.

[0324] Emotion recognition and response

[0325] 1. User: Receives notifications of disaster risks and shortages of emergency supplies, and inputs and takes action within the app.

[0326] 2. Terminal: Collects emotional data from the user's input speed, input content, etc.

[0327] 3. Server: Analyzes the collected emotional data and recognizes that the user is feeling anxious.

[0328] 4. Server: Generates a reassuring message based on the user's emotions and sends it to the device.

[0329] 5. Device: Display a message to the user saying, "Here are some measures you need to take to ensure your safety," and suggest specific measures.

[0330] In this way, by combining an emotion engine, the present invention realizes a system that provides disaster countermeasures that better meet user needs. This system allows users to take appropriate measures according to their own emotional state, increasing their sense of security in the event of a disaster.

[0331] The processing flow will be explained below.

[0332] Step 1: Data collection

[0333] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rain, etc. from reliable data sources. This data is stored in a database as basic data for disaster risk analysis.

[0334] Step 2: Enter user information

[0335] Users launch the app and enter initial information such as their address, family composition, and current list of emergency supplies.

[0336] The terminal receives the information entered by the user, checks the format, and then sends it to the server.

[0337] Step 3: Risk analysis

[0338] The server calculates the disaster risk for the area based on the address information sent by the user, and evaluates the risk level using collected big data and satellite data.

[0339] Step 4: Risk notification

[0340] The server creates the results of the risk analysis as risk information customized for each user and sends it to the terminal.

[0341] The device will notify the user of the received risk information via pop-ups or notification banners.

[0342] Step 5: Gather information on emergency supplies

[0343] Users use a smartphone app or web app to enter information about their home emergency supplies.

[0344] The terminal receives the stockpile information and transmits it to the server.

[0345] Step 6: Stockpile information analysis

[0346] The server evaluates what is lacking based on the stockpile information entered by the user, and creates a list of stockpiles that are lacking in accordance with the disaster risk assessment.

[0347] Step 7: Emotion Recognition

[0348] The device collects user input and operation data and generates data to estimate the user's emotional state.

[0349] The server recognizes the user's emotional state using an emotion engine based on the data sent from the terminal.

[0350] Step 8: Adjust notifications based on emotion

[0351] The server tailors the notification content based on the perceived emotion, for example generating a reassuring message if the user is feeling anxious.

[0352] The server sends the adjusted notification content to the terminal.

[0353] The device will then display the received notification to the user, for example, "Here are some steps you need to take to ensure your safety."

[0354] Step 9: Stockpile Shortage Notification

[0355] The server creates information to inform the user about shortages of stockpiled items and necessary measures, and sends it to the terminal.

[0356] The device displays the received notification to the user, for example, displaying a message saying "Water stocks are low."

[0357] Step 10: Provide a purchase link

[0358] The device displays links to purchase supplies that are in short supply and a request form to contact a specialist supplier.

[0359] Users click on a link to purchase the supplies they need.

[0360] Step 11: Regular checks and notifications

[0361] The server regularly checks the expiration dates of stockpiled goods and measures to be taken in response to new seasonal risks.

[0362] If the user needs new measures, the server generates the information and sends it to the terminal.

[0363] The device displays the received periodic notifications to the user, who can then check the notifications and take any necessary action.

[0364] In this way, the system supports users in taking effective disaster countermeasures through each step. By combining this system with an emotion engine, it can provide responses that are in line with the user's emotional state, increasing a sense of security in the event of a disaster.

[0365] Example 2

[0366] 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."

[0367] In modern society, natural disasters such as earthquakes, tsunamis, and heavy rains occur frequently, making it important to prepare promptly and appropriately. However, the wide variety of disaster-related information makes it difficult for ordinary users to effectively utilize this information and prepare appropriate emergency supplies. Furthermore, advanced data analysis is required to accurately assess disaster risk and take necessary measures. Furthermore, notifications and countermeasure suggestions that ignore the user's emotional state present a challenge, making it difficult to alleviate the anxiety and stress felt by users.

[0368] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting disaster-related data, means for analyzing the collected data and calculating the disaster risk in the user's area, means for analyzing stockpile information entered by the user and identifying stockpile shortages based on the disaster risk, means for notifying the user of stockpile shortages and necessary countermeasures, means for providing links to purchase the stockpile shortages and request forms to vendors, means for periodically checking stockpile expiration dates and new countermeasures according to the season and notifying the user, and means for recognizing the user's emotions and adjusting the notification content and countermeasure priority. This allows the user to receive accurate and timely disaster risk information and countermeasure suggestions, and further enables flexible countermeasures according to the user's emotional state.

[0369] A "disaster" is an emergency situation caused by natural phenomena, such as an earthquake, tsunami, or heavy rain.

[0370] "Data" refers to information in the form of numbers, images, text, etc., related to earthquakes, tsunamis, and heavy rain, including disaster-related information.

[0371] A "server" is a central computer that collects, analyzes, and stores data.

[0372] "User" means an individual or organization that uses the system to prepare for a disaster.

[0373] "Stocked goods" are supplies such as food, water, medicine, etc. that users prepare in advance in preparation for disasters.

[0374] A "notification" is a message containing information, warnings, or suggestions sent by the system to a user.

[0375] "Link" means a hypertext link that a user can click to go directly to related information or a purchasing page.

[0376] "Vendors" are businesses that specialize in providing disaster-related goods and services.

[0377] A "form" is an interface on the web or in an application that allows a user to enter information.

[0378] "Emotion" refers to the user's psychological state, and includes emotions such as joy, sadness, anxiety, and stress.

[0379] "Countermeasures" refer to specific actions and preparations that users should take to prepare for disasters.

[0380] "Analysis" is the process of performing calculations and analysis based on collected data to derive meaningful results.

[0381] "Calculation" is the process of quantifying collected data and using algorithms to perform specific risk assessments.

[0382] The present invention relates to a system for disaster risk analysis and stockpile management, and furthermore, by adjusting notification content and the priority of countermeasures based on the user's emotional state, it is possible to provide optimal countermeasures according to the user's needs.

[0383] Data collection methods

[0384] server

[0385] The server periodically collects data on earthquakes, tsunamis, heavy rains, etc. This data is retrieved from reliable data sources on the Internet using Python libraries (Requests and BeautifulSoup), including government APIs and satellite data.

[0386] Example: Obtain earthquake data from the Japan Meteorological Agency's API and store it in a database.

[0387] Data Analysis Methods

[0388] server

[0389] The server analyzes the collected data and calculates the disaster risk in the area where the user lives, using machine learning algorithms powered by TensorFlow.

[0390] Example: Predicting earthquake risk in a region by learning from past earthquake data.

[0391] Stockpile information input method

[0392] Terminal

[0393] The terminal provides an interface for users to input information about their home emergency supplies. Using a smartphone app (iOS: Swift, Android: Kotlin) or a web app (React.js), users can input the types, quantities, and expiration dates of emergency supplies such as food, water, and medicine.

[0394] Example prompt: "Please list your current stockpile items including their quantities and expiration dates."

[0395] Example: Enter "5 liters of water, expiration date: January 2024."

[0396] Stockpile information analysis method

[0397] server

[0398] The server stores the stockpile information sent by users in a database and analyzes the data using Pandas. It compares the data with disaster risk information to identify shortages of stockpile items.

[0399] Example: Comparing the stockpile list with disaster risk information and determining that "water stocks are insufficient."

[0400] Notification means

[0401] server

[0402] The server notifies users of shortages and necessary measures by sending push notifications using Firebase Cloud Messaging (FCM).

[0403] Example: "Water supplies are low. Please bring at least 3 liters of water."

[0404] Terminal

[0405] The terminal receives the notification from the server and displays the notification to the user in a pop-up or banner.

[0406] Purchase link provision method

[0407] Terminal

[0408] The terminal displays links to purchase the supplies in short supply (for example, online shopping sites) and a request form from a specialist supplier.

[0409] Example: Displaying a notification such as "Water stocks are low. You can purchase more at this link."

[0410] User

[0411] The user uses the displayed link to purchase the supplies they are missing.

[0412] Example: Clicking on a link in a notification to order water from an online shopping site.

[0413] Regular check method

[0414] server

[0415] The server uses Django's Celery to periodically check the expiration dates of stockpiled items and measures to address new risks.

[0416] Example: In summer, generate and send notifications such as "Please take precautions against heatstroke. Don't forget to replenish with water and salt."

[0417] Terminal

[0418] The terminal receives the periodic notification sent from the server and displays it to the user.

[0419] emotion recognition means

[0420] Terminal

[0421] The device collects the user's input speed and input content, and provides an interface to obtain emotion data. Emotions are estimated using a JavaScript library (TensorFlow.js).

[0422] Example prompt: "How do you feel after receiving the disaster risk notification? (e.g., worried, stressed)"

[0423] Example: Inferring "anxiety" from user input.

[0424] server

[0425] The server uses an emotion engine to analyze the emotion data sent from the terminal and recognize the user's emotional state.

[0426] Emotion-based notification adjustment

[0427] server

[0428] The server adjusts the notification content based on the emotion recognition results: if the user is feeling anxious, it generates a reassuring message.

[0429] Example: Generate and send a message such as, "We're sharing specific steps to help you stay safe. Please stay tuned for next steps."

[0430] Terminal

[0431] The terminal receives the adjusted notification and displays it to the user.

[0432] A method for adjusting countermeasure priorities according to emotions

[0433] server

[0434] The server adjusts the priority of the proposed measures according to the user's emotions. The emotion engine can utilize a generative AI model.

[0435] Example: "If a user feels anxious, we suggest they start with basic supplies of water and food."

[0436] Terminal

[0437] The terminal displays the adjusted measures to the user and encourages them to take specific actions.

[0438] This system allows users to receive disaster risk information quickly and appropriately, enabling them to take the most appropriate measures according to the situation. In addition, flexible notification adjustment and optimization of countermeasure priorities based on emotion recognition allow users to prepare for disasters with even greater peace of mind.

[0439] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0440] Program processing steps

[0441] Step 1:

[0442] A user launches a smartphone app or web app and enters the address where they live. The entered address information is sent to the server via the device.

[0443] Input: Address information (e.g. "Tokyo, Chiyoda-ku, Marunouchi 1-chome~")

[0444] Output: The address information is sent to the server and stored.

[0445] Step 2:

[0446] The server collects the data necessary to calculate the disaster risk for that area based on the received address information. The server obtains data on earthquakes, tsunamis, and heavy rain from the Japan Meteorological Agency and overseas satellite data providers.

[0447] Input: Address information

[0448] Output: Data required for disaster risk calculation (e.g., past earthquake data, tsunami impact area data, rainfall data)

[0449] Step 3:

[0450] The server analyzes the collected data using machine learning algorithms powered by TensorFlow to calculate the disaster risk level for each address.

[0451] Input: Disaster-related data

[0452] Output: Risk level (e.g., earthquake risk "high", tsunami risk "medium", heavy rain risk "low")

[0453] Step 4:

[0454] The server generates a notification message for the user based on the risk level, and the generated notification is sent to the user's device via Firebase Cloud Messaging (FCM).

[0455] Input: Risk Level

[0456] Output: Notification message (e.g. "Your area is at high risk of earthquakes")

[0457] Step 5:

[0458] The terminal receives the notification message sent from the server and displays it to the user in a pop-up or banner.

[0459] Input: Notification message

[0460] Output: Notification shown to the user (e.g., a popup saying "Your area is at high risk of earthquakes")

[0461] Step 6:

[0462] The user follows the instructions in the app to enter information about emergency supplies, which is then sent to the server via the device.

[0463] Input: Stockpile information (e.g., "5 liters of water, expiration date: January 2024")

[0464] Output: Stockpile information is sent to the server and saved.

[0465] Step 7:

[0466] The server analyzes the stockpile information sent by the user and cross-references the data using Pandas. It compares the data with disaster risk information to identify shortages of stockpile items.

[0467] Input: Stockpile information

[0468] Output: Information about the shortage of supplies (e.g., "Water supplies are running low")

[0469] Step 8:

[0470] The server creates links to purchase the stockpiles that are in short supply, as well as a request form for specialist vendors, and notifies the user.

[0471] Input: Missing stockpile information

[0472] Output: Notification message with a purchase link (e.g. "Your water supply is low. You can purchase more at this link")

[0473] Step 9:

[0474] The terminal receives the notification with the link sent from the server and displays it to the user.

[0475] Input: Notification message with purchase link

[0476] Output: The notification displayed to the user

[0477] Step 10:

[0478] The user uses the provided link to purchase the missing supplies, and once the purchase is complete, the information is updated and sent back to the server.

[0479] Input: Purchase link click and purchase information

[0480] Output: Updated stockpile information is sent to the server and stored.

[0481] Step 11:

[0482] The server uses Django's Celery to periodically check the expiration dates of users' stockpiles and new seasonal disaster risks, and generates notification messages.

[0483] Input: Stockpile information, seasonal countermeasure information

[0484] Output: Periodic notification message (e.g. "Summer preparation is necessary. Don't forget to replenish with water and salt.")

[0485] Step 12:

[0486] The terminal receives the periodic notification sent from the server and displays it to the user.

[0487] Input: Periodic notification message

[0488] Output: The notification displayed to the user

[0489] Step 13:

[0490] The device collects emotion data from the user's input speed and content, and uses a JavaScript library (TensorFlow.js) to estimate emotions.

[0491] Input: User-entered data

[0492] Output: Estimated emotion data (e.g., "anxiety")

[0493] Step 14:

[0494] The server uses an emotion engine to analyze the emotion data sent from the terminal and recognize the user's emotional state.

[0495] Input: Emotion data

[0496] Output: Emotional state (e.g., "I feel anxious")

[0497] Step 15:

[0498] The server adjusts the notification content based on the user's emotions, generating a reassuring message for users who are feeling particularly anxious.

[0499] Input: Emotional state

[0500] Output: A tailored notification message (e.g., "We'll provide you with specific steps to stay safe")

[0501] Step 16:

[0502] The terminal receives the adjusted notification and displays it to the user.

[0503] Input: The adjusted notification message

[0504] Output: The notification displayed to the user

[0505] Step 17:

[0506] The server proposes high-priority measures based on the user's emotions. The emotion engine uses a generative AI model to prioritize measures that require urgency.

[0507] Input: Emotional state

[0508] Output: A list of prioritized actions (e.g., "Secure water and food first.")

[0509] Step 18:

[0510] The terminal displays high-priority measures to the user and encourages them to take specific actions.

[0511] Input: Priority Measures List

[0512] Output: Notification with specific measures (e.g., "First, secure water and food.")

[0513] Through the above processing steps, users can receive prompt and accurate disaster risk information and countermeasure suggestions, and can take the most appropriate countermeasures according to the situation. In addition, flexible notification adjustment and countermeasure priority optimization based on emotion recognition allow users to prepare for disasters with even greater peace of mind.

[0514] (Application example 2)

[0515] 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."

[0516] Disaster risk analysis systems have a problem in that they do not take into account the user's emotional state, which means that users are unable to take appropriate measures while feeling anxious or stressed.In addition to notifying users of disaster risks and shortages of emergency supplies, there is a need for systems that can adjust the priority of measures based on emotions and send messages that provide users with a sense of security.

[0517] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting large amounts of data and satellite data related to disasters such as earthquakes, tsunamis, and heavy rain; means for analyzing the collected data and calculating the disaster risk in the area where the user resides; means for analyzing stockpile information entered by the user and identifying missing stockpile items based on the disaster risk; means for notifying the user of missing stockpile items and necessary countermeasures; means for providing links to purchase missing stockpile items and request forms to specialist vendors; means for periodically checking for new countermeasures based on the expiration date of stockpile items and the season and notifying the user; and means for recognizing the user's emotional state and adjusting the content of the notification and the priority of countermeasures based on the user's emotional state. This makes it possible to optimize the user's response to risk notifications by taking emotions into consideration.

[0518] markdown

[0519] A "disaster" is a destructive event caused by natural phenomena or human factors, including earthquakes, tsunamis, and heavy rains.

[0520] "Massive data" refers to a huge amount of information collected from various sources, and is also known as big data.

[0521] "Satellite data" refers to observation data obtained from satellites, including meteorological information and the state of the Earth's surface.

[0522] "Disaster risk" refers to an assessment of the likelihood of a disaster occurring in a particular region or environment and the magnitude of its impact.

[0523] "Stock information" refers to detailed information about food, water, first aid supplies, etc. that users have stored at home, in stores, etc.

[0524] "Missing stockpiles" indicate supplies that the user needs to prepare based on disaster risk but that are in short supply.

[0525] "Notification" refers to the act or means of providing information to a user, and includes push notifications, emails, and the like.

[0526] "Professional Service Request Form" refers to a formalized document or interface through which a user can submit a request to a professional service provider.

[0527] "Emotional state" indicates the user's psychological response and emotional state, and includes feelings such as anxiety and relief.

[0528] "Priority of measures" refers to the order of particularly important measures that should be implemented first among several measures.

[0529] markdown

[0530] This invention combines a disaster risk analysis system with an emotion engine. Specific embodiments will be described below.

[0531] System configuration

[0532] This system mainly consists of a server, a terminal, and a user.

[0533] server

[0534] The server is equipped with hardware and software to collect and analyze large amounts of data and satellite data to calculate disaster risk. The specific software used includes existing disaster models and machine learning algorithms for data analysis. It also analyzes stockpile information and identifies shortages based on disaster risk. Additionally, an emotion engine is used to recognize the user's emotional state and adjust notification content and countermeasure priorities accordingly.

[0535] For example, an emotion recognition model using TensorFlow can be used to quantify the user's emotional data. If the analysis results indicate that the user is feeling anxious, a message that provides reassurance can be generated and sent to the device.

[0536] Terminal

[0537] The terminal functions as an interface with the user and is typically a smartphone or tablet. The terminal receives notifications from the server and displays them to the user. It also has the function of collecting emergency supply information entered by the user and sending it to the server. Information is provided to the user on the terminal in the form of push notifications, email notifications, pop-ups, etc.

[0538] For example, when a user enters their home address, the server sends the disaster risk information for that area to the device, and the user is notified that "the earthquake risk in your area is high."

[0539] User

[0540] Users use the application to input their personal information, such as address and emergency supplies, and the system analyzes the data. It also estimates the user's emotional state based on their reactions and input speed.

[0541] Specific examples

[0542] As an example of how all the components work together, the following procedure can be considered:

[0543] 1. The user launches the smartphone app and enters the address where they live. For example, they enter a specific address such as "Shibuya-ku, Tokyo."

[0544] 2. The server collects and analyzes disaster data for the area based on the entered address information, and sends the results to the terminal.

[0545] 3. The device notifies the user of the disaster risk received as a result of the analysis. A message such as "The tsunami risk in your area is high" is displayed.

[0546] 4. The user inputs stockpile information and is notified of specific shortages of stockpile items, such as "not enough water."

[0547] 5. The server uses an emotion engine to analyze the user's emotional state and, for example, if the user is feeling "anxious," generates a tailored notification such as, "Don't worry, you can purchase the supplies you need from the link below."

[0548] Prompt Sentence Examples

[0549] Using the user's address and emotion data as input, generate appropriate notification content based on disaster risk and emotion.

[0550] Address: Shibuya Ward, Tokyo

[0551] Emotional data: Anxiety

[0552] Disaster data: Earthquake risk: High, Flood risk: Medium, Tsunami risk: Low

[0553] In this way, by combining an emotion engine, the present invention realizes a system that provides disaster countermeasures that take into account the emotional state of the user. This system allows users to take appropriate measures according to their own emotional state, thereby increasing their sense of security in the event of a disaster.

[0554] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0555] markdown

[0556] Step 1:

[0557] A user launches the smartphone app and enters the address where they live. Specifically, by entering address information such as "Shibuya-ku, Tokyo" into the app's input screen, the server uses this as base data for collecting disaster data for that area. The input data is in text format, and the input text data is sent to the server as output.

[0558] Step 2:

[0559] The server collects disaster data for the area based on the address information received from the user. This collection process involves retrieving data from an external API. Specifically, it sends a GET request to the specified API endpoint (e.g., "https: / / disasterdataapi.com / shibuya") to retrieve risk data such as earthquakes, tsunamis, and heavy rain. The input is the user's address, and the output is the retrieved disaster data object.

[0560] Step 3:

[0561] The server analyzes the collected disaster data and calculates the disaster risk for the area. This analysis uses machine learning algorithms and existing disaster models. For example, it calculates a risk score based on past earthquake data. The input is a disaster data object, and the output is a calculated risk score object.

[0562] Step 4:

[0563] The server receives and analyzes emergency supply information entered by the user. When a user enters emergency supply information such as "water," "emergency food," and "batteries" on their smartphone, the information is sent to the server. The input is emergency supply information text, and the output is an emergency supply data object.

[0564] Step 5:

[0565] The server identifies shortages of stockpiles based on disaster risk. This identification process uses an algorithm that compares risk scores with the user's stockpile information. For example, analysis is performed according to rules such as "if the tsunami risk is high, more water is needed." The inputs are a risk score object and a stockpile data object, and the output is a list of stockpiles that are in short supply.

[0566] Step 6:

[0567] The server notifies the user of shortages and necessary measures. Specifically, it sends a message such as "Water stocks are low" to the user using push notifications or email notifications. This notification is sent to the device in JSON format and displayed on the device. The input is a list of shortages of supplies, and the output is a notification object.

[0568] Step 7:

[0569] The server analyzes the user's input speed and content to recognize the user's emotional state. It uses an emotion recognition model such as TensorFlow. The input is the user's input data, and the output is an emotional state object.

[0570] Step 8:

[0571] The server adjusts the notification content and priority of countermeasures based on the recognized emotional state. For example, if the user is feeling anxious, it generates a message such as "Don't worry, you can purchase the necessary supplies from the link below." The input is an emotional state object, and the output is the adjusted notification object.

[0572] Step 9:

[0573] The device receives the tailored notification from the server and displays it to the user. It is displayed as a pop-up or banner notification on the device screen. A specific example of this is a notification such as "Your area is at high risk of earthquakes. Water supplies are running low. Don't worry, you can purchase supplies from the link below." The input is the tailored notification object, and the output is the displayed notification.

[0574] 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.

[0575] 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.

[0576] 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.

[0577] [Second embodiment]

[0578] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0579] 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.

[0580] 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).

[0581] 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.

[0582] 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.

[0583] 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).

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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.

[0588] 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.

[0589] 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."

[0590] The present invention relates to a disaster risk analysis and countermeasure proposal system. This system operates in cooperation with a server, terminals, and users to analyze the disaster risk in the area where the user lives and propose appropriate countermeasures and emergency supplies.

[0591] System Operation Overview

[0592] Data collection methods

[0593] server

[0594] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rains, etc. This data is used as basic data for disaster risk analysis. The data is obtained from reliable data sources via the Internet and stored in a database.

[0595] Data Analysis Methods

[0596] server

[0597] The server analyzes the collected data and calculates the disaster risk for the area the user lives in. The analysis is performed using existing disaster models and machine learning algorithms. For example, it predicts the risk level for the area based on data from past earthquakes and tsunamis.

[0598] Stockpile information input method

[0599] Terminal

[0600] The device provides an interface for users to input information about emergency supplies stored at home. Users can enter this information using a smartphone app or a web app. Input items include the type and quantity of emergency supplies, as well as their expiration date.

[0601] Stockpile information analysis method

[0602] server

[0603] The server identifies shortages based on the stockpile information entered by the user, and analyses it against disaster risk assessments to determine whether stockpiles are sufficient for high-risk disasters.

[0604] Notification means

[0605] server

[0606] The server notifies users of shortages of supplies and necessary measures via push notifications or email. For example, it may send a specific message such as, "Your water supply is low."

[0607] Terminal

[0608] The device receives notifications from the server and displays them to the user. When a notification is received, the user is notified via a pop-up or banner.

[0609] Purchase link provision method

[0610] Terminal

[0611] The device provides links to purchase supplies that are in short supply and a request form to specialists, allowing users to easily purchase needed supplies or request assistance from specialists within the app.

[0612] User

[0613] Users can view notifications and click links to purchase supplies they need, such as ordering water or food directly from within the app.

[0614] Regular check method

[0615] server

[0616] The server periodically checks expiration dates of stockpiled items and measures to address new seasonal risks, such as heatstroke prevention measures in summer and cold weather measures in winter.

[0617] Terminal

[0618] The device receives periodic notifications sent from the server and displays them to the user, allowing the user to periodically update their stockpiles and take new measures.

[0619] Specific example of system operation

[0620] Risk notification after entering address

[0621] 1. User: Launches the app and enters their address.

[0622] 2. Terminal: Sends address information to the server.

[0623] 3. Server: Analyzes the disaster risk in the area based on the address information and sends the results to the device.

[0624] 4. Device: Notify the user that "There is a high risk of tsunami in your area."

[0625] Check for shortages of emergency supplies and purchase them

[0626] 1. User: Enters current stockpile list into the app.

[0627] 2. Terminal: Sends stockpile information to the server.

[0628] 3. Server: Identify shortages of supplies based on disaster risk information.

[0629] 4. On the device: Notify the user that their water supply is low and provide a link to purchase water.

[0630] 5. User: Clicks on the link to purchase the scarce water.

[0631] In this way, the present invention provides a system that combines multiple means to support users in taking effective disaster countermeasures, allowing users to take appropriate disaster countermeasures themselves and reducing the burden on government agencies.

[0632] The processing flow will be explained below.

[0633] Step 1: Data collection

[0634] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rain, etc. from reliable data sources. This data is stored in a database as basic data for disaster risk analysis.

[0635] Step 2: Enter user information

[0636] Users launch the app and enter initial information such as their address, family composition, and current list of emergency supplies.

[0637] The terminal receives the information entered by the user, checks the format, and then sends it to the server.

[0638] Step 3: Risk analysis

[0639] The server calculates the disaster risk for the area based on the address information sent by the user, analyzes collected big data and satellite data, and evaluates the risk level.

[0640] Step 4: Risk notification

[0641] Based on the results of the risk analysis, the server generates risk information customized for each user and transmits this information to the terminal.

[0642] The device will notify the user of the received risk information via pop-ups or notification banners.

[0643] Step 5: Gather information on emergency supplies

[0644] Users use a smartphone app or web app to enter information about the emergency supplies they have at home.

[0645] The terminal receives the stockpile information and transmits it to the server.

[0646] Step 6: Stockpile information analysis

[0647] The server evaluates what is lacking based on the stockpile information entered by the user, compares it with disaster risk, and generates a list of stockpiles that are lacking.

[0648] Step 7: Stockpile shortage notification

[0649] The server generates data to notify users about shortages of stockpiles and necessary measures, and sends this data to the terminal.

[0650] The device will then display the received notification to the user, for example, a message saying "Water stocks are running low."

[0651] Step 8: Provide a purchase link

[0652] The device displays links to purchase supplies that are in short supply and a request form to contact a specialist supplier.

[0653] Users can click on the link to purchase the supplies they need.

[0654] Step 9: Regular checks

[0655] The server regularly checks the expiration dates of stockpiled goods and measures to be taken in response to new seasonal risks.

[0656] If the user needs new measures, the server generates the information and sends it to the terminal.

[0657] Step 10: Periodic Notifications

[0658] The terminal receives the periodic notifications sent from the server and displays them to the user, who can then check the notifications and take the necessary action.

[0659] In this way, the system supports users in taking effective disaster prevention measures through each step.

[0660] Example 1

[0661] 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."

[0662] In disaster risk analysis and countermeasure proposals, there is a lack of systems that can accurately assess the specific risks in the area where a user lives and identify shortages of stockpiles based on that assessment.In addition, there is a lack of notifications and purchasing links that allow users to take effective disaster countermeasures, making it difficult to respond quickly and appropriately.

[0663] 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.

[0664] In this invention, the server includes means for collecting large-scale data and satellite data related to disasters such as earthquakes, tsunamis, and heavy rains, means for analyzing the collected data and calculating the disaster risk in the area where the user lives, and means for collecting emergency stockpile information entered by the user. This makes it possible to evaluate the specific disaster risk in the area where the user lives, identify emergency stockpile shortages based on the evaluation, and provide the user with prompt and appropriate countermeasures.

[0665] A "disaster" is an event that includes adverse effects caused by natural phenomena such as earthquakes, tsunamis, and heavy rains.

[0666] "Big data" refers to a vast amount of information, also known as big data, and includes data related to natural disasters and satellite image data.

[0667] "Satellite data" refers to observation data obtained from artificial satellites, including meteorological and topographical information.

[0668] "Analysis" refers to the process of organizing, calculating, and evaluating collected data to arrive at a specific conclusion.

[0669] "Disaster risk" refers to an assessment value that indicates the possibility of a disaster occurring in a particular area and the extent of damage caused by that disaster.

[0670] "Stockpiles" refer to supplies stored in advance in preparation for emergencies such as disasters. These include food, water, medicine, etc.

[0671] "Notification" means a message or alert intended to inform the User of important information.

[0672] "Purchase Link" means a direct link to an online shopping site provided to enable Users to easily purchase shortage supplies.

[0673] "Vendor Request Form" means an electronic form for entering and submitting a request to a vendor for a particular service or supply.

[0674] "Expiration date" refers to the date by which stockpiled items are recommended for use, after which their quality may deteriorate.

[0675] "Regularly" means repeated at regular intervals, including monthly, seasonal, and other cycles.

[0676] "Residence information" refers to information about the area and address where the user lives.

[0677] MODE FOR CARRYING OUT THE INVENTION

[0678] The present invention relates to a system for disaster risk analysis and countermeasure proposals. This system operates in cooperation with a server, terminals, and users to analyze the disaster risk in the area where the user resides and propose appropriate countermeasures and stockpiles based on the analysis. This specification describes a specific implementation method of the system.

[0679] Hardware and software used

[0680] server

[0681] The server is the central component that collects and analyzes large-scale data related to earthquakes, tsunamis, heavy rain, and other events, as well as satellite data. Data is collected from reliable data sources via the Internet. Specifically, data is obtained from APIs such as those of the Japan Meteorological Agency and NASA, and the data is stored in a MySQL database. Machine learning algorithms using Python's Scikit-Learn library are used for data analysis.

[0682] Terminal

[0683] The terminal provides an interface for users to input emergency stockpile information and receive notifications. The smartphone app was created with React Native and features a user-friendly input form and notification function. The terminal also receives data sent from the server and displays alerts to the user.

[0684] User

[0685] Users use the app to enter their information, including their address and a list of supplies, and view notifications and suggestions from the system.

[0686] System Operation Overview

[0687] Data collection and analysis

[0688] server

[0689] The server periodically collects large-scale data on natural disasters and satellite data, storing it in a MySQL database. The data is analyzed using machine learning algorithms using Python's Scikit-Learn to calculate disaster risk for each region. This analysis then predicts risk levels, such as the probability of earthquakes and tsunamis occurring.

[0690] Management of emergency stockpile information

[0691] Terminal

[0692] The terminal provides a form for users to enter stockpile information, which is then sent over the Internet to a server. The data is encrypted using the HTTPS protocol, ensuring secure transmission.

[0693] server

[0694] The server stores the received stockpile information in a database, compares it with disaster risk information, and identifies shortages of stockpile items. From the analysis results, specific information such as "water stocks are insufficient" is generated.

[0695] User Notifications and Actions

[0696] server

[0697] The server then sends notifications to users based on the analysis results, including messages such as "Tsunami risk in your area is increasing. Water reserves are running low," and these notifications are sent to devices via a push notification API.

[0698] Terminal

[0699] The device receives notifications sent from the server and displays them to the user, often as popups or banners within the app, allowing the user to take any necessary action immediately.

[0700] User

[0701] Users can click on the purchase link in the notification to purchase the supplies they need online, for example, by going directly to an e-commerce site such as Amazon or Rakuten Ichiba and ordering the supplies they need.

[0702] Regular checks and update notifications

[0703] server

[0704] The server periodically checks the expiration dates and seasonal risks of stockpiled items. The results of the check are notified to the user. For example, the server may notify the user that "The expiration date of the stockpiled food is approaching. Please purchase new stockpiles."

[0705] Terminal

[0706] The terminal receives periodic update notifications sent from the server and displays them to the user, allowing the user to periodically update their stockpiles and take new measures.

[0707] Prompt Sentence Examples

[0708] 1. "I would like to know the disaster risk in the area where I live. I live in Shibuya Ward, Tokyo."

[0709] 2. "I just typed in the list of food items I have stored at home. Is this list sufficient for disaster preparedness?"

[0710] 3. "Please let me know if there is anything missing from our summer stockpile."

[0711] 4. "I'd like to purchase water within the app. Can you provide a link?"

[0712] The above is an embodiment of the present invention. This system allows users to take effective disaster countermeasures and reduces the burden on government agencies.

[0713] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0714] Step 1: Data collection

[0715] server

[0716] The server collects large-scale disaster-related data and satellite data at a specified time every day. The data is acquired using APIs from, for example, the Japan Meteorological Agency or NASA. The API endpoint and authentication information of each data source are used as input, and the acquired data is stored in the server's MySQL database as output. Specifically, the data acquisition script is executed periodically using the Python requests library.

[0717] Step 2: Data analysis

[0718] server

[0719] The server analyzes the acquired dataset and calculates the disaster risk for each region. The data collected in step 1 is used as input, and the output is an assessment value indicating the risk level for each region. Specifically, a machine learning algorithm using Python's Scikit-Learn library processes the data and performs a risk assessment. The analysis results are then stored in a MySQL database.

[0720] Step 3: Enter emergency supply information

[0721] Terminal

[0722] The terminal provides an interface for users to input emergency supply information. Input includes the type, quantity, and expiration date of emergency supplies, which the user enters through the app. This data is sent to the server as output. Specifically, a form created with React Native is displayed, and the information entered by the user is encrypted via HTTPS and sent to the server.

[0723] Step 4: Sending and receiving stockpile information

[0724] Terminal

[0725] The terminal sends the stockpile information entered by the user to the server in real time. The input includes the data entered by the user in the form, and this data is sent to the server as output. Specifically, the user enters stockpile information and clicks the "Send" button, and the data reaches the server.

[0726] server

[0727] The server receives the stockpile information sent from the terminal and stores it in a database. The input includes the stockpile information sent, and the output is information in a format that can be stored in the database. Specifically, it analyzes the received information and stores the data in the appropriate table.

[0728] Step 5: Analyze stockpile information

[0729] server

[0730] The server analyzes the received stockpile information and compares it with disaster risk information to identify shortages. Input includes stored stockpile information and the local risk level. The output generates a list of shortages. Specifically, a Python script retrieves the necessary information from the database and uses an analysis algorithm to identify shortages.

[0731] Step 6: Create and send notifications

[0732] server

[0733] The server creates and sends notifications based on shortages of stockpiles and disaster risks. The input includes the analysis results of stockpile information, and the output is a specific notification message. Specifically, the notification generation script creates the notification content based on the analysis results and sends it to the device via the push notification API.

[0734] Terminal

[0735] The device receives notifications from the server and displays them to the user. The input includes the notification message sent from the server, and the output is the notification displayed on the device screen. Specifically, the notification is displayed as a popup or banner within the app.

[0736] Step 7: Provide a purchase link and take action

[0737] Terminal

[0738] The device displays links to purchase supplies and a request form to specialist vendors for supplies that are in short supply. The input includes notification content from the server, and the output is a purchase link that is displayed to the user. Specifically, the link is displayed on a screen within the app, and the user can click it.

[0739] User

[0740] The user clicks on the purchase link in the notification to purchase the missing supplies. The input includes the displayed purchase link, and the output is access to an e-commerce site, etc. The specific operation is that the user clicks the link, a browser opens, and the purchase page is displayed.

[0741] Step 8: Regular checks and update notifications

[0742] server

[0743] The server periodically checks the expiration dates and seasonal risks of stockpiled items and notifies users of the results. The input includes stockpile information and time information in the database, and the output is an update notification message. Specifically, a periodic script scans the database and generates and sends the necessary notifications.

[0744] Terminal

[0745] The device receives periodic update notifications from the server and displays them to the user. The input includes the update notification message from the server, and the output is the update notification displayed on the device screen. Specifically, the notification is displayed as a pop-up or banner within the app, allowing the user to check it periodically.

[0746] In this way, by clarifying the input and output at each step and describing the specific operations, the operation of the entire system and its specific processing procedures become clear.

[0747] (Application example 1)

[0748] 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."

[0749] In modern society, large-scale natural disasters occur frequently, making it important for individuals and households to prepare appropriate emergency supplies. However, it is not easy for individuals to assess disaster risk and manage the necessary supplies themselves. In addition, there is a lack of systems for properly managing emergency food and other supplies needed in the event of a disaster, and for quickly replenishing supplies when shortages occur. This can result in insufficient supplies in emergencies, significantly impacting the lives of disaster victims. Furthermore, conventional systems do not integrate disaster risk assessment and emergency supply management, making it difficult to implement efficient countermeasures.

[0750] 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.

[0751] In this invention, the server includes means for collecting big data and satellite data related to disasters such as earthquakes, tsunamis, and heavy rains; means for analyzing the collected data and calculating the disaster risk in the user's area; and means for analyzing stockpile information entered by the user and identifying shortages of stockpile items based on the disaster risk. This enables the server to analyze the disaster risk in the user's area, manage the emergency food stockpile status in the event of a disaster, suggest emergency food items to the user, and enable easy ordering. Furthermore, by allowing the user to enter address information and receive confirmation of shortages of emergency food and other items according to the disaster risk and a link to purchase them, the server allows the user to quickly and reliably prepare stockpiles.

[0752] "Big data" refers to a large amount of data in a variety of formats, and is data that is so large that it is difficult to process using conventional data processing applications.

[0753] "Satellite data" refers to information obtained from Earth observation satellites and satellite images, and is data used to understand changes in terrain and climate.

[0754] "Disaster risk" is an index that evaluates the likelihood of natural disasters such as earthquakes, tsunamis, and heavy rains occurring and their impact.

[0755] "Stocked goods information" refers to information such as the type, quantity, and expiration date of disaster supplies that the user keeps in their own home or facility.

[0756] "Emergency food" refers to food that can be consumed immediately in the event of a disaster, can be stored for a long period of time, and is pre-cooked or can be eaten with simple cooking procedures.

[0757] A "notification" is the act of conveying warnings or information to a user, such as a message sent via push notification or email.

[0758] A "purchase link" is a hyperlink to a web page provided for users to quickly purchase the missing supplies.

[0759] The "specialist vendor request form" is an input form for a user to request the necessary goods or services from a specialist vendor.

[0760] The "best before" date is the date by which the quality of food is guaranteed, and after this date the taste and quality may deteriorate.

[0761] "Regular checks" are a process of checking the status of stockpiled goods and any new risks at regular intervals, and notifying users as necessary.

[0762] "Management of emergency food stockpiles in the event of a disaster" is the process of determining the types and quantities of emergency food that users possess and checking to see if there are any shortages.

[0763] "Easy ordering means" is a function that allows users to quickly and easily purchase shortages of stockpiled items through the application.

[0764] This invention relates to a system for disaster risk analysis and countermeasure proposals. This system, which operates in cooperation with a server, terminals, and users, analyzes the disaster risk in the user's residential area and proposes appropriate countermeasures and emergency supplies. It also has a function that allows users to input address information and emergency supply information, and provides a confirmation of shortages of emergency supplies and a link to purchase them.

[0765] Data collection methods

[0766] server

[0767] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rain, etc. This data is used as basic data for disaster risk analysis. The data is obtained from reliable data sources via the Internet and stored in a database. For example, data is collected from public disaster information APIs and satellite data provision services.

[0768] Data Analysis Methods

[0769] server

[0770] The server analyzes the collected data and calculates the disaster risk in the area where the user lives. The analysis uses existing disaster models and machine learning algorithms. For example, it predicts the risk level of the area based on data from past earthquakes and tsunamis, and notifies the user that "there is a high risk of tsunami in your area."

[0771] Stockpile information input method

[0772] Terminal

[0773] Users use a smartphone app or web app to input information about their home emergency supplies, including water, emergency food, batteries, etc., and are provided with an interface for entering information such as the type, quantity, and expiration date of each item.

[0774] Stockpile information analysis method

[0775] server

[0776] The server identifies shortages based on the stockpile information entered by the user. Analysis is performed against disaster risk assessments to confirm whether stockpiles are sufficient for high-risk disasters. For example, in areas with a high risk of tsunamis, the server will notify users that "water stockpiles are insufficient."

[0777] Notification means

[0778] Server and terminal

[0779] The server notifies users of shortages of supplies and necessary measures via push notifications and emails. Devices receive notifications from the server and have the ability to notify users via pop-ups and banners.

[0780] Purchase link provision method

[0781] Terminal

[0782] The device provides links to purchase supplies that are in short supply and a request form to specialists, allowing users to easily purchase the supplies they need or request assistance from specialists within the app.

[0783] Regular check method

[0784] Server and terminal

[0785] The server periodically checks expiration dates of stockpiled items and measures to respond to new seasonal risks, and notifies the user. The terminal has the function to receive periodic notifications sent from the server and display them to the user.

[0786] Example of a system

[0787] This system uses Python to write data analysis scripts and collects data from external APIs using the HTTP request module (requests library). It also uses the smtplib library to send emails. The system analyzes disaster risks in the user's area, identifies shortages of emergency supplies based on the results, and sends notifications, allowing users to take appropriate measures quickly.

[0788] Specific example prompt

[0789] Below are some example prompts to use when inputting a generative AI model:

[0790] You are the developer of a food delivery app. You want to incorporate a disaster risk analysis and countermeasure proposal system into this app. Design a disaster prevention function that works on the smartphone app based on the following specifications:

[0791] 1. Analyze disaster risk based on address information entered by the user.

[0792] 2. Analyze the stockpile information entered by the user and identify any shortages.

[0793] 3. Notify users via push notification or email about shortages and necessary precautions.

[0794] 4. Include a function to provide links to purchase supplies that are in short supply.

[0795] The output should include a detailed process flow and a description of the software and hardware used.

[0796] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0797] Step 1:

[0798] Data collection

[0799] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rain, etc. from reliable data sources via the Internet. This data includes information on past disasters and meteorological data. The server uses this data as basic data for assessing disaster risk and stores it in a database. The input is disaster data from an external API, and the output is the data to be analyzed that is stored in the server's database.

[0800] Step 2:

[0801] Data analysis

[0802] The server analyzes the collected data and calculates the disaster risk for the residential area based on the address information entered by the user. Existing disaster models and machine learning algorithms are used here. For example, the risk level for the area is predicted based on data from past earthquakes and tsunamis. The inputs are the collected data and the user's address information, and the output is the disaster risk assessment result for the area where the user lives.

[0803] Step 3:

[0804] Enter emergency stockpile information

[0805] Users use a smartphone app or web app to input information about their home emergency supplies. This includes the type, quantity, and expiration date of items such as water, emergency food, and batteries. The input is done by the user through the app interface. The input data is sent to the server and stored in a database as emergency supply information. The input is the emergency supply information entered by the user, and the output is the emergency supply information stored in the server's database.

[0806] Step 4:

[0807] Stockpile information analysis

[0808] The server identifies what is lacking based on the stockpile information entered by the user. This is done in conjunction with disaster risk assessments to ensure that stockpiles are sufficient for high-risk disasters. For example, in areas with a high risk of tsunamis, it identifies that "water stockpiles are insufficient." The inputs are stockpile information and disaster risk assessment results, and the output is a list of stockpiles that are lacking.

[0809] Step 5:

[0810] notification

[0811] The server notifies the user of the identified shortages and necessary measures. This notification is done via push notification or email. Based on the acquired information on shortages, a specific message is generated and sent to the user. The input is the information on shortages, and the output is the notification message sent to the user.

[0812] Step 6:

[0813] Purchase link provided

[0814] The device provides users with links to purchase supplies they are running low on and a request form to specialist suppliers. Users can easily purchase the supplies they need from within the app, allowing users to quickly and reliably replenish their supplies. The input is a list of supplies they are running low on, and the output is links to purchase the supplies and a request form.

[0815] Step 7:

[0816] Regular checks

[0817] The server periodically checks the expiration dates of stockpiled items and measures to be taken in response to new seasonal risks, and notifies the user. This allows users to continuously maintain appropriate disaster prevention measures. The input is stockpiled item information and seasonal data, and the output is notification messages as a result of the regular checks.

[0818] 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.

[0819] This invention combines an emotion engine with a disaster risk analysis and countermeasure proposal system. This system works in conjunction with a server, terminal, and user to analyze the disaster risk in the user's area and propose appropriate countermeasures and emergency supplies. It also has a function to recognize the user's emotions and adjust the notification content and countermeasure priority.

[0820] System Operation Overview

[0821] Data collection methods

[0822] server

[0823] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rain, etc. from reliable data sources. This data is stored in a database as basic data for disaster risk analysis.

[0824] Data Analysis Methods

[0825] server

[0826] The server analyzes the collected data and calculates the disaster risk for the area the user lives in. The analysis is performed using existing disaster models and machine learning algorithms. For example, it predicts the risk level for the area based on data from past earthquakes and tsunamis.

[0827] Stockpile information input method

[0828] Terminal

[0829] The device provides an interface for users to input information about emergency supplies stored at home. Users can enter this information using a smartphone app or a web app. Input items include the type and quantity of emergency supplies, as well as their expiration date.

[0830] Stockpile information analysis method

[0831] server

[0832] The server identifies shortages based on the stockpile information entered by the user, and analyses it against disaster risk assessments to determine whether stockpiles are sufficient for high-risk disasters.

[0833] Notification means

[0834] server

[0835] The server notifies users of shortages of supplies and necessary measures via push notifications or email. For example, it may send a specific message such as, "Your water supply is low."

[0836] Terminal

[0837] The device receives notifications from the server and displays them to the user. When a notification is received, the user is notified via a pop-up or banner.

[0838] Purchase link provision method

[0839] Terminal

[0840] The device provides links to purchase supplies that are in short supply and a request form to specialists, allowing users to easily purchase needed supplies or request assistance from specialists within the app.

[0841] User

[0842] Users can view notifications and click links to purchase supplies they need, such as ordering water or food directly from within the app.

[0843] Regular check method

[0844] server

[0845] The server periodically checks expiration dates of stockpiled items and measures to address new seasonal risks, such as heatstroke prevention measures in summer and cold weather measures in winter.

[0846] Terminal

[0847] The device receives periodic notifications sent from the server and displays them to the user, allowing the user to periodically update their stockpiles and take new measures.

[0848] emotion recognition means

[0849] Terminal

[0850] The device provides an interface for recognizing emotions from user input and behavior, for example, estimating emotions from the user's input speed and content.

[0851] server

[0852] The server uses an emotion engine to analyze the data sent from the terminal and recognize the user's emotional state.

[0853] Emotion-based notification adjustment

[0854] server

[0855] The server then adjusts the notification content based on the perceived emotion. For example, if the user is feeling anxious, it will suggest a reassuring message or suggest measures.

[0856] Terminal

[0857] The terminal receives the adjusted notification and displays it to the user.

[0858] A method for adjusting countermeasure priorities according to emotions

[0859] server

[0860] The server adjusts the priority of suggested supplies and measures according to the user's emotions. For example, if the user is feeling stressed, it will prioritize suggested measures that require urgent action.

[0861] Terminal

[0862] The device displays the adjusted measures to the user and encourages them to take specific actions.

[0863] Specific example of system operation

[0864] Risk notification after entering address

[0865] 1. User: Launches the app and enters their address.

[0866] 2. Terminal: Sends address information to the server.

[0867] 3. Server: Analyzes the disaster risk in the area based on the address information and sends the results to the device.

[0868] 4. Device: Notify the user that "There is a high risk of tsunami in your area."

[0869] Check for shortages of emergency supplies and purchase them

[0870] 1. User: Enters current stockpile list into the app.

[0871] 2. Terminal: Sends stockpile information to the server.

[0872] 3. Server: Identify shortages of supplies based on disaster risk information.

[0873] 4. On the device: Notify the user that their water supply is low and provide a link to purchase water.

[0874] 5. User: Clicks on the link to purchase the scarce water.

[0875] Emotion recognition and response

[0876] 1. User: Receives notifications of disaster risks and shortages of emergency supplies, and inputs and takes action within the app.

[0877] 2. Terminal: Collects emotional data from the user's input speed, input content, etc.

[0878] 3. Server: Analyzes the collected emotional data and recognizes that the user is feeling anxious.

[0879] 4. Server: Generates a reassuring message based on the user's emotions and sends it to the device.

[0880] 5. Device: Display a message to the user saying, "Here are some measures you need to take to ensure your safety," and suggest specific measures.

[0881] In this way, by combining an emotion engine, the present invention realizes a system that provides disaster countermeasures that better meet user needs. This system allows users to take appropriate measures according to their own emotional state, increasing their sense of security in the event of a disaster.

[0882] The processing flow will be explained below.

[0883] Step 1: Data collection

[0884] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rain, etc. from reliable data sources. This data is stored in a database as basic data for disaster risk analysis.

[0885] Step 2: Enter user information

[0886] Users launch the app and enter initial information such as their address, family composition, and current list of emergency supplies.

[0887] The terminal receives the information entered by the user, checks the format, and then sends it to the server.

[0888] Step 3: Risk analysis

[0889] The server calculates the disaster risk for the area based on the address information sent by the user, and evaluates the risk level using collected big data and satellite data.

[0890] Step 4: Risk notification

[0891] The server creates the results of the risk analysis as risk information customized for each user and sends it to the terminal.

[0892] The device will notify the user of the received risk information via pop-ups or notification banners.

[0893] Step 5: Gather information on emergency supplies

[0894] Users use a smartphone app or web app to enter information about their home emergency supplies.

[0895] The terminal receives the stockpile information and transmits it to the server.

[0896] Step 6: Stockpile information analysis

[0897] The server evaluates what is lacking based on the stockpile information entered by the user, and creates a list of stockpiles that are lacking in accordance with the disaster risk assessment.

[0898] Step 7: Emotion Recognition

[0899] The device collects user input and operation data and generates data to estimate the user's emotional state.

[0900] The server recognizes the user's emotional state using an emotion engine based on the data sent from the terminal.

[0901] Step 8: Adjust notifications based on emotion

[0902] The server tailors the notification content based on the perceived emotion, for example generating a reassuring message if the user is feeling anxious.

[0903] The server sends the adjusted notification content to the terminal.

[0904] The device will then display the received notification to the user, for example, "Here are some steps you need to take to ensure your safety."

[0905] Step 9: Stockpile Shortage Notification

[0906] The server creates information to inform the user about shortages of stockpiled items and necessary measures, and sends it to the terminal.

[0907] The device displays the received notification to the user, for example, displaying a message saying "Water stocks are low."

[0908] Step 10: Provide a purchase link

[0909] The device displays links to purchase supplies that are in short supply and a request form to contact a specialist supplier.

[0910] Users click on a link to purchase the supplies they need.

[0911] Step 11: Regular checks and notifications

[0912] The server regularly checks the expiration dates of stockpiled goods and measures to be taken in response to new seasonal risks.

[0913] If the user needs new measures, the server generates the information and sends it to the terminal.

[0914] The device displays the received periodic notifications to the user, who can then check the notifications and take any necessary action.

[0915] In this way, the system supports users in taking effective disaster countermeasures through each step. By combining this system with an emotion engine, it can provide responses that are in line with the user's emotional state, increasing a sense of security in the event of a disaster.

[0916] Example 2

[0917] 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."

[0918] In modern society, natural disasters such as earthquakes, tsunamis, and heavy rains occur frequently, making it important to prepare promptly and appropriately. However, the wide variety of disaster-related information makes it difficult for ordinary users to effectively utilize this information and prepare appropriate emergency supplies. Furthermore, advanced data analysis is required to accurately assess disaster risk and take necessary measures. Furthermore, notifications and countermeasure suggestions that ignore the user's emotional state present a challenge, making it difficult to alleviate the anxiety and stress felt by users.

[0919] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting disaster-related data, means for analyzing the collected data and calculating the disaster risk in the user's area, means for analyzing stockpile information entered by the user and identifying stockpile shortages based on the disaster risk, means for notifying the user of stockpile shortages and necessary countermeasures, means for providing links to purchase the stockpile shortages and request forms to vendors, means for periodically checking stockpile expiration dates and new countermeasures according to the season and notifying the user, and means for recognizing the user's emotions and adjusting the notification content and countermeasure priority. This allows the user to receive accurate and timely disaster risk information and countermeasure suggestions, and further enables flexible countermeasures according to the user's emotional state.

[0920] A "disaster" is an emergency situation caused by natural phenomena, such as an earthquake, tsunami, or heavy rain.

[0921] "Data" refers to information in the form of numbers, images, text, etc., related to earthquakes, tsunamis, and heavy rain, including disaster-related information.

[0922] A "server" is a central computer that collects, analyzes, and stores data.

[0923] "User" means an individual or organization that uses the system to prepare for a disaster.

[0924] "Stocked goods" are supplies such as food, water, medicine, etc. that users prepare in advance in preparation for disasters.

[0925] A "notification" is a message containing information, warnings, or suggestions sent by the system to a user.

[0926] "Link" means a hypertext link that a user can click to go directly to related information or a purchasing page.

[0927] "Vendors" are businesses that specialize in providing disaster-related goods and services.

[0928] A "form" is an interface on the web or in an application that allows a user to enter information.

[0929] "Emotion" refers to the user's psychological state, and includes emotions such as joy, sadness, anxiety, and stress.

[0930] "Countermeasures" refer to specific actions and preparations that users should take to prepare for disasters.

[0931] "Analysis" is the process of performing calculations and analysis based on collected data to derive meaningful results.

[0932] "Calculation" is the process of quantifying collected data and using algorithms to perform specific risk assessments.

[0933] The present invention relates to a system for disaster risk analysis and stockpile management, and furthermore, by adjusting notification content and the priority of countermeasures based on the user's emotional state, it is possible to provide optimal countermeasures according to the user's needs.

[0934] Data collection methods

[0935] server

[0936] The server periodically collects data on earthquakes, tsunamis, heavy rains, etc. This data is retrieved from reliable data sources on the Internet using Python libraries (Requests and BeautifulSoup), including government APIs and satellite data.

[0937] Example: Obtain earthquake data from the Japan Meteorological Agency's API and store it in a database.

[0938] Data Analysis Methods

[0939] server

[0940] The server analyzes the collected data and calculates the disaster risk in the area where the user lives, using machine learning algorithms powered by TensorFlow.

[0941] Example: Predicting earthquake risk in a region by learning from past earthquake data.

[0942] Stockpile information input method

[0943] Terminal

[0944] The terminal provides an interface for users to input information about their home emergency supplies. Using a smartphone app (iOS: Swift, Android: Kotlin) or a web app (React.js), users can input the types, quantities, and expiration dates of emergency supplies such as food, water, and medicine.

[0945] Example prompt: "Please list your current stockpile items including their quantities and expiration dates."

[0946] Example: Enter "5 liters of water, expiration date: January 2024."

[0947] Stockpile information analysis method

[0948] server

[0949] The server stores the stockpile information sent by users in a database and analyzes the data using Pandas. It compares the data with disaster risk information to identify shortages of stockpile items.

[0950] Example: Comparing the stockpile list with disaster risk information and determining that "water stocks are insufficient."

[0951] Notification means

[0952] server

[0953] The server notifies users of shortages and necessary measures by sending push notifications using Firebase Cloud Messaging (FCM).

[0954] Example: "Water supplies are low. Please bring at least 3 liters of water."

[0955] Terminal

[0956] The terminal receives the notification from the server and displays the notification to the user in a pop-up or banner.

[0957] Purchase link provision method

[0958] Terminal

[0959] The terminal displays links to purchase the supplies in short supply (for example, online shopping sites) and a request form from a specialist supplier.

[0960] Example: Displaying a notification such as "Water stocks are low. You can purchase more at this link."

[0961] User

[0962] The user uses the displayed link to purchase the supplies they are missing.

[0963] Example: Clicking on a link in a notification to order water from an online shopping site.

[0964] Regular check method

[0965] server

[0966] The server uses Django's Celery to periodically check the expiration dates of stockpiled items and measures to address new risks.

[0967] Example: In summer, generate and send notifications such as "Please take precautions against heatstroke. Don't forget to replenish with water and salt."

[0968] Terminal

[0969] The terminal receives the periodic notification sent from the server and displays it to the user.

[0970] emotion recognition means

[0971] Terminal

[0972] The device collects the user's input speed and input content, and provides an interface to obtain emotion data. Emotions are estimated using a JavaScript library (TensorFlow.js).

[0973] Example prompt: "How do you feel after receiving the disaster risk notification? (e.g., worried, stressed)"

[0974] Example: Inferring "anxiety" from user input.

[0975] server

[0976] The server uses an emotion engine to analyze the emotion data sent from the terminal and recognize the user's emotional state.

[0977] Emotion-based notification adjustment

[0978] server

[0979] The server adjusts the notification content based on the emotion recognition results: if the user is feeling anxious, it generates a reassuring message.

[0980] Example: Generate and send a message such as, "We're sharing specific steps to help you stay safe. Please stay tuned for next steps."

[0981] Terminal

[0982] The terminal receives the adjusted notification and displays it to the user.

[0983] A method for adjusting countermeasure priorities according to emotions

[0984] server

[0985] The server adjusts the priority of the proposed measures according to the user's emotions. The emotion engine can utilize a generative AI model.

[0986] Example: "If a user feels anxious, we suggest they start with basic supplies of water and food."

[0987] Terminal

[0988] The terminal displays the adjusted measures to the user and encourages them to take specific actions.

[0989] This system allows users to receive disaster risk information quickly and appropriately, enabling them to take the most appropriate measures according to the situation. In addition, flexible notification adjustment and optimization of countermeasure priorities based on emotion recognition allow users to prepare for disasters with even greater peace of mind.

[0990] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0991] Program processing steps

[0992] Step 1:

[0993] A user launches a smartphone app or web app and enters the address where they live. The entered address information is sent to the server via the device.

[0994] Input: Address information (e.g. "Tokyo, Chiyoda-ku, Marunouchi 1-chome~")

[0995] Output: The address information is sent to the server and stored.

[0996] Step 2:

[0997] The server collects the data necessary to calculate the disaster risk for that area based on the received address information. The server obtains data on earthquakes, tsunamis, and heavy rain from the Japan Meteorological Agency and overseas satellite data providers.

[0998] Input: Address information

[0999] Output: Data required for disaster risk calculation (e.g., past earthquake data, tsunami impact area data, rainfall data)

[1000] Step 3:

[1001] The server analyzes the collected data using machine learning algorithms powered by TensorFlow to calculate the disaster risk level for each address.

[1002] Input: Disaster-related data

[1003] Output: Risk level (e.g., earthquake risk "high", tsunami risk "medium", heavy rain risk "low")

[1004] Step 4:

[1005] The server generates a notification message for the user based on the risk level, and the generated notification is sent to the user's device via Firebase Cloud Messaging (FCM).

[1006] Input: Risk Level

[1007] Output: Notification message (e.g. "Your area is at high risk of earthquakes")

[1008] Step 5:

[1009] The terminal receives the notification message sent from the server and displays it to the user in a pop-up or banner.

[1010] Input: Notification message

[1011] Output: Notification shown to the user (e.g., a popup saying "Your area is at high risk of earthquakes")

[1012] Step 6:

[1013] The user follows the instructions in the app to enter information about emergency supplies, which is then sent to the server via the device.

[1014] Input: Stockpile information (e.g., "5 liters of water, expiration date: January 2024")

[1015] Output: Stockpile information is sent to the server and saved.

[1016] Step 7:

[1017] The server analyzes the stockpile information sent by the user and cross-references the data using Pandas. It compares the data with disaster risk information to identify shortages of stockpile items.

[1018] Input: Stockpile information

[1019] Output: Information about the shortage of supplies (e.g., "Water supplies are running low")

[1020] Step 8:

[1021] The server creates links to purchase the stockpiles that are in short supply, as well as a request form for specialist vendors, and notifies the user.

[1022] Input: Missing stockpile information

[1023] Output: Notification message with a purchase link (e.g. "Your water supply is low. You can purchase more at this link")

[1024] Step 9:

[1025] The terminal receives the notification with the link sent from the server and displays it to the user.

[1026] Input: Notification message with purchase link

[1027] Output: The notification displayed to the user

[1028] Step 10:

[1029] The user uses the provided link to purchase the missing supplies, and once the purchase is complete, the information is updated and sent back to the server.

[1030] Input: Purchase link click and purchase information

[1031] Output: Updated stockpile information is sent to the server and stored.

[1032] Step 11:

[1033] The server uses Django's Celery to periodically check the expiration dates of users' stockpiles and new seasonal disaster risks, and generates notification messages.

[1034] Input: Stockpile information, seasonal countermeasure information

[1035] Output: Periodic notification message (e.g. "Summer preparation is necessary. Don't forget to replenish with water and salt.")

[1036] Step 12:

[1037] The terminal receives the periodic notification sent from the server and displays it to the user.

[1038] Input: Periodic notification message

[1039] Output: The notification displayed to the user

[1040] Step 13:

[1041] The device collects emotion data from the user's input speed and content, and uses a JavaScript library (TensorFlow.js) to estimate emotions.

[1042] Input: User-entered data

[1043] Output: Estimated emotion data (e.g., "anxiety")

[1044] Step 14:

[1045] The server uses an emotion engine to analyze the emotion data sent from the terminal and recognize the user's emotional state.

[1046] Input: Emotion data

[1047] Output: Emotional state (e.g., "I feel anxious")

[1048] Step 15:

[1049] The server adjusts the notification content based on the user's emotions, generating a reassuring message for users who are feeling particularly anxious.

[1050] Input: Emotional state

[1051] Output: A tailored notification message (e.g., "We'll provide you with specific steps to stay safe")

[1052] Step 16:

[1053] The terminal receives the adjusted notification and displays it to the user.

[1054] Input: The adjusted notification message

[1055] Output: The notification displayed to the user

[1056] Step 17:

[1057] The server proposes high-priority measures based on the user's emotions. The emotion engine uses a generative AI model to prioritize measures that require urgency.

[1058] Input: Emotional state

[1059] Output: A list of prioritized actions (e.g., "Secure water and food first.")

[1060] Step 18:

[1061] The terminal displays high-priority measures to the user and encourages them to take specific actions.

[1062] Input: Priority Measures List

[1063] Output: Notification with specific measures (e.g., "First, secure water and food.")

[1064] Through the above processing steps, users can receive prompt and accurate disaster risk information and countermeasure suggestions, and can take the most appropriate countermeasures according to the situation. In addition, flexible notification adjustment and countermeasure priority optimization based on emotion recognition allow users to prepare for disasters with even greater peace of mind.

[1065] (Application example 2)

[1066] 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."

[1067] Disaster risk analysis systems have a problem in that they do not take into account the user's emotional state, which means that users are unable to take appropriate measures while feeling anxious or stressed.In addition to notifying users of disaster risks and shortages of emergency supplies, there is a need for systems that can adjust the priority of measures based on emotions and send messages that provide users with a sense of security.

[1068] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting large amounts of data and satellite data related to disasters such as earthquakes, tsunamis, and heavy rain; means for analyzing the collected data and calculating the disaster risk in the area where the user resides; means for analyzing stockpile information entered by the user and identifying missing stockpile items based on the disaster risk; means for notifying the user of missing stockpile items and necessary countermeasures; means for providing links to purchase missing stockpile items and request forms to specialist vendors; means for periodically checking for new countermeasures based on the expiration date of stockpile items and the season and notifying the user; and means for recognizing the user's emotional state and adjusting the content of the notification and the priority of countermeasures based on the user's emotional state. This makes it possible to optimize the user's response to risk notifications by taking emotions into consideration.

[1069] markdown

[1070] A "disaster" is a destructive event caused by natural phenomena or human factors, including earthquakes, tsunamis, and heavy rains.

[1071] "Massive data" refers to a huge amount of information collected from various sources, and is also known as big data.

[1072] "Satellite data" refers to observation data obtained from satellites, including meteorological information and the state of the Earth's surface.

[1073] "Disaster risk" refers to an assessment of the likelihood of a disaster occurring in a particular region or environment and the magnitude of its impact.

[1074] "Stock information" refers to detailed information about food, water, first aid supplies, etc. that users have stored at home, in stores, etc.

[1075] "Missing stockpiles" indicate supplies that the user needs to prepare based on disaster risk but that are in short supply.

[1076] "Notification" refers to the act or means of providing information to a user, and includes push notifications, emails, and the like.

[1077] "Professional Service Request Form" refers to a formalized document or interface through which a user can submit a request to a professional service provider.

[1078] "Emotional state" indicates the user's psychological response and emotional state, and includes feelings such as anxiety and relief.

[1079] "Priority of measures" refers to the order of particularly important measures that should be implemented first among several measures.

[1080] markdown

[1081] This invention combines a disaster risk analysis system with an emotion engine. Specific embodiments will be described below.

[1082] System configuration

[1083] This system mainly consists of a server, a terminal, and a user.

[1084] server

[1085] The server is equipped with hardware and software to collect and analyze large amounts of data and satellite data to calculate disaster risk. The specific software used includes existing disaster models and machine learning algorithms for data analysis. It also analyzes stockpile information and identifies shortages based on disaster risk. Additionally, an emotion engine is used to recognize the user's emotional state and adjust notification content and countermeasure priorities accordingly.

[1086] For example, an emotion recognition model using TensorFlow can be used to quantify the user's emotional data. If the analysis results indicate that the user is feeling anxious, a message that provides reassurance can be generated and sent to the device.

[1087] Terminal

[1088] The terminal functions as an interface with the user and is typically a smartphone or tablet. The terminal receives notifications from the server and displays them to the user. It also has the function of collecting emergency supply information entered by the user and sending it to the server. Information is provided to the user on the terminal in the form of push notifications, email notifications, pop-ups, etc.

[1089] For example, when a user enters their home address, the server sends the disaster risk information for that area to the device, and the user is notified that "the earthquake risk in your area is high."

[1090] User

[1091] Users use the application to input their personal information, such as address and emergency supplies, and the system analyzes the data. It also estimates the user's emotional state based on their reactions and input speed.

[1092] Specific examples

[1093] As an example of how all the components work together, the following procedure can be considered:

[1094] 1. The user launches the smartphone app and enters the address where they live. For example, they enter a specific address such as "Shibuya-ku, Tokyo."

[1095] 2. The server collects and analyzes disaster data for the area based on the entered address information, and sends the results to the terminal.

[1096] 3. The device notifies the user of the disaster risk received as a result of the analysis. A message such as "The tsunami risk in your area is high" is displayed.

[1097] 4. The user inputs stockpile information and is notified of specific shortages of stockpile items, such as "not enough water."

[1098] 5. The server uses an emotion engine to analyze the user's emotional state and, for example, if the user is feeling "anxious," generates a tailored notification such as, "Don't worry, you can purchase the supplies you need from the link below."

[1099] Prompt Sentence Examples

[1100] Using the user's address and emotion data as input, generate appropriate notification content based on disaster risk and emotion.

[1101] Address: Shibuya Ward, Tokyo

[1102] Emotional data: Anxiety

[1103] Disaster data: Earthquake risk: High, Flood risk: Medium, Tsunami risk: Low

[1104] In this way, by combining an emotion engine, the present invention realizes a system that provides disaster countermeasures that take into account the emotional state of the user. This system allows users to take appropriate measures according to their own emotional state, thereby increasing their sense of security in the event of a disaster.

[1105] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1106] markdown

[1107] Step 1:

[1108] A user launches the smartphone app and enters the address where they live. Specifically, by entering address information such as "Shibuya-ku, Tokyo" into the app's input screen, the server uses this as base data for collecting disaster data for that area. The input data is in text format, and the input text data is sent to the server as output.

[1109] Step 2:

[1110] The server collects disaster data for the area based on the address information received from the user. This collection process involves retrieving data from an external API. Specifically, it sends a GET request to the specified API endpoint (e.g., "https: / / disasterdataapi.com / shibuya") to retrieve risk data such as earthquakes, tsunamis, and heavy rain. The input is the user's address, and the output is the retrieved disaster data object.

[1111] Step 3:

[1112] The server analyzes the collected disaster data and calculates the disaster risk for the area. This analysis uses machine learning algorithms and existing disaster models. For example, it calculates a risk score based on past earthquake data. The input is a disaster data object, and the output is a calculated risk score object.

[1113] Step 4:

[1114] The server receives and analyzes emergency supply information entered by the user. When a user enters emergency supply information such as "water," "emergency food," and "batteries" on their smartphone, the information is sent to the server. The input is emergency supply information text, and the output is an emergency supply data object.

[1115] Step 5:

[1116] The server identifies shortages of stockpiles based on disaster risk. This identification process uses an algorithm that compares risk scores with the user's stockpile information. For example, analysis is performed according to rules such as "if the tsunami risk is high, more water is needed." The inputs are a risk score object and a stockpile data object, and the output is a list of stockpiles that are in short supply.

[1117] Step 6:

[1118] The server notifies the user of shortages and necessary measures. Specifically, it sends a message such as "Water stocks are low" to the user using push notifications or email notifications. This notification is sent to the device in JSON format and displayed on the device. The input is a list of shortages of supplies, and the output is a notification object.

[1119] Step 7:

[1120] The server analyzes the user's input speed and content to recognize the user's emotional state. It uses an emotion recognition model such as TensorFlow. The input is the user's input data, and the output is an emotional state object.

[1121] Step 8:

[1122] The server adjusts the notification content and priority of countermeasures based on the recognized emotional state. For example, if the user is feeling anxious, it generates a message such as "Don't worry, you can purchase the necessary supplies from the link below." The input is an emotional state object, and the output is the adjusted notification object.

[1123] Step 9:

[1124] The device receives the tailored notification from the server and displays it to the user. It is displayed as a pop-up or banner notification on the device screen. A specific example of this is a notification such as "Your area is at high risk of earthquakes. Water supplies are running low. Don't worry, you can purchase supplies from the link below." The input is the tailored notification object, and the output is the displayed notification.

[1125] 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.

[1126] 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.

[1127] 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.

[1128] [Third embodiment]

[1129] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1130] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1131] 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).

[1132] 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.

[1133] 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.

[1134] 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).

[1135] 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.

[1136] 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.

[1137] 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.

[1138] 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.

[1139] 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.

[1140] 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."

[1141] The present invention relates to a disaster risk analysis and countermeasure proposal system. This system operates in cooperation with a server, terminals, and users to analyze the disaster risk in the area where the user lives and propose appropriate countermeasures and emergency supplies.

[1142] System Operation Overview

[1143] Data collection methods

[1144] server

[1145] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rains, etc. This data is used as basic data for disaster risk analysis. The data is obtained from reliable data sources via the Internet and stored in a database.

[1146] Data Analysis Methods

[1147] server

[1148] The server analyzes the collected data and calculates the disaster risk for the area the user lives in. The analysis is performed using existing disaster models and machine learning algorithms. For example, it predicts the risk level for the area based on data from past earthquakes and tsunamis.

[1149] Stockpile information input method

[1150] Terminal

[1151] The device provides an interface for users to input information about emergency supplies stored at home. Users can enter this information using a smartphone app or a web app. Input items include the type and quantity of emergency supplies, as well as their expiration date.

[1152] Stockpile information analysis method

[1153] server

[1154] The server identifies shortages based on the stockpile information entered by the user, and analyses it against disaster risk assessments to determine whether stockpiles are sufficient for high-risk disasters.

[1155] Notification means

[1156] server

[1157] The server notifies users of shortages of supplies and necessary measures via push notifications or email. For example, it may send a specific message such as, "Your water supply is low."

[1158] Terminal

[1159] The device receives notifications from the server and displays them to the user. When a notification is received, the user is notified via a pop-up or banner.

[1160] Purchase link provision method

[1161] Terminal

[1162] The device provides links to purchase supplies that are in short supply and a request form to specialists, allowing users to easily purchase needed supplies or request assistance from specialists within the app.

[1163] User

[1164] Users can view notifications and click links to purchase supplies they need, such as ordering water or food directly from within the app.

[1165] Regular check method

[1166] server

[1167] The server periodically checks expiration dates of stockpiled items and measures to address new seasonal risks, such as heatstroke prevention measures in summer and cold weather measures in winter.

[1168] Terminal

[1169] The device receives periodic notifications sent from the server and displays them to the user, allowing the user to periodically update their stockpiles and take new measures.

[1170] Specific example of system operation

[1171] Risk notification after entering address

[1172] 1. User: Launches the app and enters their address.

[1173] 2. Terminal: Sends address information to the server.

[1174] 3. Server: Analyzes the disaster risk in the area based on the address information and sends the results to the device.

[1175] 4. Device: Notify the user that "There is a high risk of tsunami in your area."

[1176] Check for shortages of emergency supplies and purchase them

[1177] 1. User: Enters current stockpile list into the app.

[1178] 2. Terminal: Sends stockpile information to the server.

[1179] 3. Server: Identify shortages of supplies based on disaster risk information.

[1180] 4. On the device: Notify the user that their water supply is low and provide a link to purchase water.

[1181] 5. User: Clicks on the link to purchase the scarce water.

[1182] In this way, the present invention provides a system that combines multiple means to support users in taking effective disaster countermeasures, allowing users to take appropriate disaster countermeasures themselves and reducing the burden on government agencies.

[1183] The processing flow will be explained below.

[1184] Step 1: Data collection

[1185] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rain, etc. from reliable data sources. This data is stored in a database as basic data for disaster risk analysis.

[1186] Step 2: Enter user information

[1187] Users launch the app and enter initial information such as their address, family composition, and current list of emergency supplies.

[1188] The terminal receives the information entered by the user, checks the format, and then sends it to the server.

[1189] Step 3: Risk analysis

[1190] The server calculates the disaster risk for the area based on the address information sent by the user, analyzes collected big data and satellite data, and evaluates the risk level.

[1191] Step 4: Risk notification

[1192] Based on the results of the risk analysis, the server generates risk information customized for each user and transmits this information to the terminal.

[1193] The device will notify the user of the received risk information via pop-ups or notification banners.

[1194] Step 5: Gather information on emergency supplies

[1195] Users use a smartphone app or web app to enter information about the emergency supplies they have at home.

[1196] The terminal receives the stockpile information and transmits it to the server.

[1197] Step 6: Stockpile information analysis

[1198] The server evaluates what is lacking based on the stockpile information entered by the user, compares it with disaster risk, and generates a list of stockpiles that are lacking.

[1199] Step 7: Stockpile shortage notification

[1200] The server generates data to notify users about shortages of stockpiles and necessary measures, and sends this data to the terminal.

[1201] The device will then display the received notification to the user, for example, a message saying "Water stocks are running low."

[1202] Step 8: Provide a purchase link

[1203] The device displays links to purchase supplies that are in short supply and a request form to contact a specialist supplier.

[1204] Users can click on the link to purchase the supplies they need.

[1205] Step 9: Regular checks

[1206] The server regularly checks the expiration dates of stockpiled goods and measures to be taken in response to new seasonal risks.

[1207] If the user needs new measures, the server generates the information and sends it to the terminal.

[1208] Step 10: Periodic Notifications

[1209] The terminal receives the periodic notifications sent from the server and displays them to the user, who can then check the notifications and take the necessary action.

[1210] In this way, the system supports users in taking effective disaster prevention measures through each step.

[1211] Example 1

[1212] 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."

[1213] In disaster risk analysis and countermeasure proposals, there is a lack of systems that can accurately assess the specific risks in the area where a user lives and identify shortages of stockpiles based on that assessment.In addition, there is a lack of notifications and purchasing links that allow users to take effective disaster countermeasures, making it difficult to respond quickly and appropriately.

[1214] 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.

[1215] In this invention, the server includes means for collecting large-scale data and satellite data related to disasters such as earthquakes, tsunamis, and heavy rains, means for analyzing the collected data and calculating the disaster risk in the area where the user lives, and means for collecting emergency stockpile information entered by the user. This makes it possible to evaluate the specific disaster risk in the area where the user lives, identify emergency stockpile shortages based on the evaluation, and provide the user with prompt and appropriate countermeasures.

[1216] A "disaster" is an event that includes adverse effects caused by natural phenomena such as earthquakes, tsunamis, and heavy rains.

[1217] "Big data" refers to a vast amount of information, also known as big data, and includes data related to natural disasters and satellite image data.

[1218] "Satellite data" refers to observation data obtained from artificial satellites, including meteorological and topographical information.

[1219] "Analysis" refers to the process of organizing, calculating, and evaluating collected data to arrive at a specific conclusion.

[1220] "Disaster risk" refers to an assessment value that indicates the possibility of a disaster occurring in a particular area and the extent of damage caused by that disaster.

[1221] "Stockpiles" refer to supplies stored in advance in preparation for emergencies such as disasters. These include food, water, medicine, etc.

[1222] "Notification" means a message or alert intended to inform the User of important information.

[1223] "Purchase Link" means a direct link to an online shopping site provided to enable Users to easily purchase shortage supplies.

[1224] "Vendor Request Form" means an electronic form for entering and submitting a request to a vendor for a particular service or supply.

[1225] "Expiration date" refers to the date by which stockpiled items are recommended for use, after which their quality may deteriorate.

[1226] "Regularly" means repeated at regular intervals, including monthly, seasonal, and other cycles.

[1227] "Residence information" refers to information about the area and address where the user lives.

[1228] MODE FOR CARRYING OUT THE INVENTION

[1229] The present invention relates to a system for disaster risk analysis and countermeasure proposals. This system operates in cooperation with a server, terminals, and users to analyze the disaster risk in the area where the user resides and propose appropriate countermeasures and stockpiles based on the analysis. This specification describes a specific implementation method of the system.

[1230] Hardware and software used

[1231] server

[1232] The server is the central component that collects and analyzes large-scale data related to earthquakes, tsunamis, heavy rain, and other events, as well as satellite data. Data is collected from reliable data sources via the Internet. Specifically, data is obtained from APIs such as those of the Japan Meteorological Agency and NASA, and the data is stored in a MySQL database. Machine learning algorithms using Python's Scikit-Learn library are used for data analysis.

[1233] Terminal

[1234] The terminal provides an interface for users to input emergency stockpile information and receive notifications. The smartphone app was created with React Native and features a user-friendly input form and notification function. The terminal also receives data sent from the server and displays alerts to the user.

[1235] User

[1236] Users use the app to enter their information, including their address and a list of supplies, and view notifications and suggestions from the system.

[1237] System Operation Overview

[1238] Data collection and analysis

[1239] server

[1240] The server periodically collects large-scale data on natural disasters and satellite data, storing it in a MySQL database. The data is analyzed using machine learning algorithms using Python's Scikit-Learn to calculate disaster risk for each region. This analysis then predicts risk levels, such as the probability of earthquakes and tsunamis occurring.

[1241] Management of emergency stockpile information

[1242] Terminal

[1243] The terminal provides a form for users to enter stockpile information, which is then sent over the Internet to a server. The data is encrypted using the HTTPS protocol, ensuring secure transmission.

[1244] server

[1245] The server stores the received stockpile information in a database, compares it with disaster risk information, and identifies shortages of stockpile items. From the analysis results, specific information such as "water stocks are insufficient" is generated.

[1246] User Notifications and Actions

[1247] server

[1248] The server then sends notifications to users based on the analysis results, including messages such as "Tsunami risk in your area is increasing. Water reserves are running low," and these notifications are sent to devices via a push notification API.

[1249] Terminal

[1250] The device receives notifications sent from the server and displays them to the user, often as popups or banners within the app, allowing the user to take any necessary action immediately.

[1251] User

[1252] Users can click on the purchase link in the notification to purchase the supplies they need online, for example, by going directly to an e-commerce site such as Amazon or Rakuten Ichiba and ordering the supplies they need.

[1253] Regular checks and update notifications

[1254] server

[1255] The server periodically checks the expiration dates and seasonal risks of stockpiled items. The results of the check are notified to the user. For example, the server may notify the user that "The expiration date of the stockpiled food is approaching. Please purchase new stockpiles."

[1256] Terminal

[1257] The terminal receives periodic update notifications sent from the server and displays them to the user, allowing the user to periodically update their stockpiles and take new measures.

[1258] Prompt Sentence Examples

[1259] 1. "I would like to know the disaster risk in the area where I live. I live in Shibuya Ward, Tokyo."

[1260] 2. "I just typed in the list of food items I have stored at home. Is this list sufficient for disaster preparedness?"

[1261] 3. "Please let me know if there is anything missing from our summer stockpile."

[1262] 4. "I'd like to purchase water within the app. Can you provide a link?"

[1263] The above is an embodiment of the present invention. This system allows users to take effective disaster countermeasures and reduces the burden on government agencies.

[1264] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1265] Step 1: Data collection

[1266] server

[1267] The server collects large-scale disaster-related data and satellite data at a specified time every day. The data is acquired using APIs from, for example, the Japan Meteorological Agency or NASA. The API endpoint and authentication information of each data source are used as input, and the acquired data is stored in the server's MySQL database as output. Specifically, the data acquisition script is executed periodically using the Python requests library.

[1268] Step 2: Data analysis

[1269] server

[1270] The server analyzes the acquired dataset and calculates the disaster risk for each region. The data collected in step 1 is used as input, and the output is an assessment value indicating the risk level for each region. Specifically, a machine learning algorithm using Python's Scikit-Learn library processes the data and performs a risk assessment. The analysis results are then stored in a MySQL database.

[1271] Step 3: Enter emergency supply information

[1272] Terminal

[1273] The terminal provides an interface for users to input emergency supply information. Input includes the type, quantity, and expiration date of emergency supplies, which the user enters through the app. This data is sent to the server as output. Specifically, a form created with React Native is displayed, and the information entered by the user is encrypted via HTTPS and sent to the server.

[1274] Step 4: Sending and receiving stockpile information

[1275] Terminal

[1276] The terminal sends the stockpile information entered by the user to the server in real time. The input includes the data entered by the user in the form, and this data is sent to the server as output. Specifically, the user enters stockpile information and clicks the "Send" button, and the data reaches the server.

[1277] server

[1278] The server receives the stockpile information sent from the terminal and stores it in a database. The input includes the stockpile information sent, and the output is information in a format that can be stored in the database. Specifically, it analyzes the received information and stores the data in the appropriate table.

[1279] Step 5: Analyze stockpile information

[1280] server

[1281] The server analyzes the received stockpile information and compares it with disaster risk information to identify shortages. Input includes stored stockpile information and the local risk level. The output generates a list of shortages. Specifically, a Python script retrieves the necessary information from the database and uses an analysis algorithm to identify shortages.

[1282] Step 6: Create and send notifications

[1283] server

[1284] The server creates and sends notifications based on shortages of stockpiles and disaster risks. The input includes the analysis results of stockpile information, and the output is a specific notification message. Specifically, the notification generation script creates the notification content based on the analysis results and sends it to the device via the push notification API.

[1285] Terminal

[1286] The device receives notifications from the server and displays them to the user. The input includes the notification message sent from the server, and the output is the notification displayed on the device screen. Specifically, the notification is displayed as a popup or banner within the app.

[1287] Step 7: Provide a purchase link and take action

[1288] Terminal

[1289] The device displays links to purchase supplies and a request form to specialist vendors for supplies that are in short supply. The input includes notification content from the server, and the output is a purchase link that is displayed to the user. Specifically, the link is displayed on a screen within the app, and the user can click it.

[1290] User

[1291] The user clicks on the purchase link in the notification to purchase the missing supplies. The input includes the displayed purchase link, and the output is access to an e-commerce site, etc. The specific operation is that the user clicks the link, a browser opens, and the purchase page is displayed.

[1292] Step 8: Regular checks and update notifications

[1293] server

[1294] The server periodically checks the expiration dates and seasonal risks of stockpiled items and notifies users of the results. The input includes stockpile information and time information in the database, and the output is an update notification message. Specifically, a periodic script scans the database and generates and sends the necessary notifications.

[1295] Terminal

[1296] The device receives periodic update notifications from the server and displays them to the user. The input includes the update notification message from the server, and the output is the update notification displayed on the device screen. Specifically, the notification is displayed as a pop-up or banner within the app, allowing the user to check it periodically.

[1297] In this way, by clarifying the input and output at each step and describing the specific operations, the operation of the entire system and its specific processing procedures become clear.

[1298] (Application example 1)

[1299] 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."

[1300] In modern society, large-scale natural disasters occur frequently, making it important for individuals and households to prepare appropriate emergency supplies. However, it is not easy for individuals to assess disaster risk and manage the necessary supplies themselves. In addition, there is a lack of systems for properly managing emergency food and other supplies needed in the event of a disaster, and for quickly replenishing supplies when shortages occur. This can result in insufficient supplies in emergencies, significantly impacting the lives of disaster victims. Furthermore, conventional systems do not integrate disaster risk assessment and emergency supply management, making it difficult to implement efficient countermeasures.

[1301] 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.

[1302] In this invention, the server includes means for collecting big data and satellite data related to disasters such as earthquakes, tsunamis, and heavy rains; means for analyzing the collected data and calculating the disaster risk in the user's area; and means for analyzing stockpile information entered by the user and identifying shortages of stockpile items based on the disaster risk. This enables the server to analyze the disaster risk in the user's area, manage the emergency food stockpile status in the event of a disaster, suggest emergency food items to the user, and enable easy ordering. Furthermore, by allowing the user to enter address information and receive confirmation of shortages of emergency food and other items according to the disaster risk and a link to purchase them, the server allows the user to quickly and reliably prepare stockpiles.

[1303] "Big data" refers to a large amount of data in a variety of formats, and is data that is so large that it is difficult to process using conventional data processing applications.

[1304] "Satellite data" refers to information obtained from Earth observation satellites and satellite images, and is data used to understand changes in terrain and climate.

[1305] "Disaster risk" is an index that evaluates the likelihood of natural disasters such as earthquakes, tsunamis, and heavy rains occurring and their impact.

[1306] "Stocked goods information" refers to information such as the type, quantity, and expiration date of disaster supplies that the user keeps in their own home or facility.

[1307] "Emergency food" refers to food that can be consumed immediately in the event of a disaster, can be stored for a long period of time, and is pre-cooked or can be eaten with simple cooking procedures.

[1308] A "notification" is the act of conveying warnings or information to a user, such as a message sent via push notification or email.

[1309] A "purchase link" is a hyperlink to a web page provided for users to quickly purchase the missing supplies.

[1310] The "specialist vendor request form" is an input form for a user to request the necessary goods or services from a specialist vendor.

[1311] The "best before" date is the date by which the quality of food is guaranteed, and after this date the taste and quality may deteriorate.

[1312] "Regular checks" are a process of checking the status of stockpiled goods and any new risks at regular intervals, and notifying users as necessary.

[1313] "Management of emergency food stockpiles in the event of a disaster" is the process of determining the types and quantities of emergency food that users possess and checking to see if there are any shortages.

[1314] "Easy ordering means" is a function that allows users to quickly and easily purchase shortages of stockpiled items through the application.

[1315] This invention relates to a system for disaster risk analysis and countermeasure proposals. This system, which operates in cooperation with a server, terminals, and users, analyzes the disaster risk in the user's residential area and proposes appropriate countermeasures and emergency supplies. It also has a function that allows users to input address information and emergency supply information, and provides a confirmation of shortages of emergency supplies and a link to purchase them.

[1316] Data collection methods

[1317] server

[1318] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rain, etc. This data is used as basic data for disaster risk analysis. The data is obtained from reliable data sources via the Internet and stored in a database. For example, data is collected from public disaster information APIs and satellite data provision services.

[1319] Data Analysis Methods

[1320] server

[1321] The server analyzes the collected data and calculates the disaster risk in the area where the user lives. The analysis uses existing disaster models and machine learning algorithms. For example, it predicts the risk level of the area based on data from past earthquakes and tsunamis, and notifies the user that "there is a high risk of tsunami in your area."

[1322] Stockpile information input method

[1323] Terminal

[1324] Users use a smartphone app or web app to input information about their home emergency supplies, including water, emergency food, batteries, etc., and are provided with an interface for entering information such as the type, quantity, and expiration date of each item.

[1325] Stockpile information analysis method

[1326] server

[1327] The server identifies shortages based on the stockpile information entered by the user. Analysis is performed against disaster risk assessments to confirm whether stockpiles are sufficient for high-risk disasters. For example, in areas with a high risk of tsunamis, the server will notify users that "water stockpiles are insufficient."

[1328] Notification means

[1329] Server and terminal

[1330] The server notifies users of shortages of supplies and necessary measures via push notifications and emails. Devices receive notifications from the server and have the ability to notify users via pop-ups and banners.

[1331] Purchase link provision method

[1332] Terminal

[1333] The device provides links to purchase supplies that are in short supply and a request form to specialists, allowing users to easily purchase the supplies they need or request assistance from specialists within the app.

[1334] Regular check method

[1335] Server and terminal

[1336] The server periodically checks expiration dates of stockpiled items and measures to respond to new seasonal risks, and notifies the user. The terminal has the function to receive periodic notifications sent from the server and display them to the user.

[1337] Example of a system

[1338] This system uses Python to write data analysis scripts and collects data from external APIs using the HTTP request module (requests library). It also uses the smtplib library to send emails. The system analyzes disaster risks in the user's area, identifies shortages of emergency supplies based on the results, and sends notifications, allowing users to take appropriate measures quickly.

[1339] Specific example prompt

[1340] Below are some example prompts to use when inputting a generative AI model:

[1341] You are the developer of a food delivery app. You want to incorporate a disaster risk analysis and countermeasure proposal system into this app. Design a disaster prevention function that works on the smartphone app based on the following specifications:

[1342] 1. Analyze disaster risk based on address information entered by the user.

[1343] 2. Analyze the stockpile information entered by the user and identify any shortages.

[1344] 3. Notify users via push notification or email about shortages and necessary precautions.

[1345] 4. Include a function to provide links to purchase supplies that are in short supply.

[1346] The output should include a detailed process flow and a description of the software and hardware used.

[1347] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1348] Step 1:

[1349] Data collection

[1350] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rain, etc. from reliable data sources via the Internet. This data includes information on past disasters and meteorological data. The server uses this data as basic data for assessing disaster risk and stores it in a database. The input is disaster data from an external API, and the output is the data to be analyzed that is stored in the server's database.

[1351] Step 2:

[1352] Data analysis

[1353] The server analyzes the collected data and calculates the disaster risk for the residential area based on the address information entered by the user. Existing disaster models and machine learning algorithms are used here. For example, the risk level for the area is predicted based on data from past earthquakes and tsunamis. The inputs are the collected data and the user's address information, and the output is the disaster risk assessment result for the area where the user lives.

[1354] Step 3:

[1355] Enter emergency stockpile information

[1356] Users use a smartphone app or web app to input information about their home emergency supplies. This includes the type, quantity, and expiration date of items such as water, emergency food, and batteries. The input is done by the user through the app interface. The input data is sent to the server and stored in a database as emergency supply information. The input is the emergency supply information entered by the user, and the output is the emergency supply information stored in the server's database.

[1357] Step 4:

[1358] Stockpile information analysis

[1359] The server identifies what is lacking based on the stockpile information entered by the user. This is done in conjunction with disaster risk assessments to ensure that stockpiles are sufficient for high-risk disasters. For example, in areas with a high risk of tsunamis, it identifies that "water stockpiles are insufficient." The inputs are stockpile information and disaster risk assessment results, and the output is a list of stockpiles that are lacking.

[1360] Step 5:

[1361] notification

[1362] The server notifies the user of the identified shortages and necessary measures. This notification is done via push notification or email. Based on the acquired information on shortages, a specific message is generated and sent to the user. The input is the information on shortages, and the output is the notification message sent to the user.

[1363] Step 6:

[1364] Purchase link provided

[1365] The device provides users with links to purchase supplies they are running low on and a request form to specialist suppliers. Users can easily purchase the supplies they need from within the app, allowing users to quickly and reliably replenish their supplies. The input is a list of supplies they are running low on, and the output is links to purchase the supplies and a request form.

[1366] Step 7:

[1367] Regular checks

[1368] The server periodically checks the expiration dates of stockpiled items and measures to be taken in response to new seasonal risks, and notifies the user. This allows users to continuously maintain appropriate disaster prevention measures. The input is stockpiled item information and seasonal data, and the output is notification messages as a result of the regular checks.

[1369] 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.

[1370] This invention combines an emotion engine with a disaster risk analysis and countermeasure proposal system. This system works in conjunction with a server, terminal, and user to analyze the disaster risk in the user's area and propose appropriate countermeasures and emergency supplies. It also has a function to recognize the user's emotions and adjust the notification content and countermeasure priority.

[1371] System Operation Overview

[1372] Data collection methods

[1373] server

[1374] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rain, etc. from reliable data sources. This data is stored in a database as basic data for disaster risk analysis.

[1375] Data Analysis Methods

[1376] server

[1377] The server analyzes the collected data and calculates the disaster risk for the area the user lives in. The analysis is performed using existing disaster models and machine learning algorithms. For example, it predicts the risk level for the area based on data from past earthquakes and tsunamis.

[1378] Stockpile information input method

[1379] Terminal

[1380] The device provides an interface for users to input information about emergency supplies stored at home. Users can enter this information using a smartphone app or a web app. Input items include the type and quantity of emergency supplies, as well as their expiration date.

[1381] Stockpile information analysis method

[1382] server

[1383] The server identifies shortages based on the stockpile information entered by the user, and analyses it against disaster risk assessments to determine whether stockpiles are sufficient for high-risk disasters.

[1384] Notification means

[1385] server

[1386] The server notifies users of shortages of supplies and necessary measures via push notifications or email. For example, it may send a specific message such as, "Your water supply is low."

[1387] Terminal

[1388] The device receives notifications from the server and displays them to the user. When a notification is received, the user is notified via a pop-up or banner.

[1389] Purchase link provision method

[1390] Terminal

[1391] The device provides links to purchase supplies that are in short supply and a request form to specialists, allowing users to easily purchase needed supplies or request assistance from specialists within the app.

[1392] User

[1393] Users can view notifications and click links to purchase supplies they need, such as ordering water or food directly from within the app.

[1394] Regular check method

[1395] server

[1396] The server periodically checks expiration dates of stockpiled items and measures to address new seasonal risks, such as heatstroke prevention measures in summer and cold weather measures in winter.

[1397] Terminal

[1398] The device receives periodic notifications sent from the server and displays them to the user, allowing the user to periodically update their stockpiles and take new measures.

[1399] emotion recognition means

[1400] Terminal

[1401] The device provides an interface for recognizing emotions from user input and behavior, for example, estimating emotions from the user's input speed and content.

[1402] server

[1403] The server uses an emotion engine to analyze the data sent from the terminal and recognize the user's emotional state.

[1404] Emotion-based notification adjustment

[1405] server

[1406] The server then adjusts the notification content based on the perceived emotion. For example, if the user is feeling anxious, it will suggest a reassuring message or suggest measures.

[1407] Terminal

[1408] The terminal receives the adjusted notification and displays it to the user.

[1409] A method for adjusting countermeasure priorities according to emotions

[1410] server

[1411] The server adjusts the priority of suggested supplies and measures according to the user's emotions. For example, if the user is feeling stressed, it will prioritize suggested measures that require urgent action.

[1412] Terminal

[1413] The device displays the adjusted measures to the user and encourages them to take specific actions.

[1414] Specific example of system operation

[1415] Risk notification after entering address

[1416] 1. User: Launches the app and enters their address.

[1417] 2. Terminal: Sends address information to the server.

[1418] 3. Server: Analyzes the disaster risk in the area based on the address information and sends the results to the device.

[1419] 4. Device: Notify the user that "There is a high risk of tsunami in your area."

[1420] Check for shortages of emergency supplies and purchase them

[1421] 1. User: Enters current stockpile list into the app.

[1422] 2. Terminal: Sends stockpile information to the server.

[1423] 3. Server: Identify shortages of supplies based on disaster risk information.

[1424] 4. On the device: Notify the user that their water supply is low and provide a link to purchase water.

[1425] 5. User: Clicks on the link to purchase the scarce water.

[1426] Emotion recognition and response

[1427] 1. User: Receives notifications of disaster risks and shortages of emergency supplies, and inputs and takes action within the app.

[1428] 2. Terminal: Collects emotional data from the user's input speed, input content, etc.

[1429] 3. Server: Analyzes the collected emotional data and recognizes that the user is feeling anxious.

[1430] 4. Server: Generates a reassuring message based on the user's emotions and sends it to the device.

[1431] 5. Device: Display a message to the user saying, "Here are some measures you need to take to ensure your safety," and suggest specific measures.

[1432] In this way, by combining an emotion engine, the present invention realizes a system that provides disaster countermeasures that better meet user needs. This system allows users to take appropriate measures according to their own emotional state, increasing their sense of security in the event of a disaster.

[1433] The processing flow will be explained below.

[1434] Step 1: Data collection

[1435] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rain, etc. from reliable data sources. This data is stored in a database as basic data for disaster risk analysis.

[1436] Step 2: Enter user information

[1437] Users launch the app and enter initial information such as their address, family composition, and current list of emergency supplies.

[1438] The terminal receives the information entered by the user, checks the format, and then sends it to the server.

[1439] Step 3: Risk analysis

[1440] The server calculates the disaster risk for the area based on the address information sent by the user, and evaluates the risk level using collected big data and satellite data.

[1441] Step 4: Risk notification

[1442] The server creates the results of the risk analysis as risk information customized for each user and sends it to the terminal.

[1443] The device will notify the user of the received risk information via pop-ups or notification banners.

[1444] Step 5: Gather information on emergency supplies

[1445] Users use a smartphone app or web app to enter information about their home emergency supplies.

[1446] The terminal receives the stockpile information and transmits it to the server.

[1447] Step 6: Stockpile information analysis

[1448] The server evaluates what is lacking based on the stockpile information entered by the user, and creates a list of stockpiles that are lacking in accordance with the disaster risk assessment.

[1449] Step 7: Emotion Recognition

[1450] The device collects user input and operation data and generates data to estimate the user's emotional state.

[1451] The server recognizes the user's emotional state using an emotion engine based on the data sent from the terminal.

[1452] Step 8: Adjust notifications based on emotion

[1453] The server tailors the notification content based on the perceived emotion, for example generating a reassuring message if the user is feeling anxious.

[1454] The server sends the adjusted notification content to the terminal.

[1455] The device will then display the received notification to the user, for example, "Here are some steps you need to take to ensure your safety."

[1456] Step 9: Stockpile Shortage Notification

[1457] The server creates information to inform the user about shortages of stockpiled items and necessary measures, and sends it to the terminal.

[1458] The device displays the received notification to the user, for example, displaying a message saying "Water stocks are low."

[1459] Step 10: Provide a purchase link

[1460] The device displays links to purchase supplies that are in short supply and a request form to contact a specialist supplier.

[1461] Users click on a link to purchase the supplies they need.

[1462] Step 11: Regular checks and notifications

[1463] The server regularly checks the expiration dates of stockpiled goods and measures to be taken in response to new seasonal risks.

[1464] If the user needs new measures, the server generates the information and sends it to the terminal.

[1465] The device displays the received periodic notifications to the user, who can then check the notifications and take any necessary action.

[1466] In this way, the system supports users through each step to effectively implement disaster countermeasures. By combining this system with an emotion engine, it can provide responses that are in line with the user's emotional state, increasing a sense of security in the event of a disaster.

[1467] Example 2

[1468] 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."

[1469] In modern society, natural disasters such as earthquakes, tsunamis, and heavy rains occur frequently, making it important to prepare promptly and appropriately. However, the wide variety of disaster-related information makes it difficult for ordinary users to effectively utilize this information and prepare appropriate emergency supplies. Furthermore, advanced data analysis is required to accurately assess disaster risk and take necessary measures. Furthermore, notifications and countermeasure suggestions that ignore the user's emotional state present a challenge, making it difficult to alleviate the anxiety and stress felt by users.

[1470] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting disaster-related data, means for analyzing the collected data and calculating the disaster risk in the user's area, means for analyzing stockpile information entered by the user and identifying stockpile shortages based on the disaster risk, means for notifying the user of stockpile shortages and necessary countermeasures, means for providing links to purchase the stockpile shortages and request forms to vendors, means for periodically checking stockpile expiration dates and new countermeasures according to the season and notifying the user, and means for recognizing the user's emotions and adjusting the notification content and countermeasure priority. This allows the user to receive accurate and timely disaster risk information and countermeasure suggestions, and further enables flexible countermeasures according to the user's emotional state.

[1471] A "disaster" is an emergency situation caused by natural phenomena, such as an earthquake, tsunami, or heavy rain.

[1472] "Data" refers to information in the form of numbers, images, text, etc., related to earthquakes, tsunamis, and heavy rain, including disaster-related information.

[1473] A "server" is a central computer that collects, analyzes, and stores data.

[1474] "User" means an individual or organization that uses the system to prepare for a disaster.

[1475] "Stocked goods" are supplies such as food, water, medicine, etc. that users prepare in advance in preparation for disasters.

[1476] A "notification" is a message containing information, warnings, or suggestions sent by the system to a user.

[1477] "Link" means a hypertext link that a user can click to go directly to related information or a purchasing page.

[1478] "Vendors" are businesses that specialize in providing disaster-related goods and services.

[1479] A "form" is an interface on the web or in an application that allows a user to enter information.

[1480] "Emotion" refers to the user's psychological state, and includes emotions such as joy, sadness, anxiety, and stress.

[1481] "Countermeasures" refer to specific actions and preparations that users should take to prepare for disasters.

[1482] "Analysis" is the process of performing calculations and analysis based on collected data to derive meaningful results.

[1483] "Calculation" is the process of quantifying collected data and using algorithms to perform specific risk assessments.

[1484] The present invention relates to a system for disaster risk analysis and stockpile management, and furthermore, by adjusting notification content and the priority of countermeasures based on the user's emotional state, it is possible to provide optimal countermeasures according to the user's needs.

[1485] Data collection methods

[1486] server

[1487] The server periodically collects data on earthquakes, tsunamis, heavy rains, etc. This data is retrieved from reliable data sources on the Internet using Python libraries (Requests and BeautifulSoup), including government APIs and satellite data.

[1488] Example: Obtain earthquake data from the Japan Meteorological Agency's API and store it in a database.

[1489] Data Analysis Methods

[1490] server

[1491] The server analyzes the collected data and calculates the disaster risk in the area where the user lives, using machine learning algorithms powered by TensorFlow.

[1492] Example: Predicting earthquake risk in a region by learning from past earthquake data.

[1493] Stockpile information input method

[1494] Terminal

[1495] The terminal provides an interface for users to input information about their home emergency supplies. Using a smartphone app (iOS: Swift, Android: Kotlin) or a web app (React.js), users can input the types, quantities, and expiration dates of emergency supplies such as food, water, and medicine.

[1496] Example prompt: "Please list your current stockpile items including their quantities and expiration dates."

[1497] Example: Enter "5 liters of water, expiration date: January 2024."

[1498] Stockpile information analysis method

[1499] server

[1500] The server stores the stockpile information sent by users in a database and analyzes the data using Pandas. It compares the data with disaster risk information to identify shortages of stockpile items.

[1501] Example: Comparing the stockpile list with disaster risk information and determining that "water stocks are insufficient."

[1502] Notification means

[1503] server

[1504] The server notifies users of shortages and necessary measures by sending push notifications using Firebase Cloud Messaging (FCM).

[1505] Example: "Water supplies are low. Please bring at least 3 liters of water."

[1506] Terminal

[1507] The terminal receives the notification from the server and displays the notification to the user in a pop-up or banner.

[1508] Purchase link provision method

[1509] Terminal

[1510] The terminal displays links to purchase the supplies in short supply (for example, online shopping sites) and a request form from a specialist supplier.

[1511] Example: Displaying a notification such as "Water stocks are low. You can purchase more at this link."

[1512] User

[1513] The user uses the displayed link to purchase the supplies they are missing.

[1514] Example: Clicking on a link in a notification to order water from an online shopping site.

[1515] Regular check method

[1516] server

[1517] The server uses Django's Celery to periodically check the expiration dates of stockpiled items and measures to address new risks.

[1518] Example: In summer, generate and send notifications such as "Please take precautions against heatstroke. Don't forget to replenish with water and salt."

[1519] Terminal

[1520] The terminal receives the periodic notification sent from the server and displays it to the user.

[1521] emotion recognition means

[1522] Terminal

[1523] The device collects the user's input speed and input content, and provides an interface to obtain emotion data. Emotions are estimated using a JavaScript library (TensorFlow.js).

[1524] Example prompt: "How do you feel after receiving the disaster risk notification? (e.g., worried, stressed)"

[1525] Example: Inferring "anxiety" from user input.

[1526] server

[1527] The server uses an emotion engine to analyze the emotion data sent from the terminal and recognize the user's emotional state.

[1528] Emotion-based notification adjustment

[1529] server

[1530] The server adjusts the notification content based on the emotion recognition results: if the user is feeling anxious, it generates a reassuring message.

[1531] Example: Generate and send a message such as, "We're sharing specific steps to help you stay safe. Please stay tuned for next steps."

[1532] Terminal

[1533] The terminal receives the adjusted notification and displays it to the user.

[1534] A method for adjusting countermeasure priorities according to emotions

[1535] server

[1536] The server adjusts the priority of the proposed measures according to the user's emotions. The emotion engine can utilize a generative AI model.

[1537] Example: "If a user feels anxious, we suggest they start with basic supplies of water and food."

[1538] Terminal

[1539] The terminal displays the adjusted measures to the user and encourages them to take specific actions.

[1540] This system allows users to receive disaster risk information quickly and appropriately, enabling them to take the most appropriate measures according to the situation. In addition, flexible notification adjustment and optimization of countermeasure priorities based on emotion recognition allow users to prepare for disasters with even greater peace of mind.

[1541] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1542] Program processing steps

[1543] Step 1:

[1544] A user launches a smartphone app or web app and enters the address where they live. The entered address information is sent to the server via the device.

[1545] Input: Address information (e.g. "Tokyo, Chiyoda-ku, Marunouchi 1-chome~")

[1546] Output: The address information is sent to the server and stored.

[1547] Step 2:

[1548] The server collects the data necessary to calculate the disaster risk for that area based on the received address information. The server obtains data on earthquakes, tsunamis, and heavy rain from the Japan Meteorological Agency and overseas satellite data providers.

[1549] Input: Address information

[1550] Output: Data required for disaster risk calculation (e.g., past earthquake data, tsunami impact area data, rainfall data)

[1551] Step 3:

[1552] The server analyzes the collected data using machine learning algorithms powered by TensorFlow to calculate the disaster risk level for each address.

[1553] Input: Disaster-related data

[1554] Output: Risk level (e.g., earthquake risk "high", tsunami risk "medium", heavy rain risk "low")

[1555] Step 4:

[1556] The server generates a notification message for the user based on the risk level, and the generated notification is sent to the user's device via Firebase Cloud Messaging (FCM).

[1557] Input: Risk Level

[1558] Output: Notification message (e.g. "Your area is at high risk of earthquakes")

[1559] Step 5:

[1560] The terminal receives the notification message sent from the server and displays it to the user in a pop-up or banner.

[1561] Input: Notification message

[1562] Output: Notification shown to the user (e.g., a popup saying "Your area is at high risk of earthquakes")

[1563] Step 6:

[1564] The user follows the instructions in the app to enter information about emergency supplies, which is then sent to the server via the device.

[1565] Input: Stockpile information (e.g., "5 liters of water, expiration date: January 2024")

[1566] Output: Stockpile information is sent to the server and saved.

[1567] Step 7:

[1568] The server analyzes the stockpile information sent by the user and cross-references the data using Pandas. It compares the data with disaster risk information to identify shortages of stockpile items.

[1569] Input: Stockpile information

[1570] Output: Information about the shortage of supplies (e.g., "Water supplies are running low")

[1571] Step 8:

[1572] The server creates links to purchase the stockpiles that are in short supply, as well as a request form for specialist vendors, and notifies the user.

[1573] Input: Missing stockpile information

[1574] Output: Notification message with a purchase link (e.g. "Your water supply is low. You can purchase more at this link")

[1575] Step 9:

[1576] The terminal receives the notification with the link sent from the server and displays it to the user.

[1577] Input: Notification message with purchase link

[1578] Output: The notification displayed to the user

[1579] Step 10:

[1580] The user uses the provided link to purchase the missing supplies, and once the purchase is complete, the information is updated and sent back to the server.

[1581] Input: Purchase link click and purchase information

[1582] Output: Updated stockpile information is sent to the server and stored.

[1583] Step 11:

[1584] The server uses Django's Celery to periodically check the expiration dates of users' stockpiles and new seasonal disaster risks, and generates notification messages.

[1585] Input: Stockpile information, seasonal countermeasure information

[1586] Output: Periodic notification message (e.g. "Summer preparation is necessary. Don't forget to replenish with water and salt.")

[1587] Step 12:

[1588] The terminal receives the periodic notification sent from the server and displays it to the user.

[1589] Input: Periodic notification message

[1590] Output: The notification displayed to the user

[1591] Step 13:

[1592] The device collects emotion data from the user's input speed and content, and uses a JavaScript library (TensorFlow.js) to estimate emotions.

[1593] Input: User-entered data

[1594] Output: Estimated emotion data (e.g., "anxiety")

[1595] Step 14:

[1596] The server uses an emotion engine to analyze the emotion data sent from the terminal and recognize the user's emotional state.

[1597] Input: Emotion data

[1598] Output: Emotional state (e.g., "I feel anxious")

[1599] Step 15:

[1600] The server adjusts the notification content based on the user's emotions, generating a reassuring message for users who are feeling particularly anxious.

[1601] Input: Emotional state

[1602] Output: A tailored notification message (e.g., "We'll provide you with specific steps to stay safe")

[1603] Step 16:

[1604] The terminal receives the adjusted notification and displays it to the user.

[1605] Input: The adjusted notification message

[1606] Output: The notification displayed to the user

[1607] Step 17:

[1608] The server proposes high-priority measures based on the user's emotions. The emotion engine uses a generative AI model to prioritize measures that require urgency.

[1609] Input: Emotional state

[1610] Output: A list of prioritized actions (e.g., "Secure water and food first.")

[1611] Step 18:

[1612] The terminal displays high-priority measures to the user and encourages them to take specific actions.

[1613] Input: Priority Measures List

[1614] Output: Notification with specific measures (e.g., "First, secure water and food.")

[1615] Through the above processing steps, users can receive prompt and accurate disaster risk information and countermeasure suggestions, and can take the most appropriate countermeasures according to the situation. In addition, flexible notification adjustment and countermeasure priority optimization based on emotion recognition allow users to prepare for disasters with even greater peace of mind.

[1616] (Application example 2)

[1617] 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."

[1618] Disaster risk analysis systems have a problem in that they do not take into account the user's emotional state, which means that users are unable to take appropriate measures while feeling anxious or stressed.In addition to notifying users of disaster risks and shortages of emergency supplies, there is a need for systems that can adjust the priority of measures based on emotions and send messages that provide users with a sense of security.

[1619] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting large amounts of data and satellite data related to disasters such as earthquakes, tsunamis, and heavy rain; means for analyzing the collected data and calculating the disaster risk in the area where the user resides; means for analyzing stockpile information entered by the user and identifying missing stockpile items based on the disaster risk; means for notifying the user of missing stockpile items and necessary countermeasures; means for providing links to purchase missing stockpile items and request forms to specialist vendors; means for periodically checking for new countermeasures based on the expiration date of stockpile items and the season and notifying the user; and means for recognizing the user's emotional state and adjusting the content of the notification and the priority of countermeasures based on the user's emotional state. This makes it possible to optimize the user's response to risk notifications by taking emotions into consideration.

[1620] markdown

[1621] A "disaster" is a destructive event caused by natural phenomena or human factors, including earthquakes, tsunamis, and heavy rains.

[1622] "Massive data" refers to a huge amount of information collected from various sources, and is also known as big data.

[1623] "Satellite data" refers to observation data obtained from satellites, including meteorological information and the state of the Earth's surface.

[1624] "Disaster risk" refers to an assessment of the likelihood of a disaster occurring in a particular region or environment and the magnitude of its impact.

[1625] "Stock information" refers to detailed information about food, water, first aid supplies, etc. that users have stored at home, in stores, etc.

[1626] "Missing stockpiles" indicate supplies that the user needs to prepare based on disaster risk but that are in short supply.

[1627] "Notification" refers to the act or means of providing information to a user, and includes push notifications, emails, and the like.

[1628] "Professional Service Request Form" refers to a formalized document or interface through which a user can submit a request to a professional service provider.

[1629] "Emotional state" indicates the user's psychological response and emotional state, and includes feelings such as anxiety and relief.

[1630] "Priority of measures" refers to the order of particularly important measures that should be implemented first among several measures.

[1631] markdown

[1632] This invention combines a disaster risk analysis system with an emotion engine. Specific embodiments will be described below.

[1633] System configuration

[1634] This system mainly consists of a server, a terminal, and a user.

[1635] server

[1636] The server is equipped with hardware and software to collect and analyze large amounts of data and satellite data to calculate disaster risk. The specific software used includes existing disaster models and machine learning algorithms for data analysis. It also analyzes stockpile information and identifies shortages based on disaster risk. Additionally, an emotion engine is used to recognize the user's emotional state and adjust notification content and countermeasure priorities accordingly.

[1637] For example, an emotion recognition model using TensorFlow can be used to quantify the user's emotional data. If the analysis results indicate that the user is feeling anxious, a message that provides reassurance can be generated and sent to the device.

[1638] Terminal

[1639] The terminal functions as an interface with the user and is typically a smartphone or tablet. The terminal receives notifications from the server and displays them to the user. It also has the function of collecting emergency supply information entered by the user and sending it to the server. Information is provided to the user on the terminal in the form of push notifications, email notifications, pop-ups, etc.

[1640] For example, when a user enters their home address, the server sends the disaster risk information for that area to the device, and the user is notified that "the earthquake risk in your area is high."

[1641] User

[1642] Users use the application to input their personal information, such as address and emergency supplies, and the system analyzes the data. It also estimates the user's emotional state based on their reactions and input speed.

[1643] Specific examples

[1644] As an example of how all the components work together, the following procedure can be considered:

[1645] 1. The user launches the smartphone app and enters the address where they live. For example, they enter a specific address such as "Shibuya-ku, Tokyo."

[1646] 2. The server collects and analyzes disaster data for the area based on the entered address information, and sends the results to the terminal.

[1647] 3. The device notifies the user of the disaster risk received as a result of the analysis. A message such as "The tsunami risk in your area is high" is displayed.

[1648] 4. The user inputs stockpile information and is notified of specific shortages of stockpile items, such as "not enough water."

[1649] 5. The server uses an emotion engine to analyze the user's emotional state and, for example, if the user is feeling "anxious," generates a tailored notification such as, "Don't worry, you can purchase the supplies you need from the link below."

[1650] Prompt Sentence Examples

[1651] Using the user's address and emotion data as input, generate appropriate notification content based on disaster risk and emotion.

[1652] Address: Shibuya Ward, Tokyo

[1653] Emotional data: Anxiety

[1654] Disaster data: Earthquake risk: High, Flood risk: Medium, Tsunami risk: Low

[1655] In this way, by combining an emotion engine, the present invention realizes a system that provides disaster countermeasures that take into account the emotional state of the user. This system allows users to take appropriate measures according to their own emotional state, thereby increasing their sense of security in the event of a disaster.

[1656] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1657] markdown

[1658] Step 1:

[1659] A user launches the smartphone app and enters the address where they live. Specifically, by entering address information such as "Shibuya-ku, Tokyo" into the app's input screen, the server uses this as base data for collecting disaster data for that area. The input data is in text format, and the input text data is sent to the server as output.

[1660] Step 2:

[1661] The server collects disaster data for the area based on the address information received from the user. This collection process involves retrieving data from an external API. Specifically, it sends a GET request to the specified API endpoint (e.g., "https: / / disasterdataapi.com / shibuya") to retrieve risk data such as earthquakes, tsunamis, and heavy rain. The input is the user's address, and the output is the retrieved disaster data object.

[1662] Step 3:

[1663] The server analyzes the collected disaster data and calculates the disaster risk for the area. This analysis uses machine learning algorithms and existing disaster models. For example, it calculates a risk score based on past earthquake data. The input is a disaster data object, and the output is a calculated risk score object.

[1664] Step 4:

[1665] The server receives and analyzes emergency supply information entered by the user. When a user enters emergency supply information such as "water," "emergency food," and "batteries" on their smartphone, the information is sent to the server. The input is emergency supply information text, and the output is an emergency supply data object.

[1666] Step 5:

[1667] The server identifies shortages of stockpiles based on disaster risk. This identification process uses an algorithm that compares risk scores with the user's stockpile information. For example, analysis is performed according to rules such as "if the tsunami risk is high, more water is needed." The inputs are a risk score object and a stockpile data object, and the output is a list of stockpiles that are in short supply.

[1668] Step 6:

[1669] The server notifies the user of shortages and necessary measures. Specifically, it sends a message such as "Water stocks are low" to the user using push notifications or email notifications. This notification is sent to the device in JSON format and displayed on the device. The input is a list of shortages of supplies, and the output is a notification object.

[1670] Step 7:

[1671] The server analyzes the user's input speed and content to recognize the user's emotional state. It uses an emotion recognition model such as TensorFlow. The input is the user's input data, and the output is an emotional state object.

[1672] Step 8:

[1673] The server adjusts the notification content and priority of countermeasures based on the recognized emotional state. For example, if the user is feeling anxious, it generates a message such as "Don't worry, you can purchase the necessary supplies from the link below." The input is an emotional state object, and the output is the adjusted notification object.

[1674] Step 9:

[1675] The device receives the tailored notification from the server and displays it to the user. It is displayed as a pop-up or banner notification on the device screen. A specific example of this is a notification such as "Your area is at high risk of earthquakes. Water supplies are running low. Don't worry, you can purchase supplies from the link below." The input is the tailored notification object, and the output is the displayed notification.

[1676] 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.

[1677] 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.

[1678] 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.

[1679] [Fourth embodiment]

[1680] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1681] 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.

[1682] 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).

[1683] 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.

[1684] 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.

[1685] 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).

[1686] 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.

[1687] 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.

[1688] 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.

[1689] 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.

[1690] 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.

[1691] 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.

[1692] 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."

[1693] The present invention relates to a disaster risk analysis and countermeasure proposal system. This system operates in cooperation with a server, terminals, and users to analyze the disaster risk in the area where the user lives and propose appropriate countermeasures and emergency supplies.

[1694] System Operation Overview

[1695] Data collection methods

[1696] server

[1697] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rains, etc. This data is used as basic data for disaster risk analysis. The data is obtained from reliable data sources via the Internet and stored in a database.

[1698] Data Analysis Methods

[1699] server

[1700] The server analyzes the collected data and calculates the disaster risk for the area the user lives in. The analysis is performed using existing disaster models and machine learning algorithms. For example, it predicts the risk level for the area based on data from past earthquakes and tsunamis.

[1701] Stockpile information input method

[1702] Terminal

[1703] The device provides an interface for users to input information about emergency supplies stored at home. Users can enter this information using a smartphone app or a web app. Input items include the type and quantity of emergency supplies, as well as their expiration date.

[1704] Stockpile information analysis method

[1705] server

[1706] The server identifies shortages based on the stockpile information entered by the user, and analyses it against disaster risk assessments to determine whether stockpiles are sufficient for high-risk disasters.

[1707] Notification means

[1708] server

[1709] The server notifies users of shortages of supplies and necessary measures via push notifications or email. For example, it may send a specific message such as, "Your water supply is low."

[1710] Terminal

[1711] The device receives notifications from the server and displays them to the user. When a notification is received, the user is notified via a pop-up or banner.

[1712] Purchase link provision method

[1713] Terminal

[1714] The device provides links to purchase supplies that are in short supply and a request form to specialists, allowing users to easily purchase needed supplies or request assistance from specialists within the app.

[1715] User

[1716] Users can view notifications and click links to purchase supplies they need, such as ordering water or food directly from within the app.

[1717] Regular check method

[1718] server

[1719] The server periodically checks expiration dates of stockpiled items and measures to address new seasonal risks, such as heatstroke prevention measures in summer and cold weather measures in winter.

[1720] Terminal

[1721] The device receives periodic notifications sent from the server and displays them to the user, allowing the user to periodically update their stockpiles and take new measures.

[1722] Specific example of system operation

[1723] Risk notification after entering address

[1724] 1. User: Launches the app and enters their address.

[1725] 2. Terminal: Sends address information to the server.

[1726] 3. Server: Analyzes the disaster risk in the area based on the address information and sends the results to the device.

[1727] 4. Device: Notify the user that "There is a high risk of tsunami in your area."

[1728] Check for shortages of emergency supplies and purchase them

[1729] 1. User: Enters current stockpile list into the app.

[1730] 2. Terminal: Sends stockpile information to the server.

[1731] 3. Server: Identify shortages of supplies based on disaster risk information.

[1732] 4. On the device: Notify the user that their water supply is low and provide a link to purchase water.

[1733] 5. User: Clicks on the link to purchase the scarce water.

[1734] In this way, the present invention provides a system that combines multiple means to support users in taking effective disaster countermeasures, allowing users to take appropriate disaster countermeasures themselves and reducing the burden on government agencies.

[1735] The processing flow will be explained below.

[1736] Step 1: Data collection

[1737] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rain, etc. from reliable data sources. This data is stored in a database as basic data for disaster risk analysis.

[1738] Step 2: Enter user information

[1739] Users launch the app and enter initial information such as their address, family composition, and current list of emergency supplies.

[1740] The terminal receives the information entered by the user, checks the format, and then sends it to the server.

[1741] Step 3: Risk analysis

[1742] The server calculates the disaster risk for the area based on the address information sent by the user, analyzes collected big data and satellite data, and evaluates the risk level.

[1743] Step 4: Risk notification

[1744] Based on the results of the risk analysis, the server generates risk information customized for each user and transmits this information to the terminal.

[1745] The device will notify the user of the received risk information via pop-ups or notification banners.

[1746] Step 5: Gather information on emergency supplies

[1747] Users use a smartphone app or web app to enter information about the emergency supplies they have at home.

[1748] The terminal receives the stockpile information and transmits it to the server.

[1749] Step 6: Stockpile information analysis

[1750] The server evaluates what is lacking based on the stockpile information entered by the user, compares it with disaster risk, and generates a list of stockpiles that are lacking.

[1751] Step 7: Stockpile shortage notification

[1752] The server generates data to notify users about shortages of stockpiles and necessary measures, and sends this data to the terminal.

[1753] The device will then display the received notification to the user, for example, a message saying "Water stocks are running low."

[1754] Step 8: Provide a purchase link

[1755] The device displays links to purchase supplies that are in short supply and a request form to contact a specialist supplier.

[1756] Users can click on the link to purchase the supplies they need.

[1757] Step 9: Regular checks

[1758] The server regularly checks the expiration dates of stockpiled goods and measures to be taken in response to new seasonal risks.

[1759] If the user needs new measures, the server generates the information and sends it to the terminal.

[1760] Step 10: Periodic Notifications

[1761] The terminal receives the periodic notifications sent from the server and displays them to the user, who can then check the notifications and take the necessary action.

[1762] In this way, the system supports users in taking effective disaster prevention measures through each step.

[1763] Example 1

[1764] 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."

[1765] In disaster risk analysis and countermeasure proposals, there is a lack of systems that can accurately assess the specific risks in the area where a user lives and identify shortages of stockpiles based on that assessment.In addition, there is a lack of notifications and purchasing links that allow users to take effective disaster countermeasures, making it difficult to respond quickly and appropriately.

[1766] 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.

[1767] In this invention, the server includes means for collecting large-scale data and satellite data related to disasters such as earthquakes, tsunamis, and heavy rains, means for analyzing the collected data and calculating the disaster risk in the area where the user lives, and means for collecting emergency stockpile information entered by the user. This makes it possible to evaluate the specific disaster risk in the area where the user lives, identify emergency stockpile shortages based on the evaluation, and provide the user with prompt and appropriate countermeasures.

[1768] A "disaster" is an event that includes adverse effects caused by natural phenomena such as earthquakes, tsunamis, and heavy rains.

[1769] "Big data" refers to a vast amount of information, also known as big data, and includes data related to natural disasters and satellite image data.

[1770] "Satellite data" refers to observation data obtained from artificial satellites, including meteorological and topographical information.

[1771] "Analysis" refers to the process of organizing, calculating, and evaluating collected data to arrive at a specific conclusion.

[1772] "Disaster risk" refers to an assessment value that indicates the possibility of a disaster occurring in a particular area and the extent of damage caused by that disaster.

[1773] "Stockpiles" refer to supplies stored in advance in preparation for emergencies such as disasters. These include food, water, medicine, etc.

[1774] "Notification" means a message or alert intended to inform the User of important information.

[1775] "Purchase Link" means a direct link to an online shopping site provided to enable Users to easily purchase shortage supplies.

[1776] "Vendor Request Form" means an electronic form for entering and submitting a request to a vendor for a particular service or supply.

[1777] "Expiration date" refers to the date by which stockpiled items are recommended for use, after which their quality may deteriorate.

[1778] "Regularly" means repeated at regular intervals, including monthly, seasonal, and other cycles.

[1779] "Residence information" refers to information about the area and address where the user lives.

[1780] MODE FOR CARRYING OUT THE INVENTION

[1781] The present invention relates to a system for disaster risk analysis and countermeasure proposals. This system operates in cooperation with a server, terminals, and users to analyze the disaster risk in the area where the user resides and propose appropriate countermeasures and stockpiles based on the analysis. This specification describes a specific implementation method of the system.

[1782] Hardware and software used

[1783] server

[1784] The server is the central component that collects and analyzes large-scale data related to earthquakes, tsunamis, heavy rain, and other events, as well as satellite data. Data is collected from reliable data sources via the Internet. Specifically, data is obtained from APIs such as those of the Japan Meteorological Agency and NASA, and the data is stored in a MySQL database. Machine learning algorithms using Python's Scikit-Learn library are used for data analysis.

[1785] Terminal

[1786] The terminal provides an interface for users to input emergency stockpile information and receive notifications. The smartphone app was created with React Native and features a user-friendly input form and notification function. The terminal also receives data sent from the server and displays alerts to the user.

[1787] User

[1788] Users use the app to enter their information, including their address and a list of supplies, and view notifications and suggestions from the system.

[1789] System Operation Overview

[1790] Data collection and analysis

[1791] server

[1792] The server periodically collects large-scale data on natural disasters and satellite data, storing it in a MySQL database. The data is analyzed using machine learning algorithms using Python's Scikit-Learn to calculate disaster risk for each region. This analysis then predicts risk levels, such as the probability of earthquakes and tsunamis occurring.

[1793] Management of emergency stockpile information

[1794] Terminal

[1795] The terminal provides a form for users to enter stockpile information, which is then sent over the Internet to a server. The data is encrypted using the HTTPS protocol, ensuring secure transmission.

[1796] server

[1797] The server stores the received stockpile information in a database, compares it with disaster risk information, and identifies shortages of stockpile items. From the analysis results, specific information such as "water stocks are insufficient" is generated.

[1798] User Notifications and Actions

[1799] server

[1800] The server then sends notifications to users based on the analysis results, including messages such as "Tsunami risk in your area is increasing. Water reserves are running low," and these notifications are sent to devices via a push notification API.

[1801] Terminal

[1802] The device receives notifications sent from the server and displays them to the user, often as popups or banners within the app, allowing the user to take any necessary action immediately.

[1803] User

[1804] Users can click on the purchase link in the notification to purchase the supplies they need online, for example, by going directly to an e-commerce site such as Amazon or Rakuten Ichiba and ordering the supplies they need.

[1805] Regular checks and update notifications

[1806] server

[1807] The server periodically checks the expiration dates and seasonal risks of stockpiled items. The results of the check are notified to the user. For example, the server may notify the user that "The expiration date of the stockpiled food is approaching. Please purchase new stockpiles."

[1808] Terminal

[1809] The terminal receives periodic update notifications sent from the server and displays them to the user, allowing the user to periodically update their stockpiles and take new measures.

[1810] Prompt Sentence Examples

[1811] 1. "I would like to know the disaster risk in the area where I live. I live in Shibuya Ward, Tokyo."

[1812] 2. "I just typed in the list of food items I have stored at home. Is this list sufficient for disaster preparedness?"

[1813] 3. "Please let me know if there is anything missing from our summer stockpile."

[1814] 4. "I'd like to purchase water within the app. Can you provide a link?"

[1815] The above is an embodiment of the present invention. This system allows users to take effective disaster countermeasures and reduces the burden on government agencies.

[1816] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1817] Step 1: Data collection

[1818] server

[1819] The server collects large-scale disaster-related data and satellite data at a specified time every day. The data is acquired using APIs from, for example, the Japan Meteorological Agency or NASA. The API endpoint and authentication information of each data source are used as input, and the acquired data is stored in the server's MySQL database as output. Specifically, the data acquisition script is executed periodically using the Python requests library.

[1820] Step 2: Data analysis

[1821] server

[1822] The server analyzes the acquired dataset and calculates the disaster risk for each region. The data collected in step 1 is used as input, and the output is an assessment value indicating the risk level for each region. Specifically, a machine learning algorithm using Python's Scikit-Learn library processes the data and performs a risk assessment. The analysis results are then stored in a MySQL database.

[1823] Step 3: Enter emergency supply information

[1824] Terminal

[1825] The terminal provides an interface for users to input emergency supply information. Input includes the type, quantity, and expiration date of emergency supplies, which the user enters through the app. This data is sent to the server as output. Specifically, a form created with React Native is displayed, and the information entered by the user is encrypted via HTTPS and sent to the server.

[1826] Step 4: Sending and receiving stockpile information

[1827] Terminal

[1828] The terminal sends the stockpile information entered by the user to the server in real time. The input includes the data entered by the user in the form, and this data is sent to the server as output. Specifically, the user enters stockpile information and clicks the "Send" button, and the data reaches the server.

[1829] server

[1830] The server receives the stockpile information sent from the terminal and stores it in a database. The input includes the stockpile information sent, and the output is information in a format that can be stored in the database. Specifically, it analyzes the received information and stores the data in the appropriate table.

[1831] Step 5: Analyze stockpile information

[1832] server

[1833] The server analyzes the received stockpile information and compares it with disaster risk information to identify shortages. Input includes stored stockpile information and the local risk level. The output generates a list of shortages. Specifically, a Python script retrieves the necessary information from the database and uses an analysis algorithm to identify shortages.

[1834] Step 6: Create and send notifications

[1835] server

[1836] The server creates and sends notifications based on shortages of stockpiles and disaster risks. The input includes the analysis results of stockpile information, and the output is a specific notification message. Specifically, the notification generation script creates the notification content based on the analysis results and sends it to the device via the push notification API.

[1837] Terminal

[1838] The device receives notifications from the server and displays them to the user. The input includes the notification message sent from the server, and the output is the notification displayed on the device screen. Specifically, the notification is displayed as a popup or banner within the app.

[1839] Step 7: Provide a purchase link and take action

[1840] Terminal

[1841] The device displays links to purchase supplies and a request form to specialist vendors for supplies that are in short supply. The input includes notification content from the server, and the output is a purchase link that is displayed to the user. Specifically, the link is displayed on a screen within the app, and the user can click it.

[1842] User

[1843] The user clicks on the purchase link in the notification to purchase the missing supplies. The input includes the displayed purchase link, and the output is access to an e-commerce site, etc. The specific operation is that the user clicks the link, a browser opens, and the purchase page is displayed.

[1844] Step 8: Regular checks and update notifications

[1845] server

[1846] The server periodically checks the expiration dates and seasonal risks of stockpiled items and notifies users of the results. The input includes stockpile information and time information in the database, and the output is an update notification message. Specifically, a periodic script scans the database and generates and sends the necessary notifications.

[1847] Terminal

[1848] The device receives periodic update notifications from the server and displays them to the user. The input includes the update notification message from the server, and the output is the update notification displayed on the device screen. Specifically, the notification is displayed as a pop-up or banner within the app, allowing the user to check it periodically.

[1849] In this way, by clarifying the input and output at each step and describing the specific operations, the operation of the entire system and its specific processing procedures become clear.

[1850] (Application example 1)

[1851] 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."

[1852] In modern society, large-scale natural disasters occur frequently, making it important for individuals and households to prepare appropriate emergency supplies. However, it is not easy for individuals to assess disaster risk and manage the necessary supplies themselves. In addition, there is a lack of systems for properly managing emergency food and other supplies needed in the event of a disaster, and for quickly replenishing supplies when shortages occur. This can result in insufficient supplies in emergencies, significantly impacting the lives of disaster victims. Furthermore, conventional systems do not integrate disaster risk assessment and emergency supply management, making it difficult to implement efficient countermeasures.

[1853] 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.

[1854] In this invention, the server includes means for collecting big data and satellite data related to disasters such as earthquakes, tsunamis, and heavy rains; means for analyzing the collected data and calculating the disaster risk in the user's area; and means for analyzing stockpile information entered by the user and identifying stockpile shortages based on the disaster risk. This enables the server to analyze the disaster risk in the user's area, manage the emergency food stockpile status in the event of a disaster, suggest emergency food to the user, and enable easy ordering. Furthermore, by allowing the user to enter address information and receive confirmation of shortages of emergency food and other items according to the disaster risk and a link to purchase them, the server allows the user to quickly and reliably prepare stockpiles.

[1855] "Big data" refers to a large amount of data in a variety of formats, and is data that is so large that it is difficult to process using conventional data processing applications.

[1856] "Satellite data" refers to information obtained from Earth observation satellites and satellite images, and is data used to understand changes in topography and climate.

[1857] "Disaster risk" is an index that evaluates the likelihood of natural disasters such as earthquakes, tsunamis, and heavy rains occurring and their impact.

[1858] "Stocked goods information" refers to information such as the type, quantity, and expiration date of disaster supplies that the user keeps in their own home or facility.

[1859] "Emergency food" refers to food that can be consumed immediately in the event of a disaster, can be stored for a long period of time, and is pre-cooked or can be eaten with simple cooking procedures.

[1860] A "notification" is the act of conveying warnings or information to a user, such as a message sent via push notification or email.

[1861] A "purchase link" is a hyperlink to a web page provided for users to quickly purchase the missing supplies.

[1862] The "specialist vendor request form" is an input form for a user to request the necessary goods or services from a specialist vendor.

[1863] The "best before" date is the date by which the quality of food is guaranteed, and after this date the taste and quality may deteriorate.

[1864] "Regular checks" are a process of checking the status of stockpiled goods and any new risks at regular intervals, and notifying users as necessary.

[1865] "Management of emergency food stockpiles in the event of a disaster" is the process of determining the types and quantities of emergency food that users possess and checking to see if there are any shortages.

[1866] "Easy ordering means" is a function that allows users to quickly and easily purchase shortages of stockpiled items through the application.

[1867] This invention relates to a system for disaster risk analysis and countermeasure proposals. This system, which operates in cooperation with a server, terminals, and users, analyzes the disaster risk in the user's residential area and proposes appropriate countermeasures and emergency supplies. It also has a function that allows users to input address information and emergency supply information, and provides a confirmation of shortages of emergency supplies and a link to purchase them.

[1868] Data collection methods

[1869] server

[1870] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rain, etc. This data is used as basic data for disaster risk analysis. The data is obtained from reliable data sources via the Internet and stored in a database. For example, data is collected from public disaster information APIs and satellite data provision services.

[1871] Data Analysis Methods

[1872] server

[1873] The server analyzes the collected data and calculates the disaster risk in the area where the user lives. The analysis uses existing disaster models and machine learning algorithms. For example, it predicts the risk level of the area based on data from past earthquakes and tsunamis, and notifies the user that "there is a high risk of tsunami in your area."

[1874] Stockpile information input method

[1875] Terminal

[1876] Users use a smartphone app or web app to input information about their home emergency supplies, including water, emergency food, batteries, etc., and are provided with an interface for entering information such as the type, quantity, and expiration date of each item.

[1877] Stockpile information analysis method

[1878] server

[1879] The server identifies shortages based on the stockpile information entered by the user. Analysis is performed in conjunction with disaster risk assessments to confirm whether stockpiles are sufficient for high-risk disasters. For example, in areas with a high risk of tsunamis, the server will notify users that "water stockpiles are insufficient."

[1880] Notification means

[1881] Server and terminal

[1882] The server notifies users of shortages of supplies and necessary measures via push notifications and emails. Devices receive notifications from the server and have the ability to notify users via pop-ups and banners.

[1883] Purchase link provision method

[1884] Terminal

[1885] The device provides links to purchase supplies that are in short supply and a request form to specialists, allowing users to easily purchase the supplies they need or request assistance from specialists within the app.

[1886] Regular check method

[1887] Server and terminal

[1888] The server periodically checks expiration dates of stockpiled items and measures to respond to new seasonal risks, and notifies the user. The terminal has the function to receive periodic notifications sent from the server and display them to the user.

[1889] Example of a system

[1890] This system uses Python to write data analysis scripts and collects data from external APIs using the HTTP request module (requests library). It also uses the smtplib library to send emails. The system analyzes disaster risks in the user's area, identifies shortages of emergency supplies based on the results, and sends notifications, allowing users to take appropriate measures quickly.

[1891] Specific example prompt

[1892] Below are some example prompts to use when inputting a generative AI model:

[1893] You are the developer of a food delivery app. You want to incorporate a disaster risk analysis and countermeasure proposal system into this app. Design a disaster prevention function that works on the smartphone app based on the following specifications:

[1894] 1. Analyze disaster risk based on address information entered by the user.

[1895] 2. Analyze the stockpile information entered by the user and identify any shortages.

[1896] 3. Notify users via push notification or email about shortages and necessary precautions.

[1897] 4. Include a function to provide links to purchase supplies that are in short supply.

[1898] The output should include a detailed process flow and a description of the software and hardware used.

[1899] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1900] Step 1:

[1901] Data collection

[1902] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rain, etc. from reliable data sources via the Internet. This data includes information on past disasters and meteorological data. The server uses this data as basic data for assessing disaster risk and stores it in a database. The input is disaster data from an external API, and the output is the data to be analyzed that is stored in the server's database.

[1903] Step 2:

[1904] Data analysis

[1905] The server analyzes the collected data and calculates the disaster risk for the residential area based on the address information entered by the user. Existing disaster models and machine learning algorithms are used here. For example, the risk level for the area is predicted based on data from past earthquakes and tsunamis. The inputs are the collected data and the user's address information, and the output is the disaster risk assessment result for the area where the user lives.

[1906] Step 3:

[1907] Enter emergency stockpile information

[1908] Users use a smartphone app or web app to input information about their home emergency supplies. This includes the type, quantity, and expiration date of items such as water, emergency food, and batteries. The input is done by the user through the app interface. The input data is sent to the server and stored in a database as emergency supply information. The input is the emergency supply information entered by the user, and the output is the emergency supply information stored in the server's database.

[1909] Step 4:

[1910] Stockpile information analysis

[1911] The server identifies what is lacking based on the stockpile information entered by the user. This is done in conjunction with disaster risk assessments to ensure that stockpiles are sufficient for high-risk disasters. For example, in areas with a high risk of tsunamis, it identifies that "water stockpiles are insufficient." The inputs are stockpile information and disaster risk assessment results, and the output is a list of stockpiles that are lacking.

[1912] Step 5:

[1913] notification

[1914] The server notifies the user of the identified shortages and necessary measures. This notification is done via push notification or email. Based on the acquired information on shortages, a specific message is generated and sent to the user. The input is the information on shortages, and the output is the notification message sent to the user.

[1915] Step 6:

[1916] Purchase link provided

[1917] The device provides users with links to purchase supplies they are running low on and a request form to specialist suppliers. Users can easily purchase the supplies they need from within the app, allowing users to quickly and reliably replenish their supplies. The input is a list of supplies they are running low on, and the output is links to purchase the supplies and a request form.

[1918] Step 7:

[1919] Regular checks

[1920] The server periodically checks the expiration dates of stockpiled items and measures to be taken in response to new seasonal risks, and notifies the user. This allows users to continuously maintain appropriate disaster prevention measures. The input is stockpiled item information and seasonal data, and the output is notification messages as a result of the regular checks.

[1921] 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.

[1922] This invention combines an emotion engine with a disaster risk analysis and countermeasure proposal system. This system works in conjunction with a server, terminal, and user to analyze the disaster risk in the user's area and propose appropriate countermeasures and emergency supplies. It also has a function to recognize the user's emotions and adjust the notification content and countermeasure priority.

[1923] System Operation Overview

[1924] Data collection methods

[1925] server

[1926] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rain, etc. from reliable data sources. This data is stored in a database as basic data for disaster risk analysis.

[1927] Data Analysis Methods

[1928] server

[1929] The server analyzes the collected data and calculates the disaster risk for the area the user lives in. The analysis is performed using existing disaster models and machine learning algorithms. For example, it predicts the risk level for the area based on data from past earthquakes and tsunamis.

[1930] Stockpile information input method

[1931] Terminal

[1932] The device provides an interface for users to input information about emergency supplies stored at home. Users can enter this information using a smartphone app or a web app. Input items include the type and quantity of emergency supplies, as well as their expiration date.

[1933] Stockpile information analysis method

[1934] server

[1935] The server identifies shortages based on the stockpile information entered by the user, and analyses it against disaster risk assessments to determine whether stockpiles are sufficient for high-risk disasters.

[1936] Notification means

[1937] server

[1938] The server notifies users of shortages of supplies and necessary measures via push notifications or email. For example, it may send a specific message such as, "Your water supply is low."

[1939] Terminal

[1940] The device receives notifications from the server and displays them to the user. When a notification is received, the user is notified via a pop-up or banner.

[1941] Purchase link provision method

[1942] Terminal

[1943] The device provides links to purchase supplies that are in short supply and a request form to specialists, allowing users to easily purchase needed supplies or request assistance from specialists within the app.

[1944] User

[1945] Users can view notifications and click links to purchase supplies they need, such as ordering water or food directly from within the app.

[1946] Regular check method

[1947] server

[1948] The server periodically checks expiration dates of stockpiled items and measures to address new seasonal risks, such as heatstroke prevention measures in summer and cold weather measures in winter.

[1949] Terminal

[1950] The device receives periodic notifications sent from the server and displays them to the user, allowing the user to periodically update their stockpiles and take new measures.

[1951] emotion recognition means

[1952] Terminal

[1953] The device provides an interface for recognizing emotions from user input and behavior, for example, estimating emotions from the user's input speed and content.

[1954] server

[1955] The server uses an emotion engine to analyze the data sent from the terminal and recognize the user's emotional state.

[1956] Emotion-based notification adjustment

[1957] server

[1958] The server then adjusts the notification content based on the perceived emotion. For example, if the user is feeling anxious, it will suggest a reassuring message or suggest measures.

[1959] Terminal

[1960] The terminal receives the adjusted notification and displays it to the user.

[1961] A method for adjusting countermeasure priorities according to emotions

[1962] server

[1963] The server adjusts the priority of suggested supplies and measures according to the user's emotions. For example, if the user is feeling stressed, it will prioritize suggested measures that require urgent action.

[1964] Terminal

[1965] The device displays the adjusted measures to the user and encourages them to take specific actions.

[1966] Specific example of system operation

[1967] Risk notification after entering address

[1968] 1. User: Launches the app and enters their address.

[1969] 2. Terminal: Sends address information to the server.

[1970] 3. Server: Analyzes the disaster risk in the area based on the address information and sends the results to the device.

[1971] 4. Device: Notify the user that "There is a high risk of tsunami in your area."

[1972] Check for shortages of emergency supplies and purchase them

[1973] 1. User: Enters current stockpile list into the app.

[1974] 2. Terminal: Sends stockpile information to the server.

[1975] 3. Server: Identify shortages of supplies based on disaster risk information.

[1976] 4. On the device: Notify the user that their water supply is low and provide a link to purchase water.

[1977] 5. User: Clicks on the link to purchase the scarce water.

[1978] Emotion recognition and response

[1979] 1. User: Receives notifications of disaster risks and shortages of emergency supplies, and inputs and takes action within the app.

[1980] 2. Terminal: Collects emotional data from the user's input speed, input content, etc.

[1981] 3. Server: Analyzes the collected emotional data and recognizes that the user is feeling anxious.

[1982] 4. Server: Generates a reassuring message based on the user's emotions and sends it to the device.

[1983] 5. Device: Display a message to the user saying, "Here are some measures you need to take to ensure your safety," and suggest specific measures.

[1984] In this way, by combining an emotion engine, the present invention realizes a system that provides disaster countermeasures that better meet user needs. This system allows users to take appropriate measures according to their own emotional state, increasing their sense of security in the event of a disaster.

[1985] The processing flow will be explained below.

[1986] Step 1: Data collection

[1987] The server periodically collects big data and satellite data related to earthquakes, tsunamis, heavy rain, etc. from reliable data sources. This data is stored in a database as basic data for disaster risk analysis.

[1988] Step 2: Enter user information

[1989] Users launch the app and enter initial information such as their address, family composition, and current list of emergency supplies.

[1990] The terminal receives the information entered by the user, checks the format, and then sends it to the server.

[1991] Step 3: Risk analysis

[1992] The server calculates the disaster risk for the area based on the address information sent by the user, and evaluates the risk level using collected big data and satellite data.

[1993] Step 4: Risk notification

[1994] The server creates the results of the risk analysis as risk information customized for each user and sends it to the terminal.

[1995] The device will notify the user of the received risk information via pop-ups or notification banners.

[1996] Step 5: Gather information on emergency supplies

[1997] Users use a smartphone app or web app to enter information about their home emergency supplies.

[1998] The terminal receives the stockpile information and transmits it to the server.

[1999] Step 6: Stockpile information analysis

[2000] The server evaluates what is lacking based on the stockpile information entered by the user, and creates a list of stockpiles that are lacking in accordance with the disaster risk assessment.

[2001] Step 7: Emotion Recognition

[2002] The device collects user input and operation data and generates data to estimate the user's emotional state.

[2003] The server recognizes the user's emotional state using an emotion engine based on the data sent from the terminal.

[2004] Step 8: Adjust notifications based on emotion

[2005] The server tailors the notification content based on the perceived emotion, for example generating a reassuring message if the user is feeling anxious.

[2006] The server sends the adjusted notification content to the terminal.

[2007] The device will then display the received notification to the user, for example, "Here are some steps you need to take to ensure your safety."

[2008] Step 9: Stockpile Shortage Notification

[2009] The server creates information to inform the user about shortages of stockpiled items and necessary measures, and sends it to the terminal.

[2010] The device displays the received notification to the user, for example, displaying a message saying "Water stocks are low."

[2011] Step 10: Provide a purchase link

[2012] The device displays links to purchase supplies that are in short supply and a request form to contact a specialist supplier.

[2013] Users click on a link to purchase the supplies they need.

[2014] Step 11: Regular checks and notifications

[2015] The server regularly checks the expiration dates of stockpiled goods and measures to be taken in response to new seasonal risks.

[2016] If the user needs new measures, the server generates the information and sends it to the terminal.

[2017] The device displays the received periodic notifications to the user, who can then check the notifications and take any necessary action.

[2018] In this way, the system supports users in taking effective disaster countermeasures through each step. By combining this system with an emotion engine, it can provide responses that are in line with the user's emotional state, increasing a sense of security in the event of a disaster.

[2019] Example 2

[2020] 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."

[2021] In modern society, natural disasters such as earthquakes, tsunamis, and heavy rains occur frequently, making it important to prepare promptly and appropriately. However, the wide variety of disaster-related information makes it difficult for ordinary users to effectively utilize this information and prepare appropriate emergency supplies. Furthermore, advanced data analysis is required to accurately assess disaster risk and take necessary measures. Furthermore, notifications and countermeasure suggestions that ignore the user's emotional state present a challenge, making it difficult to alleviate the anxiety and stress felt by users.

[2022] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting disaster-related data, means for analyzing the collected data and calculating the disaster risk in the user's area, means for analyzing stockpile information entered by the user and identifying stockpile shortages based on the disaster risk, means for notifying the user of stockpile shortages and necessary countermeasures, means for providing links to purchase the stockpile shortages and request forms to vendors, means for periodically checking stockpile expiration dates and new countermeasures according to the season and notifying the user, and means for recognizing the user's emotions and adjusting the notification content and countermeasure priority. This allows the user to receive accurate and timely disaster risk information and countermeasure suggestions, and further enables flexible countermeasures according to the user's emotional state.

[2023] A "disaster" is an emergency situation caused by natural phenomena, such as an earthquake, tsunami, or heavy rain.

[2024] "Data" refers to information in the form of numbers, images, text, etc., related to earthquakes, tsunamis, and heavy rain, including disaster-related information.

[2025] A "server" is a central computer that collects, analyzes, and stores data.

[2026] "User" means an individual or organization that uses the system to prepare for a disaster.

[2027] "Stocked goods" are supplies such as food, water, medicine, etc. that users prepare in advance in preparation for disasters.

[2028] A "notification" is a message containing information, warnings, or suggestions sent by the system to a user.

[2029] "Link" means a hypertext link that a user can click to go directly to related information or a purchasing page.

[2030] "Vendors" are businesses that specialize in providing disaster-related goods and services.

[2031] A "form" is an interface on the web or in an application that allows a user to enter information.

[2032] "Emotion" refers to the user's psychological state, and includes emotions such as joy, sadness, anxiety, and stress.

[2033] "Countermeasures" refer to specific actions and preparations that users should take to prepare for disasters.

[2034] "Analysis" is the process of performing calculations and analysis based on collected data to derive meaningful results.

[2035] "Calculation" is the process of quantifying collected data and using algorithms to perform specific risk assessments.

[2036] The present invention relates to a system for disaster risk analysis and stockpile management, and furthermore, by adjusting notification content and the priority of countermeasures based on the user's emotional state, it is possible to provide optimal countermeasures according to the user's needs.

[2037] Data collection methods

[2038] server

[2039] The server periodically collects data on earthquakes, tsunamis, heavy rains, etc. This data is retrieved from reliable data sources on the Internet using Python libraries (Requests and BeautifulSoup), including government APIs and satellite data.

[2040] Example: Obtain earthquake data from the Japan Meteorological Agency's API and store it in a database.

[2041] Data Analysis Methods

[2042] server

[2043] The server analyzes the collected data and calculates the disaster risk in the area where the user lives, using machine learning algorithms powered by TensorFlow.

[2044] Example: Predicting earthquake risk in a region by learning from past earthquake data.

[2045] Stockpile information input method

[2046] Terminal

[2047] The terminal provides an interface for users to input information about their home emergency supplies. Using a smartphone app (iOS: Swift, Android: Kotlin) or a web app (React.js), users can input the types, quantities, and expiration dates of emergency supplies such as food, water, and medicine.

[2048] Example prompt: "Please list your current stockpile items including their quantities and expiration dates."

[2049] Example: Enter "5 liters of water, expiration date: January 2024."

[2050] Stockpile information analysis method

[2051] server

[2052] The server stores the stockpile information sent by users in a database and analyzes the data using Pandas. It compares the data with disaster risk information to identify shortages of stockpile items.

[2053] Example: Comparing the stockpile list with disaster risk information and determining that "water stocks are insufficient."

[2054] Notification means

[2055] server

[2056] The server notifies users of shortages and necessary measures by sending push notifications using Firebase Cloud Messaging (FCM).

[2057] Example: "Water supplies are low. Please bring at least 3 liters of water."

[2058] Terminal

[2059] The terminal receives the notification from the server and displays the notification to the user in a pop-up or banner.

[2060] Purchase link provision method

[2061] Terminal

[2062] The terminal displays links to purchase the supplies in short supply (for example, online shopping sites) and a request form from a specialist supplier.

[2063] Example: Displaying a notification such as "Water stocks are low. You can purchase more at this link."

[2064] User

[2065] The user uses the displayed link to purchase the supplies they are missing.

[2066] Example: Clicking on a link in a notification to order water from an online shopping site.

[2067] Regular check method

[2068] server

[2069] The server uses Django's Celery to periodically check the expiration dates of stockpiled items and measures to address new risks.

[2070] Example: In summer, generate and send notifications such as "Please take precautions against heatstroke. Don't forget to replenish with water and salt."

[2071] Terminal

[2072] The terminal receives the periodic notification sent from the server and displays it to the user.

[2073] emotion recognition means

[2074] Terminal

[2075] The device collects the user's input speed and input content, and provides an interface to obtain emotion data. Emotions are estimated using a JavaScript library (TensorFlow.js).

[2076] Example prompt: "How do you feel after receiving the disaster risk notification? (e.g., worried, stressed)"

[2077] Example: Inferring "anxiety" from user input.

[2078] server

[2079] The server uses an emotion engine to analyze the emotion data sent from the terminal and recognize the user's emotional state.

[2080] Emotion-based notification adjustment

[2081] server

[2082] The server adjusts the notification content based on the emotion recognition results: if the user is feeling anxious, it generates a reassuring message.

[2083] Example: Generate and send a message such as, "We're sharing specific steps to help you stay safe. Please stay tuned for next steps."

[2084] Terminal

[2085] The terminal receives the adjusted notification and displays it to the user.

[2086] A method for adjusting countermeasure priorities according to emotions

[2087] server

[2088] The server adjusts the priority of the proposed measures according to the user's emotions. The emotion engine can utilize a generative AI model.

[2089] Example: "If a user feels anxious, we suggest they start with basic supplies of water and food."

[2090] Terminal

[2091] The terminal displays the adjusted measures to the user and encourages them to take specific actions.

[2092] This system allows users to receive disaster risk information quickly and appropriately, enabling them to take the most appropriate measures according to the situation. In addition, flexible notification adjustment and optimization of countermeasure priorities based on emotion recognition allow users to prepare for disasters with even greater peace of mind.

[2093] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2094] Program processing steps

[2095] Step 1:

[2096] A user launches a smartphone app or web app and enters the address where they live. The entered address information is sent to the server via the device.

[2097] Input: Address information (e.g. "Tokyo, Chiyoda-ku, Marunouchi 1-chome~")

[2098] Output: The address information is sent to the server and stored.

[2099] Step 2:

[2100] The server collects the data necessary to calculate the disaster risk for that area based on the received address information. The server obtains data on earthquakes, tsunamis, and heavy rain from the Japan Meteorological Agency and overseas satellite data providers.

[2101] Input: Address information

[2102] Output: Data required for disaster risk calculation (e.g., past earthquake data, tsunami impact area data, rainfall data)

[2103] Step 3:

[2104] The server analyzes the collected data using machine learning algorithms powered by TensorFlow to calculate the disaster risk level for each address.

[2105] Input: Disaster-related data

[2106] Output: Risk level (e.g., earthquake risk "high", tsunami risk "medium", heavy rain risk "low")

[2107] Step 4:

[2108] The server generates a notification message for the user based on the risk level, and the generated notification is sent to the user's device via Firebase Cloud Messaging (FCM).

[2109] Input: Risk Level

[2110] Output: Notification message (e.g. "Your area is at high risk of earthquakes")

[2111] Step 5:

[2112] The terminal receives the notification message sent from the server and displays it to the user in a pop-up or banner.

[2113] Input: Notification message

[2114] Output: Notification shown to the user (e.g., a popup saying "Your area is at high risk of earthquakes")

[2115] Step 6:

[2116] The user follows the instructions in the app to enter information about emergency supplies, which is then sent to the server via the device.

[2117] Input: Stockpile information (e.g., "5 liters of water, expiration date: January 2024")

[2118] Output: Stockpile information is sent to the server and stored.

[2119] Step 7:

[2120] The server analyzes the stockpile information sent by the user and cross-references the data using Pandas. It compares the data with disaster risk information to identify shortages of stockpile items.

[2121] Input: Stockpile information

[2122] Output: Information about the shortage of supplies (e.g., "Water supplies are running low")

[2123] Step 8:

[2124] The server creates links to purchase the stockpiles that are in short supply, as well as a request form for specialist vendors, and notifies the user.

[2125] Input: Missing stockpile information

[2126] Output: Notification message with a purchase link (e.g. "Your water supply is low. You can purchase more at this link")

[2127] Step 9:

[2128] The terminal receives the notification with the link sent from the server and displays it to the user.

[2129] Input: Notification message with purchase link

[2130] Output: The notification displayed to the user

[2131] Step 10:

[2132] The user uses the provided link to purchase the missing supplies, and once the purchase is complete, the information is updated and sent back to the server.

[2133] Input: Purchase link click and purchase information

[2134] Output: Updated stockpile information is sent to the server and stored.

[2135] Step 11:

[2136] The server uses Django's Celery to periodically check the expiration dates of users' stockpiles and new seasonal disaster risks, and generates notification messages.

[2137] Input: Stockpile information, seasonal countermeasure information

[2138] Output: Periodic notification message (e.g. "Summer preparation is necessary. Don't forget to replenish with water and salt.")

[2139] Step 12:

[2140] The terminal receives the periodic notification sent from the server and displays it to the user.

[2141] Input: Periodic notification message

[2142] Output: The notification displayed to the user

[2143] Step 13:

[2144] The device collects emotion data from the user's input speed and content, and uses a JavaScript library (TensorFlow.js) to estimate emotions.

[2145] Input: User-entered data

[2146] Output: Estimated emotion data (e.g., "anxiety")

[2147] Step 14:

[2148] The server uses an emotion engine to analyze the emotion data sent from the terminal and recognize the user's emotional state.

[2149] Input: Emotion data

[2150] Output: Emotional state (e.g., "I feel anxious")

[2151] Step 15:

[2152] The server adjusts the notification content based on the user's emotions, generating a reassuring message for users who are feeling particularly anxious.

[2153] Input: Emotional state

[2154] Output: A tailored notification message (e.g., "We'll provide you with specific steps to stay safe")

[2155] Step 16:

[2156] The terminal receives the adjusted notification and displays it to the user.

[2157] Input: The adjusted notification message

[2158] Output: The notification displayed to the user

[2159] Step 17:

[2160] The server proposes high-priority measures based on the user's emotions. The emotion engine uses a generative AI model to prioritize measures that require urgency.

[2161] Input: Emotional state

[2162] Output: A list of prioritized actions (e.g., "Secure water and food first.") 【2...

Claims

1. A means of collecting big data and satellite data on disasters such as earthquakes, tsunamis, and heavy rains, and A means for analyzing the collected data and calculating the disaster risk in the area where the user lives; A means for analyzing the stockpile information input by the user and identifying stockpile items that are in short supply based on disaster risk; A means of informing users of shortages and necessary measures; Providing links to purchase supplies that are in short supply and request forms from specialist suppliers; The system includes a means to regularly check the expiration dates of stockpiled items and new seasonal measures, and notify users.

2. 2. The system according to claim 1, wherein the system uses address information input by the user to collect and analyze disaster risk information for the area and notify the user of the information.

3. The system according to claim 1, wherein a shortage is displayed based on stockpile information input by a user, and a purchase link is provided.

Images