System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting and analyzing weather data and sensor data in real time; means for predicting future disaster risk based on historical disaster data; means for detecting abnormal patterns and identifying precursors to disaster; means for automatically issuing warnings based on the identified precursors; means for calculating and providing optimal evacuation routes to users in real time; and means for supporting information exchange between affected areas and rescue teams.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In recent years, the frequent occurrence of natural disasters has put many lives and property at risk. Conventional disaster prevention systems lack the ability to collect and predict data in real time, provide optimal evacuation routes, accurately predict the extent of damage, and provide effective communication support. As a result, early warning, rapid evacuation, and appropriate resource allocation are difficult, making it impossible to minimize damage. Therefore, the purpose of this invention is to utilize AI technology to solve these issues and provide comprehensive measures against natural disasters. [Means for solving the problem]

[0005] The present invention is a system that includes means for collecting and analyzing meteorological and sensor data in real time, means for predicting future disaster risks based on past disaster data, means for detecting abnormal patterns and identifying disaster precursors, means for automatically issuing warnings based on the identified precursors, means for calculating optimal evacuation routes and providing them to users in real time, and means for supporting information exchange between disaster-stricken areas and rescue teams. It also includes means for controlling AI-equipped drones and robots to collect and analyze disaster site information in real time, means for predicting the extent of damage and risks based on the collected and analyzed data and for systematically allocating necessary relief supplies and medical resources, and means for performing natural language processing and translation between different languages ​​to enhance communication between disaster-stricken areas and rescue teams. This enables early response to natural disasters, rapid evacuation, appropriate resource allocation, and reliable communication.

[0006] "Weather data" refers to information about weather, temperature, precipitation, wind speed, and other meteorological information obtained from weather satellites and ground observation stations.

[0007] "Sensor data" refers to physical measurement data collected in real time from sensor devices such as seismometers and river water level gauges.

[0008] "Real-time" means that data is collected and processed in real time, with results available immediately and without any delay.

[0009] "Past disaster data" refers to recorded data on disasters that have occurred in the past, including details of damage, meteorological information, and human and material losses.

[0010] "Disaster risk" refers to the possibility of a natural disaster occurring and the extent of the damage caused by its impact.

[0011] An "unusual pattern" is one that exhibits significant changes or movements that deviate from the standard variations observed during normal data collection.

[0012] A "warning" is a message, such as audio, text, or image, sent to warn of impending disaster or danger.

[0013] An "evacuation route" is the optimal route for victims to quickly evacuate to a safe place when a disaster occurs.

[0014] "User" means any individual or entity that uses this system to receive information.

[0015] An "AI-equipped drone" is an unmanned aerial vehicle that incorporates artificial intelligence technology and has the ability to collect and analyze information at disaster sites.

[0016] "Collected in real time" means that data is collected in an ongoing manner and is available immediately without any delay.

[0017] The "affected area" is the geographical area in which a natural disaster occurs and which is subject to physical damage or impact.

[0018] "Resource allocation" is the process of efficiently and appropriately distributing needed relief supplies and medical resources to disaster-stricken areas.

[0019] "Natural language processing" is a technology that allows computers to understand, process, and analyze human language.

[0020] "Machine translation" is a technique in which a computer automatically translates text between different languages.

[0021] "Disaster area" means a geographic area affected by a natural disaster.

[0022] A "rescue team" is a specialized group or organization that carries out rescue operations and assistance during disasters. [Brief explanation of the drawings]

[0023] [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

[0024] 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.

[0025] First, the terms used in the following description will be explained.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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."

[0031] [First embodiment]

[0032] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0033] 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.

[0034] 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).

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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."

[0044] This invention is a disaster prevention system that utilizes AI, and its main functions are the real-time collection and analysis of meteorological and sensor data, analysis of past disaster data, detection and warning of abnormal patterns, optimization of evacuation routes, damage prediction and resource allocation, enhanced communication, and utilization of AI-equipped drones and robots. The specific operation of each function in the system of this invention will be explained below.

[0045] Real-time collection and analysis of weather and sensor data

[0046] server

[0047] The server periodically collects weather data from weather satellites and ground observation stations, as well as sensor data from seismometers, river level gauges, and other sources.

[0048] The collected data is stored in a database in real time and is then analyzed using AI algorithms.

[0049] The server can detect signs of disaster by linking weather data with sensor data.

[0050] example

[0051] During periods of heavy rain, the server collects data from various sensors and detects sudden rises in water levels. By analyzing this data, it is possible to identify potential floods early and take appropriate action.

[0052] Analysis of past disaster data and disaster risk prediction

[0053] server

[0054] The server collects data from past disasters, inputs it into an AI model, and performs analysis.

[0055] The AI ​​model predicts future disaster risks based on data such as weather patterns, damage, and relief efforts.

[0056] Based on the forecast data, we identify areas where disasters may occur and the scale of the damage.

[0057] example

[0058] When predicting the path of a hurricane, the server analyzes current weather data based on past hurricane data to predict the possible extent and intensity of damage, which can be useful for making necessary preparations in advance.

[0059] Detecting abnormal patterns and issuing alerts

[0060] server

[0061] The server analyzes normal weather and sensor data to detect any unusual patterns.

[0062] Based on the identified abnormal patterns, an alert is generated if they are deemed to be a sign of a disaster.

[0063] The warning will be sent to a designated device (smartphone, PC) and simultaneously sent to relevant agencies.

[0064] example

[0065] Servers monitoring seismometer data detect the signs of an earthquake that is building up in size and send warnings to local residents' smartphones.

[0066] Optimizing evacuation routes

[0067] Terminal

[0068] The device (user's smartphone) uses GPS to send current location information to the server.

[0069] The server analyzes traffic conditions, road closure information, and real-time environmental conditions to calculate the optimal evacuation route.

[0070] The terminal provides the user with the evacuation route received from the server, assisting in evacuation in the event of an emergency.

[0071] example

[0072] In the event of a fire, users can launch the app on their smartphones and get real-time information on the optimal evacuation route. If the route is blocked, the server will calculate and provide a new route.

[0073] Damage prediction and resource allocation optimization

[0074] server

[0075] The server uses the collected data and past disaster data to predict the extent of damage and the impact on each region.

[0076] Based on the forecast data, we calculate the amount of relief supplies and medical resources needed and create a plan for efficient allocation.

[0077] Resource allocation plans will be shared with relevant agencies, enabling rapid response.

[0078] example

[0079] When a major typhoon is approaching, the server creates a list of predicted affected areas and needed relief supplies, and automatically sends it to the relevant organizations, allowing for faster preparations.

[0080] Strengthening communication

[0081] User

[0082] Users can use a smartphone app to input information about the damage situation and requests for help in natural language.

[0083] The server analyzes the entered information, translates it appropriately, and automatically sends it to the relevant organizations.

[0084] example

[0085] If a victim requests help from a rescue team in Japanese, they can input their voice into the app, and the server will translate the message into English and quickly transmit the information to rescue teams dispatched from overseas.

[0086] Utilizing AI-equipped drones and robots

[0087] server

[0088] The server dispatches AI-equipped drones and robots to disaster-stricken areas and collects video and sensor information in real time.

[0089] The collected data is analyzed on a server and provided to relevant organizations.

[0090] example

[0091] After an earthquake occurs, the server uses drones to collect detailed images of the affected area and transmits the analysis results to rescue teams in real time, enabling them to make quick decisions and take action.

[0092] As described above, this invention is a system that combines various technologies to enable real-time disaster countermeasures and minimize damage. Implementing this system is expected to significantly improve disaster preparedness and response, protecting many lives and property.

[0093] The processing flow will be explained below.

[0094] 1. Early Warning System

[0095] server

[0096] Step 1:

[0097] The server collects weather data from weather satellites and ground observation stations.

[0098] Similarly, sensor data is collected in real time from seismometers, river water level gauges, and other sources.

[0099] Step 2:

[0100] The collected weather data and sensor data are stored in a database.

[0101] The stored data is fed into AI algorithms to detect abnormal patterns.

[0102] Step 3:

[0103] AI algorithms analyze the data and detect unusual patterns and fluctuations.

[0104] Based on the detected abnormal data, it is determined whether it is a sign of a disaster.

[0105] Step 4:

[0106] If it is determined to be a sign of a disaster, a warning message will be generated.

[0107] This warning message is sent to designated terminals and relevant organizations.

[0108] 2. Evacuation route optimization

[0109] Terminal

[0110] Step 1:

[0111] The user's device (smartphone) obtains current location information using the GPS function.

[0112] The current location information is periodically sent to the server.

[0113] server

[0114] Step 2:

[0115] The server analyzes real-time traffic and road information based on the received current location information.

[0116] Uses AI algorithms to calculate optimal evacuation routes.

[0117] Terminal

[0118] Step 3:

[0119] The optimal evacuation route information is sent to the user's terminal.

[0120] Evacuation route information is displayed on the device app and provided to the user.

[0121] Step 4:

[0122] If roads are closed during an evacuation, the server recalculates a new route and sends updates to the device.

[0123] 3. Damage prediction and risk analysis

[0124] server

[0125] Step 1:

[0126] The server collects past disaster data and current weather data and inputs it into the AI ​​model.

[0127] The AI ​​model predicts the extent and impact of disasters based on past data.

[0128] Step 2:

[0129] Based on the prediction results, a risk assessment is carried out.

[0130] Based on the results of the risk assessment, identify the amount of resources needed (relief supplies, medical resources, etc.).

[0131] Step 3:

[0132] Prepare a resource allocation plan and provide it to relevant agencies and users.

[0133] 4. Strengthening communication

[0134] User

[0135] Step 1:

[0136] Users enter disaster situations and requests for assistance into the app.

[0137] Input methods include voice input and text input.

[0138] server

[0139] Step 2:

[0140] The server feeds the entered information into natural language processing and machine translation algorithms.

[0141] Carry out translation between different languages.

[0142] Step 3:

[0143] Translated information will be sent to rescue teams and relevant agencies.

[0144] 5. Use of drones and robots

[0145] server

[0146] Step 1:

[0147] The server will control AI-equipped drones and robots and begin collecting information from the disaster site.

[0148] The drone follows a set flight path and collects data in real time.

[0149] Step 2:

[0150] The server analyzes the collected data in real time to grasp the detailed situation at the disaster site.

[0151] If necessary, the drone or robot's movements will be adjusted to gather more detailed information.

[0152] Step 3:

[0153] The analysis results will be provided to relevant agencies and rescue teams to assist in rapid response.

[0154] 6. Demand forecasting and resource allocation

[0155] server

[0156] Step 1:

[0157] The server inputs past disaster data and current situation data into the AI ​​model.

[0158] AI models predict demand (food, water, medical supplies, etc.) after a disaster occurs.

[0159] Step 2:

[0160] Calculate the amount of relief supplies and medical resources needed based on forecast data.

[0161] Develop distribution plans and support efficient relief efforts.

[0162] Step 3:

[0163] The allocation plan will be provided to relevant organizations and users to ensure appropriate resource allocation.

[0164] Based on these steps, the system of the present invention can realize rapid and effective countermeasures against natural disasters, and can save many lives and properties.

[0165] Example 1

[0166] 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."

[0167] In recent years, the frequency of natural disasters has increased, and the resulting damage has become more severe. This has created a need for systems that can quickly and accurately predict disaster risks and minimize damage. However, current disaster response systems lack the precision to collect and analyze real-time data, and are lacking in the ability to detect abnormal situations early and issue warnings. Furthermore, improvements are needed to optimize evacuation routes and improve the efficiency of damage prediction and resource allocation. Furthermore, there is a lack of means to facilitate communication between disaster-stricken areas and rescue teams.

[0168] 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.

[0169] In this invention, the server includes means for collecting and analyzing meteorological information and environmental monitoring data in real time, means for predicting future disaster risks based on past disaster information, means for detecting abnormal patterns and identifying signs of disaster, means for automatically issuing warnings based on the identified signs, means for calculating optimal evacuation routes and providing them to users in real time, means for supporting information exchange between the disaster-stricken area and rescue teams, means for providing optimal evacuation routes using current location information from the terminal, means for analyzing collected data with an AI algorithm and detecting abnormal patterns, and means for analyzing past data using a generative AI model to predict disaster risks.This enables early detection of disaster risks in real time, rapid issuance of warnings, provision of efficient evacuation routes, optimal resource allocation, and smooth communication between the disaster-stricken area and rescue teams.

[0170] "Weather information" refers to data such as temperature, humidity, wind speed, and precipitation obtained from meteorological satellites and ground observation stations.

[0171] "Environmental monitoring data" refers to data used to monitor environmental changes obtained from sensors such as seismometers and river water level gauges.

[0172] "Past disaster information" refers to data on natural disasters that have occurred in the past, including details of damage, weather patterns, and relief efforts.

[0173] "Disaster risk" indicates the possibility of a disaster occurring in a particular area in the future and the magnitude of its impact.

[0174] "Abnormal patterns" refer to fluctuations in data that differ from normal weather data or sensor data and are thought to be a sign of a disaster.

[0175] A "warning" is information that notifies that a disaster is expected to occur based on a detected abnormal pattern.

[0176] An "evacuation route" refers to a route for safe evacuation in the event of a disaster, and is calculated based on real-time traffic and environmental conditions.

[0177] "User" refers to an individual or organization that uses the disaster response system to receive data and carry out evacuation and countermeasures.

[0178] "Relief supplies" refers to food, water, medicine and other basic necessities provided to disaster victims in the event of a disaster.

[0179] "Medical resources" is a general term for medical equipment, medicines, medical staff, etc. needed for disaster response.

[0180] A "generative AI model" is a type of machine learning algorithm that learns from past disaster data and predicts future disaster risks.

[0181] A "terminal" is a device that a user uses to receive disaster information and evacuation routes in real time, such as a smartphone or PC.

[0182] The present invention is a disaster prevention system that utilizes AI, and its configuration and implementation method will be described in detail below.

[0183] Real-time collection and analysis of meteorological and environmental monitoring data

[0184] server

[0185] The server periodically retrieves weather information from weather satellites and ground observation stations via an API, using scripts written in Python and a database management system (e.g., PostgreSQL).

[0186] The server also receives environmental monitoring data from seismometers and river level gauges, which are collected using IoT platforms such as AWS® IoT.

[0187] The collected data is stored in a database in real time and analyzed using AI algorithms (e.g., TENSORFLOW (registered trademark)).

[0188] Specific examples

[0189] For example, during periods of heavy rain, the server can collect data from water level sensors and detect sudden rises in water levels. By analyzing this data, flood risks can be detected early and appropriate warnings can be issued.

[0190] Analysis of past disaster information and prediction of future disaster risks

[0191] server

[0192] The server collects past disaster data and inputs it into a generative AI model (e.g., GPT-4 (registered trademark)) for analysis.

[0193] The AI ​​model predicts future disaster risks based on data such as weather patterns, damage, and relief efforts.

[0194] The prediction results are stored in a database and notified to relevant agencies.

[0195] Specific examples

[0196] The server inputs past hurricane data into a generative AI model for analysis, and then predicts the hurricane's path and the extent of the damage it will cause based on current weather data. This information is then sent to relevant organizations to help with advance preparations.

[0197] Detecting abnormal patterns and raising alerts

[0198] server

[0199] The server analyzes weather and environmental monitoring data using AI algorithms to detect abnormal patterns.

[0200] If an anomaly is detected, an alert is generated and notified to the specified device, using Firebase Cloud Messaging and Twilio APIs.

[0201] Specific examples

[0202] A server that monitors data from the seismometer detects abnormal tremors that could signal a major earthquake and immediately sends a warning to the smartphones of local residents.

[0203] Providing optimal evacuation routes

[0204] Terminal

[0205] The user's smartphone uses GPS to send current location information to the server.

[0206] The server takes into account traffic conditions and road closure information to calculate the optimal evacuation route, using the Google® Maps API and OpenStreetMap API.

[0207] The terminal provides the user with the evacuation route information received from the server, displays a map, and performs real-time navigation.

[0208] Specific examples

[0209] When a fire occurs, the user launches the app on their smartphone, and the server calculates the optimal evacuation route, taking into account traffic congestion information and closed roads, and provides it to the user in real time.

[0210] Damage prediction and resource allocation optimization

[0211] server

[0212] The server analyzes the collected data and past disaster data to predict the extent of damage and the impact on each region.

[0213] Based on the forecast data, the amount of relief supplies and medical resources needed is calculated and an efficient distribution plan is developed.

[0214] The allocation plan will be communicated to relevant agencies to assist in rapid response.

[0215] Specific examples

[0216] When a major typhoon is approaching, the server creates a list of predicted affected areas and needed relief supplies, and notifies relevant agencies to help them make prompt preparations.

[0217] Strengthening information exchange between affected areas and relief teams

[0218] User

[0219] Users use a smartphone app to input information about the damage situation and requests for help in natural language.

[0220] The server analyzes the input information, translates it appropriately, and notifies the relevant authorities. Generative AI models (e.g., GPT-4) are used for natural language processing.

[0221] Specific examples

[0222] When a victim types in Japanese, "Help me, my house has collapsed," the server translates this into English and quickly notifies rescue teams.

[0223] Prompt Sentence Examples

[0224] "Based on past hurricane data, analyze current weather data and predict future hurricane risk."

[0225] "Please provide sample code for an algorithm that analyzes collected sensor information and meteorological data to detect flood risk early."

[0226] "Please show us how to translate disaster information entered in Japanese into English and notify the relief team."

[0227] In this way, the present invention is a system that uses multi-layered technologies to enable real-time disaster countermeasures and minimize damage. Implementing this system is expected to significantly improve disaster preparedness and response, saving many lives and property.

[0228] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0229] Step 1: Data collection

[0230] server

[0231] The server periodically retrieves weather data from weather satellites and ground observation stations using APIs. It also receives environmental monitoring data such as seismometers and river water level gauges. This process uses scripts written in Python and a database management system (e.g., PostgreSQL). The input is raw data retrieved from the API, and the output is data stored in a database in an organized format.

[0232] Specific actions

[0233] The server receives data from weather satellites every 30 minutes and inserts it into a database in JSON format. It also receives data from seismometers and analyzes it in real time.

[0234] Step 2: Data storage and processing

[0235] server

[0236] The collected meteorological and environmental monitoring data is stored in a database in real time. This data is then immediately analyzed by AI algorithms using Python (e.g., TensorFlow). The input is the raw data points, and the output is the analyzed indicators and abnormal pattern detection results.

[0237] Specific actions

[0238] The server periodically reads the weather and sensor data stored in the database and analyzes it using a TensorFlow model. Any abnormal patterns detected are stored in a separate table and sent to the warning system.

[0239] Step 3: Detect and alert on abnormal patterns

[0240] server

[0241] The server uses AI algorithms to detect abnormal patterns, which involves recognizing different behavior compared to normal weather patterns. The input is the pre-processed data points, and the output is the identification of abnormal patterns and a corresponding warning message.

[0242] Specific actions

[0243] If an abnormally large tremor is detected in the seismograph data, the information is analyzed and a warning message is quickly generated and sent to relevant organizations and users' terminals.

[0244] Step 4: Optimize evacuation routes

[0245] Terminal

[0246] The user's smartphone uses GPS to obtain their current location and sends that information to a server. The server then uses real-time traffic and environmental data to calculate the optimal evacuation route. The input is the GPS location and current traffic and environmental data, and the output is the optimized route information.

[0247] Specific actions

[0248] In the event of a fire, when a user launches the evacuation app, the device sends its current location information to the server, which then calculates the optimal evacuation route, taking into account real-time traffic congestion information and road closures, and displays it on the device.

[0249] Step 5: Damage prediction and resource allocation

[0250] server

[0251] The server analyzes the collected data and past disaster data to predict the extent of damage and the impact on each region. It also calculates the required quantities of relief supplies and medical resources and creates an efficient distribution plan. The input is an integrated disaster dataset, and the output is the predicted extent of damage and a resource allocation plan.

[0252] Specific actions

[0253] If a major typhoon is approaching, the server analyzes multiple data points and runs a predictive model to create a list of affected areas and needed supplies, which is then immediately communicated to the appropriate agencies.

[0254] Step 6: Strengthen communication

[0255] User

[0256] Users can use a smartphone app to input information about the disaster situation and requests for assistance in natural language. The server analyzes this information, translates it appropriately, and notifies the relevant authorities. The input is text or voice data entered in natural language, and the output is translated text information.

[0257] Specific actions

[0258] When a victim voices a request for help in Japanese, the server translates it into English and quickly notifies rescue teams from overseas.

[0259] Step 7: Dispatch drones and robots and gather information

[0260] server

[0261] The server dispatches AI-equipped unmanned aerial vehicles and autonomous mobile devices to disaster-stricken areas to collect video and sensor information in real time. The collected data is analyzed by the server and provided to relevant organizations. The input is the video and sensor information collected in real time, and the output is analyzed information and specific instructions.

[0262] Specific actions

[0263] After an earthquake occurs, the server dispatches a drone to the site to collect aerial video data, which is then analyzed immediately and provided to rescue teams as information to grasp the situation on the ground.

[0264] Through these processing steps, this system aims to enable real-time disaster response and minimize damage, significantly improving disaster response preparation and execution, and saving many lives and property.

[0265] (Application example 1)

[0266] 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."

[0267] Recently, the frequency and intensity of natural disasters have increased, necessitating rapid response and evacuation. However, current disaster response systems lack the ability to collect and analyze information in real time, provide optimal evacuation routes, detect abnormal patterns early, and effectively share information between affected areas and rescue teams. Furthermore, language barriers mean that requests for help and the transmission of information take time. This makes it difficult to respond quickly and accurately when a disaster occurs, and poses the challenge of minimizing damage to human life and property.

[0268] 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.

[0269] In this invention, the server includes means for collecting and analyzing meteorological data and sensor data in real time, means for predicting future disaster risks based on past disaster data, means for detecting abnormal patterns and identifying signs of disaster, means for automatically issuing warnings based on the identified signs, means for calculating optimal evacuation routes and providing them to users in real time, means for supporting information exchange between disaster-stricken areas and rescue teams and performing automatic translation, and means for updating and providing evacuation route information in real time. This enables early collection and analysis of information, provision of optimal evacuation routes, early detection of abnormal patterns, and efficient information sharing, thereby realizing rapid and accurate measures in the event of a disaster.

[0270] "Weather data" refers to data related to weather collected from meteorological observation equipment, satellites, etc.

[0271] "Sensor data" refers to data about the environment and situation collected from various sensors (seismic sensors, water level gauges, temperature sensors, etc.).

[0272] "Real-time" means that collection and processing occur immediately, without any time delay.

[0273] "Past disaster data" refers to records and related information relating to disasters that have occurred in the past.

[0274] "Disaster risk" refers to the scale and impact of potential future disasters.

[0275] An "abnormal pattern" refers to a specific data fluctuation or behavior that is different from the norm.

[0276] "Disaster precursors" are signs or indicators that indicate a high probability of a disaster occurring.

[0277] A "warning" is a notification or alert to notify you of danger or risk.

[0278] An "evacuation route" is a route used to evacuate to a safe place in the event of a disaster.

[0279] "Information exchange" is the act of sharing information among multiple parties.

[0280] "Translation" refers to the conversion of written or spoken content in one language into another language.

[0281] An "unmanned aerial vehicle" is an aircraft that can be remotely controlled or fly autonomously, such as a drone.

[0282] "Affected area" means the area or area affected by a disaster.

[0283] "Resources" refers to the supplies, personnel, and equipment needed for relief operations and recovery.

[0284] "Automatic" means that a machine or system operates independently, without human intervention.

[0285] The present invention is a disaster recovery system, and is implemented by the following method and configuration.

[0286] 1. Real-time collection and analysis of meteorological and sensor data

[0287] The server collects weather data from meteorological observation equipment and satellites, and sensor data from various sensors (such as seismometers, water level gauges, and temperature sensors). This data is stored in a database in real time and analyzed by AI algorithms (such as TensorFlow and PyTorch). This makes it possible to detect unusual patterns that are out of the ordinary and identify signs of disaster.

[0288] Specific examples

[0289] The server detects ongoing heavy rainfall and confirms that water levels are rising rapidly from data from water level sensors, allowing for early detection of potential floods and response.

[0290] 2. Analysis of past disaster data and prediction of disaster risks

[0291] The server collects past disaster data, inputs it into an AI model, and analyzes it. Based on the analysis results, future disaster risks can be predicted. Data such as weather patterns, damage, and relief efforts are used to assess the risk in a specific area.

[0292] Specific examples

[0293] When predicting the path of a hurricane, the server analyzes current weather data based on past hurricane data to predict the possible extent and impact of damage, allowing necessary preparations to be made in advance.

[0294] 3. Detecting abnormal patterns and issuing warnings

[0295] The server analyzes the collected data in real time to detect abnormal patterns, and automatically issues warnings based on the identified signs. These warnings are sent as push notifications to users' smartphones or computers, and simultaneously to relevant authorities.

[0296] Specific examples

[0297] Servers monitoring data from local seismometers detect the signs of an earthquake that is building up in size and send warnings to local residents' smartphones.

[0298] 4. Calculating and providing optimal evacuation routes

[0299] The device (user's smartphone) uses GPS to send its current location information to the server. The server analyzes traffic conditions, road closure information, and real-time environmental conditions to calculate the optimal evacuation route. The calculation results are provided to the user's smartphone in real time.

[0300] Specific examples

[0301] In the event of a fire, users can launch the app on their smartphones and get real-time information on the optimal evacuation route. If the route is blocked, the server will calculate and provide a new route.

[0302] 5. Supporting information exchange between disaster-stricken areas and relief teams

[0303] Users can use a smartphone app to voice-input information about the damage situation and requests for assistance. The server analyzes the input using voice recognition (Google Speech-to-Text), translates it appropriately (Google Translate API), and automatically sends it to the relevant organizations. This allows information to be shared quickly, even between different languages.

[0304] Specific examples

[0305] When a victim types "rescue needed" in Japanese into a smartphone app, the server translates the message and transmits it in English to overseas rescue teams.

[0306] 6. Control and intelligence gathering of AI-equipped unmanned aerial vehicles and robots

[0307] The server dispatches AI-equipped unmanned aerial vehicles and robots to disaster-stricken areas and collects video and sensor information in real time. The collected data is analyzed by the server and provided to relevant organizations. The collected data is also used to create optimal resource allocation plans, streamlining rescue efforts.

[0308] Specific examples

[0309] After an earthquake occurs, the server uses unmanned aerial vehicles to collect detailed images of the affected area and transmits the analysis results to rescue teams in real time, enabling them to make quick decisions and take action.

[0310] Prompt Sentence Examples

[0311] "Is there an increased risk of flooding in this location? Please advise the best evacuation route. Also, please send a request for help in English."

[0312] Combining these functions will enable adequate preparation and rapid response to disasters, making it possible to protect many lives and property.

[0313] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0314] Step 1:

[0315] The server collects weather data from meteorological observation equipment and satellites, and sensor data from various sensors (seismic sensors, water level gauges, temperature sensors, etc.). The data is sent to the server in real time via API and stored in a database. The input is various weather data and sensor data, and the output is the stored data. Specifically, the data collection script periodically sends requests to the API endpoint and stores the obtained data in the database.

[0316] Step 2:

[0317] The server analyzes the collected data in real time to detect abnormal patterns. The input is weather data and sensor data stored in a database, and the output is the results of detecting abnormal patterns. AI algorithms (such as TensorFlow and PyTorch) are used to process the data and analyze data points that fall outside of normal ranges. Specifically, the data is input into an AI model, and a function is executed to detect anomalies.

[0318] Step 3:

[0319] The server collects past disaster data, inputs it into an AI model, and analyzes it. The input is historical disaster data, and the output is disaster risk prediction results. Data processing involves comparing past disaster data with current data and applying a model that predicts future risks. Specifically, past and real-time data are input into the AI ​​model, and a risk prediction function is executed.

[0320] Step 4:

[0321] The server automatically issues an alert based on the detected anomaly pattern. The input is the anomaly pattern detection result, and the output is an alert notification. Data processing involves triggering the anomaly detection result to send an alert message to a push notification service (such as Firebase). Specifically, the alert generation function receives the anomaly detection event and sends an appropriate message to the notification service.

[0322] Step 5:

[0323] The device uses GPS to send current location information to the server. The input is the user's current location information, and the output is the location information sent to the server. Specifically, the smartphone app obtains the GPS data and sends it to the server via an HTTP request.

[0324] Step 6:

[0325] The server analyzes traffic conditions, road closure information, and real-time environmental conditions to calculate the optimal evacuation route. The input is current location information and traffic / road information, and the output is the calculated evacuation route. Data processing involves applying an algorithm that takes into account information such as road closures and traffic delays to find the shortest and most optimal route. Specifically, the route calculation function is executed, and the generated route is sent to the user's device.

[0326] Step 7:

[0327] The server predicts the extent of damage and resource allocation based on the collected data. The input is real-time data and past disaster data, and the output is a resource allocation plan. Data processing involves using a damage prediction model to generate prediction results and calculate the amount of relief resources required. In concrete terms, the prediction function processes the data, and the results are linked to the resource management system.

[0328] Step 8:

[0329] The server analyzes the user's voice input, performs automatic translation, and sends the information to the relevant organizations. The input is the user's voice data, and the output is translated text. Data processing involves converting the voice into text using voice recognition (Google Speech-to-Text), and then translating it into another language using a translation system (Google Translate API). Specifically, the voice recognition function processes the voice data, and the translation function converts the text into another language and sends it to the relevant organizations.

[0330] Step 9:

[0331] The server controls AI-equipped unmanned aerial vehicles and robots and collects information on disaster sites in real time. The input is sensor data and video data from the unmanned aerial vehicles and robots, and the output is analyzed information. Data processing involves analyzing the collected data and extracting important information. Specifically, the control program operates the unmanned aerial vehicles and robots and sends the collected data to the analysis server.

[0332] These processing steps enable a quick and accurate response in the event of a disaster, ensuring the safety of users.

[0333] 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.

[0334] This invention is a disaster prevention system that utilizes AI, and combines mainly real-time collection and analysis of meteorological data and sensor data, analysis of past disaster data, detection and warning of abnormal patterns, optimization of evacuation routes, damage prediction and resource allocation, enhanced communication, utilization of AI-equipped drones and robots, and an emotion engine that recognizes user emotions. The specific operation of each function in the system of this invention will be explained.

[0335] Real-time collection and analysis of weather and sensor data

[0336] server

[0337] The server collects weather data from weather satellites and ground observation stations, and collects sensor data in real time from seismometers, river water level gauges, and other devices.

[0338] The collected data is stored in a database and analyzed using AI algorithms.

[0339] The server analyzes weather and sensor data to detect signs of disaster.

[0340] example

[0341] For example, during periods of heavy rain, the server collects data from various sensors and detects sudden rises in water levels. By analyzing this data, it is possible to identify potential floods early and take appropriate action.

[0342] Detecting abnormal patterns and issuing alerts

[0343] server

[0344] The server analyzes the collected weather and sensor data to detect any abnormal patterns.

[0345] If the identified abnormal pattern is judged to be a sign of a disaster, a warning message will be generated and sent to designated terminals and relevant organizations.

[0346] example

[0347] Servers monitoring seismometer data detect the signs of an earthquake that is building up in size and send warnings to local residents' smartphones.

[0348] Optimizing evacuation routes

[0349] Terminal

[0350] The device (user's smartphone) uses its GPS function to obtain current location information and periodically sends it to the server.

[0351] server

[0352] Based on the received current location information, the server analyzes real-time traffic and road information and calculates the optimal evacuation route.

[0353] Terminal

[0354] Information on the optimal evacuation route is sent to the user's device and displayed on the device's app.

[0355] example

[0356] In the event of a fire, users can launch the app on their smartphones and get real-time information on the optimal evacuation route. If the route is blocked, the server will calculate and provide a new route.

[0357] Damage prediction and resource allocation optimization

[0358] server

[0359] The server inputs past disaster data and current weather data into an AI model for analysis.

[0360] The AI ​​model will generate a damage scope and resource allocation plan and provide it to relevant agencies.

[0361] example

[0362] When a major typhoon is approaching, the server creates a list of predicted affected areas and needed relief supplies, and automatically sends it to the relevant organizations, allowing for faster preparations.

[0363] Strengthening communication

[0364] User

[0365] Users can use a smartphone app to input disaster situations and requests for help.

[0366] The entered information undergoes natural language processing and machine translation on the server and is then sent to the relevant organizations.

[0367] example

[0368] If a victim requests help from a rescue team in Japanese, they can input their voice into the app, and the server will translate the message into English and quickly transmit the information to rescue teams dispatched from overseas.

[0369] Utilizing AI-equipped drones and robots

[0370] server

[0371] The server controls AI-equipped drones and robots and collects information from disaster sites in real time.

[0372] The collected data is analyzed on a server and provided to relevant organizations.

[0373] example

[0374] After an earthquake occurs, the server uses drones to collect detailed images of the affected area and transmits the analysis results to rescue teams in real time, enabling them to make quick decisions and take action.

[0375] Introducing the Emotion Engine

[0376] Terminal

[0377] An emotion engine is built into the user's device (smartphone) and recognizes emotions from the user's voice and text in real time.

[0378] server

[0379] The emotion engine assesses the user's mental state and automatically contacts experts if necessary.

[0380] The content of evacuation instructions and warnings is optimized based on user emotional data.

[0381] example

[0382] If the emotion engine detects that the user is panicking during a disaster, the server notifies a professional counselor and sends a calming message to the user, encouraging them to take action to ensure their safety.

[0383] As described above, this invention is a system that combines various technologies to realize real-time disaster countermeasures and minimize damage. Implementing this system is expected to significantly improve disaster preparedness and response, protecting many lives and property.

[0384] The processing flow will be explained below.

[0385] Real-time collection and analysis of weather and sensor data

[0386] server

[0387] Step 1:

[0388] The server collects weather data from weather satellites and ground observation stations.

[0389] Data collected includes temperature, precipitation, wind speed, and wind direction.

[0390] Step 2:

[0391] The server collects data in real time from sensors such as seismometers and river water level gauges.

[0392] The data collected includes earthquake intensity and water level fluctuations.

[0393] Step 3:

[0394] The server stores the collected weather and sensor data in a database.

[0395] The database is designed with a structure that allows fast access for analysis.

[0396] Step 4:

[0397] The server inputs the stored data into an AI algorithm and begins analysis.

[0398] AI algorithms use machine learning models to detect anomalous patterns.

[0399] Detecting abnormal patterns and issuing alerts

[0400] server

[0401] Step 1:

[0402] The server analyzes weather and sensor data to detect unusual patterns.

[0403] Unusual patterns include sudden temperature changes, unusual seismic activity, and sudden rises in river levels.

[0404] Step 2:

[0405] AI algorithms determine whether unusual patterns are a sign of disaster.

[0406] The criteria for judgment are determined by comparing with past data and whether or not it exceeds a certain threshold.

[0407] Step 3:

[0408] The server generates a warning message if it determines that a disaster is imminent.

[0409] The warning message includes information such as the type of disaster, predicted damage, and the need for evacuation.

[0410] Step 4:

[0411] The server sends the generated warning message to designated devices (smartphones, PCs) and relevant organizations.

[0412] Optimizing evacuation routes

[0413] Terminal

[0414] Step 1:

[0415] The user's device acquires current location information using the GPS function.

[0416] The acquired location information is sent to the server at regular intervals.

[0417] server

[0418] Step 2:

[0419] The server analyzes real-time traffic and road information based on the received current location information.

[0420] Analysis includes current traffic conditions, road closure information, and disaster situations.

[0421] Step 3:

[0422] The server uses AI algorithms to calculate the optimal evacuation route.

[0423] The calculation results show the safest evacuation route in the shortest time.

[0424] Terminal

[0425] Step 4:

[0426] The optimal evacuation route information received from the server is displayed on the user's terminal.

[0427] Evacuation route information can be visually confirmed in map format.

[0428] Step 5:

[0429] If a road is blocked during an evacuation, the server recalculates a new route and sends updates to the device.

[0430] Damage prediction and resource allocation optimization

[0431] server

[0432] Step 1:

[0433] The server collects past disaster data and current weather data and inputs it into the AI ​​model.

[0434] The data includes records of damage caused by past disasters, weather conditions, and resource allocation.

[0435] Step 2:

[0436] Based on the collected data, the AI ​​model identifies the extent of damage that can be predicted in the future based on the current situation.

[0437] The prediction includes the probability of occurrence and the extent of damage.

[0438] Step 3:

[0439] The server generates a resource allocation plan based on the prediction results.

[0440] The plan includes necessary relief supplies, medical resources, response times, etc.

[0441] Step 4:

[0442] The resource allocation plan will be sent to the relevant agencies to support a rapid response.

[0443] Strengthening communication

[0444] User

[0445] Step 1:

[0446] Users use a smartphone app to input information about the disaster situation and requests for help.

[0447] Input methods include voice input and text input.

[0448] server

[0449] Step 2:

[0450] The server analyzes the input information using a natural language processing algorithm and extracts the necessary information.

[0451] If information is entered in multiple languages, it is automatically machine translated.

[0452] Step 3:

[0453] The extracted and translated information is automatically sent to relevant agencies and relief teams.

[0454] Utilizing AI-equipped drones and robots

[0455] server

[0456] Step 1:

[0457] The server controls AI-equipped drones and robots and dispatches them to disaster sites.

[0458] Drones and robots operate according to set flight and movement paths.

[0459] Step 2:

[0460] Drones and robots collect footage and sensor data from disaster sites in real time.

[0461] The collected data is immediately sent to the server.

[0462] Step 3:

[0463] The server analyzes the received data and grasps the situation on site.

[0464] If necessary, the operation of drones and robots will be adjusted to gather more information.

[0465] Step 4:

[0466] The analysis results will be provided to relevant agencies and rescue teams to assist in rapid response.

[0467] Introducing the Emotion Engine

[0468] Terminal

[0469] Step 1:

[0470] The emotion engine built into the user's device analyzes the user's voice and text in real time and recognizes emotions.

[0471] server

[0472] Step 2:

[0473] The server receives the emotion data sent from the emotion engine and evaluates the mental state of the user.

[0474] If the user's emotions are judged to be dangerous, an appropriate alert or support message will be generated.

[0475] Step 3:

[0476] If necessary, emotional data will be used to automatically connect you with a specialist (medical professional or counselor).

[0477] The expert is informed of the user's current emotional state and situation.

[0478] Step 4:

[0479] The server analyzes the user's emotional data and optimizes the evacuation instructions and warnings to suit the user's psychological state.

[0480] Optimized warnings and instructions are sent to the user's device.

[0481] example

[0482] In the event of a disaster, if the system detects a panicked state in the user's voice, the emotion engine will display a message to alleviate the user's anxiety. It will also automatically notify a professional counselor based on the user's situation and provide necessary support.

[0483] Based on the above steps, the system of the present invention can realize quick and effective countermeasures against natural disasters and minimize damage, which is expected to save many lives and properties.

[0484] Example 2

[0485] 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."

[0486] In recent years, natural disasters have become more frequent, and rapid and accurate responses are required to minimize the damage they cause. However, existing systems have limitations in real-time data collection and analysis, evacuation route optimization, and user emotion recognition. Furthermore, there is a lack of information collection and damage prediction using autonomous devices equipped with AI. This can lead to delayed responses in the event of a disaster, potentially exacerbating the damage.

[0487] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting and analyzing meteorological data and sensor data in real time, means for predicting future disaster risks based on past disaster data, means for detecting abnormal patterns and identifying precursors of disasters, means for automatically issuing warnings based on the identified precursors, means for calculating optimal evacuation routes and providing them to the user in real time, means for supporting information exchange between the disaster-stricken area and rescue teams, means for acquiring user location information and calculating optimal evacuation routes based on real-time traffic data, and means for recognizing the user's emotions and providing appropriate instructions and warnings. This enables disaster countermeasures to be taken in real time, minimizing damage.

[0488] "Weather data" refers to information about atmospheric conditions, including temperature, humidity, wind speed, air pressure, and precipitation.

[0489] "Sensor data" refers to various types of measurement information obtained by sensors such as seismometers and river water level gauges, and includes data indicating environmental and physical conditions.

[0490] "Real-time" refers to data and information being collected, processed, and distributed almost instantly.

[0491] "Analysis" refers to analyzing collected data and extracting meaning and patterns from it.

[0492] "AI algorithms" refer to calculation methods and mathematical models that utilize artificial intelligence technology and are used to predict and analyze data.

[0493] "Past disaster data" refers to recorded information about disasters that have occurred in the past, and includes data such as the extent of damage, causes of occurrence, and effectiveness of countermeasures.

[0494] "Disaster risk" is an indicator that shows the possibility of a disaster occurring and the extent of its impact.

[0495] An "abnormal pattern" refers to a situation in which data movements or trends that differ from normal are observed.

[0496] "Disaster precursors" refer to abnormal changes in data or signs of actual natural phenomena that occur before a disaster occurs.

[0497] "Warning" refers to a message that alerts you to an abnormal situation or potential danger.

[0498] An "evacuation route" refers to the route that should be taken to evacuate to a safe place in the event of a disaster.

[0499] "Information exchange" refers to the exchange of data or messages between two or more parties.

[0500] A "rescue team" refers to a specialized unit formed to rescue and provide support to victims in the event of a disaster.

[0501] "Location information" refers to latitude and longitude information for a specific location or region.

[0502] "Traffic data" refers to information about current road conditions and traffic flow.

[0503] "User's emotions" refers to information that represents the emotions and mental state that the user is experiencing.

[0504] "Autonomous devices" refer to devices such as drones and robots that are equipped with AI and machine learning technology and operate automatically.

[0505] "Resource allocation" refers to the efficient distribution of necessary materials and personnel.

[0506] This invention is a disaster prevention system that utilizes AI, and combines mainly real-time collection and analysis of meteorological data and sensor data, analysis of past disaster data, detection and warning of abnormal patterns, optimization of evacuation routes, damage prediction and resource allocation, enhanced communication, utilization of autonomous devices equipped with AI, and an emotion engine that recognizes user emotions. Each function of this invention is explained in detail below.

[0507] Real-time collection and analysis of weather and sensor data

[0508] server

[0509] The server retrieves weather data provided by weather satellites and ground observation stations via an API. It also collects real-time data from various sensors, such as seismometers and river water level gauges. The API is implemented using Python to manage data requests and responses.

[0510] The collected data is stored in a PostgreSQL database and analyzed using an AI algorithm powered by TensorFlow, which allows for the detection of abnormal patterns and signs of disasters from large amounts of data.

[0511] Specific examples

[0512] For example, to detect the risk of flooding due to heavy rain at an early stage, the server analyzes meteorological data and water level gauge data to detect abnormally high water levels, and sends a warning to the relevant authorities.

[0513] Prompt Sentence Examples

[0514] "If the water level sensor detects an abnormality during heavy rainfall, please provide the analysis procedures for taking appropriate action."

[0515] Detecting abnormal patterns and issuing alerts

[0516] server

[0517] The server uses PySpark to perform distributed processing of collected weather and sensor data to detect abnormal patterns. If the detected abnormal pattern is judged to be a sign of a disaster, a warning message is generated on the spot.

[0518] The generated warning message is sent to designated user devices and relevant organizations using the Twilio API.

[0519] Specific examples

[0520] If an abnormal vibration pattern is detected from the seismometer data, the server analyzes it as a precursor to an earthquake and sends a warning message to the smartphones of local residents.

[0521] Prompt Sentence Examples

[0522] "If an anomaly is detected in the seismometer data, please explain the procedure for generating a warning message and sending it to the relevant authorities."

[0523] Optimizing evacuation routes

[0524] Terminal

[0525] The user's smartphone acquires the current location information using the GPS function and periodically sends it to the server, using HTTP requests and WebSockets for communication.

[0526] server

[0527] The server obtains real-time traffic data using the Google Maps API and Waze API, and calculates the optimal evacuation route using the Dijkstra algorithm and the A algorithm.

[0528] The calculated optimal evacuation route is sent to the user's smartphone app and displayed on the screen.

[0529] Specific examples

[0530] In the event of a fire, users can receive real-time evacuation route information via a smartphone app, and if a road is closed, the server will calculate and provide a new route.

[0531] Prompt Sentence Examples

[0532] "Explain the steps to use GPS data and real-time traffic information to calculate the optimal route to optimize evacuation routes."

[0533] Damage prediction and resource allocation optimization

[0534] server

[0535] The server collects past disaster data and current weather data and uses OpenAI (registered trademark) generative AI models to predict damage.

[0536] Based on the analysis results of the AI ​​model, a distribution plan for necessary relief supplies and medical resources is generated.

[0537] Specific examples

[0538] When a major typhoon is approaching, the server creates a list of areas expected to be affected and necessary supplies, and automatically notifies relevant organizations, enabling effective preparations.

[0539] Prompt Sentence Examples

[0540] "When a large typhoon is approaching, please explain the procedure for generating damage forecasts and lists of relief supplies and notifying relevant organizations."

[0541] Strengthening communication

[0542] User

[0543] Users can use a smartphone app to input information about the situation in the affected area and requests for help. Information can be entered using both voice and text.

[0544] server

[0545] The server uses the Google Cloud Natural Language API to parse the user's input and perform machine translation if necessary, then sends the translated information to the appropriate authorities.

[0546] Specific examples

[0547] When a victim requests help in Japanese, they can input their voice and the server will automatically translate it into English and transmit it to a rescue team dispatched from overseas.

[0548] Prompt Sentence Examples

[0549] "Please explain the procedure for analyzing the voice input of the victim using natural language processing and machine translation and conveying it appropriately to the rescue team."

[0550] Utilizing AI-powered autonomous machines

[0551] server

[0552] The server will control autonomous devices (e.g., drones and robots) equipped with AI technology to collect detailed information on disaster sites, including the use of autonomous devices equipped with NVIDIA Jetson.

[0553] The collected data is sent to a server in real time, and the analysis results are provided to relevant organizations.

[0554] Specific examples

[0555] After an earthquake occurs, the server uses drones to collect footage of the affected area and transmits the analysis results to rescue teams in real time.

[0556] Prompt Sentence Examples

[0557] "Please explain the procedure for using drones to collect information from disaster sites and analyze it in real time."

[0558] User Emotion Recognition

[0559] Terminal

[0560] The user's smartphone is equipped with an emotion engine that uses Amazon Rekognition, which recognizes emotions from the user's voice and text.

[0561] server

[0562] The server uses an emotion engine to assess the user's mental state and notify experts as necessary. It also optimizes evacuation instructions and warnings based on emotion data.

[0563] Specific examples

[0564] During a disaster, if the emotion engine detects that the user is in a panic, the server notifies a counselor and sends a calming message to the user.

[0565] Prompt Sentence Examples

[0566] "Please explain the procedure for analyzing user emotion data and providing appropriate responses during a disaster."

[0567] As described above, this invention is a system that combines various technologies to realize real-time disaster countermeasures and minimize damage. Implementing this system is expected to significantly improve disaster preparedness and response, protecting many lives and property.

[0568] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0569] Real-time collection and analysis of weather and sensor data

[0570] Step 1: Collect data

[0571] server

[0572] Input: Weather data from weather satellites and observation stations, sensor data such as seismometers and river level gauges.

[0573] Specific operation: The server uses a Python script to periodically execute API requests to obtain weather data and various sensor data.

[0574] Output: The retrieved data.

[0575] Step 2: Store the data in the database

[0576] server

[0577] Input: Collected weather and sensor data.

[0578] Specific operation: The server connects to a PostgreSQL database and stores data in chronological order.

[0579] Output: Database entries organized chronologically.

[0580] Step 3: Analyze the data

[0581] server

[0582] Input: Weather and sensor data stored in a database.

[0583] How it works: The server uses TensorFlow to analyze the collected data and apply machine learning models to detect anomalous patterns.

[0584] Output: Analysis results (detection of abnormal patterns).

[0585] Detecting abnormal patterns and issuing alerts

[0586] Step 4: Detecting abnormal patterns

[0587] server

[0588] Input: Analysis results (detection of abnormal patterns).

[0589] What it does: The server verifies the analysis results and flags any abnormal patterns that could be a sign of disaster.

[0590] Output: Disaster warning flag.

[0591] Step 5: Generate a warning message

[0592] server

[0593] Input: Disaster omen flag.

[0594] Specific operation: The server uses the warning message template to generate a customized warning message.

[0595] Output: A warning message.

[0596] Step 6: Sending a warning message

[0597] server

[0598] Input: Warning message.

[0599] Specific operation: The server uses the Twilio API to send a warning message to the specified device or relevant organization.

[0600] Output: The warning message sent.

[0601] Optimizing evacuation routes

[0602] Step 7: Obtaining User Location

[0603] Terminal

[0604] Input: GPS data from your smartphone.

[0605] Specific operation: The device periodically obtains its current location information and sends it to the server.

[0606] Output: The location sent.

[0607] Step 8: Acquiring real-time traffic data

[0608] server

[0609] Input: Traffic data from Google Maps API and Waze API.

[0610] Specific operation: The server executes an API request to obtain the latest traffic data and road information.

[0611] Output: Latest traffic data.

[0612] Step 9: Calculate evacuation routes

[0613] server

[0614] Input: User location and real-time traffic data.

[0615] Specific operation: Based on the acquired data, the optimal evacuation route is calculated using the Dijkstra algorithm or the A algorithm.

[0616] Output: Optimal evacuation route.

[0617] Step 10: Send and display route information

[0618] Terminal

[0619] Input: The optimal evacuation route sent by the server.

[0620] Specific operation: The device displays the received evacuation route information to the user. Specifically, a dedicated app displays the route on a map.

[0621] Output: Evacuation route displayed on the screen.

[0622] Damage prediction and resource allocation optimization

[0623] Step 11: Collect historical disaster data and current weather data

[0624] server

[0625] Input: Historical disaster data and current weather data.

[0626] Specific operation: The server retrieves past disaster data from an existing database and collects current weather data through an API.

[0627] Output: Collected data.

[0628] Step 12: Damage prediction analysis

[0629] server

[0630] Input: Historical disaster data and current weather data.

[0631] Specific operation: The server uses a generative AI model (such as OpenAI) to analyze damage predictions.

[0632] Output: Damage prediction results.

[0633] Step 13: Create a resource allocation plan

[0634] server

[0635] Input: Damage prediction results.

[0636] Specific operation: The server creates a list of needed relief supplies and medical resources and generates a resource allocation plan.

[0637] Output: Resource allocation plan.

[0638] Step 14: Notify appropriate authorities

[0639] server

[0640] Input: Resource Allocation Plan.

[0641] Specific operation: The server sends the resource allocation plan to the relevant institutions.

[0642] Output: The submitted resource allocation plan.

[0643] Strengthening communication

[0644] Step 15: Getting User Input

[0645] User

[0646] Input: Situation in the affected area and request for help (voice or text).

[0647] Specific operation: The user inputs information using a smartphone app.

[0648] Output: Data sent to the server.

[0649] Step 16: Natural Language Processing and Machine Translation

[0650] server

[0651] Input: Data entered by the user.

[0652] What it does: The server uses the Google Cloud Natural Language API to parse the data and perform machine translation if necessary.

[0653] Output: The translated data.

[0654] Step 17: Send to the appropriate authorities

[0655] server

[0656] Input: Translated data.

[0657] Specific operation: The server sends the data to the relevant organization.

[0658] Output: The data sent.

[0659] Utilizing AI-powered autonomous machines

[0660] Step 18: Controlling Autonomous Devices

[0661] server

[0662] Input: Operation instructions.

[0663] Specific operation: The server issues commands to control autonomous devices (drones, robots) equipped with AI technology.

[0664] Output: Control signals to autonomous devices.

[0665] Step 19: Data Collection

[0666] autonomous equipment

[0667] Input: Site condition data.

[0668] Specific operation: The autonomous device collects video and sensor data from the disaster site and transmits it to a server in real time.

[0669] Output: Data sent to the server.

[0670] Step 20: Data Analysis

[0671] server

[0672] Input: Transmitted data from autonomous devices.

[0673] Specific operation: The server analyzes the collected data and evaluates the situation on site in detail.

[0674] Output: Analysis results.

[0675] Step 21: Provide to relevant organizations

[0676] server

[0677] Input: Analysis results.

[0678] Specific operation: The server provides the analysis results to the relevant organizations.

[0679] Output:Notification to relevant authorities.

[0680] User Emotion Recognition

[0681] Step 22: Collect Emotional Data

[0682] Terminal

[0683] Input: User voice or text data.

[0684] What it does: The device uses Amazon Rekognition to collect user emotion data.

[0685] Output: Collected emotion data.

[0686] Step 23: Emotion Recognition

[0687] server

[0688] Input: Emotion data.

[0689] Specific operation: The server analyzes the collected emotional data and evaluates the user's mental state in real time.

[0690] Output: Emotion evaluation results.

[0691] Step 24: Collaborate with experts

[0692] server

[0693] Input: Emotion assessment results.

[0694] Specific behavior: The server uses the Twilio API to notify a professional (such as a counselor) as needed.

[0695] Output:Notification to experts.

[0696] Step 25: Optimizing Instructions and Warnings

[0697] server

[0698] Input: Emotion assessment results.

[0699] Specific operation: The server generates appropriate evacuation instructions and warning messages based on the user's mental state and sends them to the terminal.

[0700] Output: Customized instructions and warning messages to the user.

[0701] (Application example 2)

[0702] 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."

[0703] Conventional disaster response systems require a wide range of functions, including not only real-time data collection and analysis, but also the detection of abnormal patterns, the issuance of warnings, and the calculation of evacuation routes. However, these systems are limited in their functionality during disasters, when a rapid and accurate response is required. Their application to autonomous vehicles is particularly limited, and they often fail to function adequately. Furthermore, it is difficult to provide prompt and optimal evacuation instructions that take into account the situational awareness and emotional state of disaster victims, which is a major obstacle to safe evacuation.

[0704] The identification processing 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 and analyzing weather data and sensor data in real time, means for predicting future disaster risks based on past disaster data, means for detecting abnormal patterns and identifying precursors of disasters, means for automatically issuing warnings based on the identified precursors, means for calculating optimal evacuation routes and providing them to autonomously driven vehicles in real time, means for supporting information exchange between the disaster-stricken area and rescue teams, means for recognizing the user's emotions and promoting safe evacuation behavior, and means for calculating and providing optimal evacuation routes for autonomously driven vehicles in the event of a disaster. This enables autonomously driven vehicles to evacuate quickly and safely in the event of a disaster, and by instructing appropriate evacuation behavior according to the emotional state of disaster victims, it is possible to significantly improve the efficiency and safety of evacuation.

[0705] "Weather data" is a general term for information collected from meteorological satellites and ground observation stations, and includes data on weather, temperature, precipitation, wind speed, etc.

[0706] "Sensor data" is a general term for data collected in real time from seismometers, river water level gauges, and other environmental sensors.

[0707] "Historical disaster data" refers to a database of records and information about disasters that have occurred in the past, including details about the type, scale, and impact of such disasters.

[0708] An "abnormal pattern" refers to data that shows abnormal fluctuations or behavior outside the normal data range, and is detected as a sign of a disaster.

[0709] A "warning" is a warning message sent to users and relevant organizations when a sign of a disaster is detected.

[0710] The "optimal evacuation route" is a route calculated based on real-time data that will allow users to evacuate quickly and safely in the event of a disaster.

[0711] An "autonomous vehicle" is a vehicle that uses AI technology to move and drive autonomously, eliminating the need for a driver.

[0712] "Disaster area" refers to an area that has been affected by a disaster and is in need of assistance and rescue.

[0713] A "rescue team" refers to a specialized organization or group that carries out relief activities when a disaster occurs, including rescue teams and first aid medical teams.

[0714] "Emotion recognition means" refers to technologies and algorithms that analyze a user's voice or text to automatically determine their current emotional state.

[0715] A "drone" refers to an unmanned aircraft that can be remotely controlled and fly autonomously, and is used to collect information at disaster sites.

[0716] "Means to promote evacuation behavior" is a system that takes into account the user's emotional state and surrounding circumstances to instruct and guide the optimal evacuation method and behavior.

[0717] This invention is a disaster prevention system that utilizes AI, and its main functions are as follows:

[0718] Real-time collection and analysis of weather and sensor data

[0719] The server collects meteorological data from weather satellites and ground observation stations, and also acquires real-time sensor data from seismometers and river water level gauges. This data is stored in a database and analyzed by AI algorithms. For example, if heavy rain continues, a sudden rise in river water levels can be detected, allowing for early detection of the possibility of flooding.

[0720] Detecting abnormal patterns and issuing alerts

[0721] The server analyzes the collected weather and sensor data to detect any abnormal patterns. If this abnormal pattern is determined to be a sign of a disaster, a warning message is automatically generated and sent to the user's smartphone and relevant organizations. For example, if seismometer data detects a sign of a large earthquake, this warning will be sent to local residents.

[0722] Calculating and providing optimal evacuation routes

[0723] The device (user's smartphone) uses its GPS function to obtain current location information and sends it to the server. The server analyzes this current location information along with real-time traffic and road information to calculate the optimal evacuation route. This information is sent to the user's device, and the evacuation route is displayed. For example, in the event of a fire, the optimal evacuation route is provided in real time.

[0724] Damage prediction and resource allocation optimization

[0725] The server inputs past disaster data and current weather data into the AI ​​model to generate a damage scope and resource allocation plan, allowing relevant agencies to quickly prepare response measures. For example, if a typhoon is approaching, damage forecasts and a list of needed relief supplies are immediately provided.

[0726] Utilizing AI-enabled drones and robots

[0727] The server will control AI-equipped drones and robots to collect information on disaster sites in real time and provide the analysis results to relevant organizations. For example, after an earthquake, drones can collect detailed images of the affected area, enabling them to quickly provide information to rescue teams.

[0728] Introducing the Emotion Engine

[0729] The user's device is equipped with an emotion engine that recognizes emotions from the user's voice and text in real time. The server evaluates the user's emotional state and responds by contacting experts as necessary. The server also optimizes evacuation instructions and warnings based on the emotion data. For example, if the emotion engine detects that the user is panicking during a disaster, it will notify a counselor and send the user a message to calm them down.

[0730] Specific examples

[0731] For example, when a typhoon is approaching, the system will collect weather and sensor data, and calculate and provide the optimal evacuation route for autonomous vehicles in real time. Furthermore, if the emotion engine detects that the user in the car is in a state of panic, it can also send a reassuring message.

[0732] Prompt Sentence Examples

[0733] "Calculate the evacuation route for your current location and lead you to a safe location."

[0734] "Generate a damage forecast and a list of necessary relief supplies in case of a typhoon."

[0735] "When users are panicking, send them a reassuring message."

[0736] In this way, the AI-based disaster prevention system provides comprehensive support, from collecting and analyzing real-time data to providing optimal evacuation routes and responding based on the user's emotional state.

[0737] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0738] Step 1: Collect weather and sensor data

[0739] The server collects meteorological data from meteorological satellites and ground observation stations, and acquires real-time data from various sensors, such as seismometers and river water level gauges. The input data consists of meteorological information and sensor information, which are then stored in a database.

[0740] Step 2: Analyze data to detect abnormal patterns

[0741] The server analyzes the collected weather and sensor data using AI algorithms. The input is the latest weather and sensor data, and the output is the detection of abnormal patterns. If an abnormality is detected, an analysis is performed to determine whether it is a sign of a disaster.

[0742] Step 3: Automatically generate and issue warning messages

[0743] If the server determines that an abnormal pattern is a sign of a disaster, it automatically generates a warning message and sends it to a designated device (the user's smartphone or a relevant institution). The input is the abnormal pattern detection result, and the output is a warning message.

[0744] Step 4: Get the user's location

[0745] The device (user's smartphone) uses its GPS function to obtain the user's current location information and sends it to the server. The input is GPS data, and the output is the current location information.

[0746] Step 5: Calculate the optimal evacuation route

[0747] The server analyzes real-time traffic and road information based on the received current location information and calculates the optimal evacuation route. The input is current location information, traffic data, and road information, and the output is the optimal evacuation route.

[0748] Step 6: Provide evacuation routes

[0749] The server sends the calculated optimal evacuation route to the user's device and displays the route. The input is the optimal evacuation route information, and the output is the display of the evacuation route.

[0750] Step 7: Damage prediction and resource allocation planning

[0751] The server inputs past disaster data and current weather data into the AI ​​model to generate a damage extent and resource allocation plan. The input is past disaster data and weather data, and the output is a damage forecast and resource allocation plan.

[0752] Step 8: Drone and robot control and intelligence gathering

[0753] The server controls AI-equipped drones and robots and collects information on disaster sites in real time. The input is control instructions for the drones and robots, and the output is the collected on-site information.

[0754] Step 9: Recognizing user emotions with the emotion engine

[0755] The device's emotion engine recognizes emotions from the user's voice and text in real time. The input is the user's voice and text data, and the output is the emotion recognition result.

[0756] Step 10: Responding to a panic attack

[0757] If the server detects the user's emotional state (e.g., panic), it automatically contacts an expert and sends a calming message to the user. The input is the user's emotion recognition result, and the output is a notification to the expert and a message to the user.

[0758] 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.

[0759] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0760] 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.

[0761] [Second embodiment]

[0762] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0763] 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.

[0764] 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).

[0765] 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.

[0766] 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.

[0767] 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).

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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."

[0774] This invention is a disaster prevention system that utilizes AI, and its main functions are the real-time collection and analysis of meteorological and sensor data, analysis of past disaster data, detection and warning of abnormal patterns, optimization of evacuation routes, damage prediction and resource allocation, enhanced communication, and utilization of AI-equipped drones and robots. The specific operation of each function in the system of this invention will be explained below.

[0775] Real-time collection and analysis of weather and sensor data

[0776] server

[0777] The server periodically collects weather data from weather satellites and ground observation stations, as well as sensor data from seismometers, river level gauges, and other sources.

[0778] The collected data is stored in a database in real time and is then analyzed using AI algorithms.

[0779] The server can detect signs of disaster by linking weather data with sensor data.

[0780] example

[0781] During periods of heavy rain, the server collects data from various sensors and detects sudden rises in water levels. By analyzing this data, it is possible to identify potential floods early and take appropriate action.

[0782] Analysis of past disaster data and disaster risk prediction

[0783] server

[0784] The server collects data from past disasters, inputs it into an AI model, and performs analysis.

[0785] The AI ​​model predicts future disaster risks based on data such as weather patterns, damage, and relief efforts.

[0786] Based on the forecast data, we identify areas where disasters may occur and the scale of the damage.

[0787] example

[0788] When predicting the path of a hurricane, the server analyzes current weather data based on past hurricane data to predict the possible extent and intensity of damage, which can be useful for making necessary preparations in advance.

[0789] Detecting abnormal patterns and issuing alerts

[0790] server

[0791] The server analyzes normal weather and sensor data to detect any unusual patterns.

[0792] Based on the identified abnormal patterns, an alert is generated if they are deemed to be a sign of a disaster.

[0793] The warning will be sent to a designated device (smartphone, PC) and simultaneously sent to relevant agencies.

[0794] example

[0795] Servers monitoring seismometer data detect the signs of an earthquake that is building up in size and send warnings to local residents' smartphones.

[0796] Optimizing evacuation routes

[0797] Terminal

[0798] The device (user's smartphone) uses GPS to send current location information to the server.

[0799] The server analyzes traffic conditions, road closure information, and real-time environmental conditions to calculate the optimal evacuation route.

[0800] The terminal provides the user with the evacuation route received from the server, assisting in evacuation in the event of an emergency.

[0801] example

[0802] In the event of a fire, users can launch the app on their smartphones and get real-time information on the optimal evacuation route. If the route is blocked, the server will calculate and provide a new route.

[0803] Damage prediction and resource allocation optimization

[0804] server

[0805] The server uses the collected data and past disaster data to predict the extent of damage and the impact on each region.

[0806] Based on the forecast data, we calculate the amount of relief supplies and medical resources needed and create a plan for efficient allocation.

[0807] Resource allocation plans will be shared with relevant agencies, enabling rapid response.

[0808] example

[0809] When a major typhoon is approaching, the server creates a list of predicted affected areas and needed relief supplies, and automatically sends it to the relevant organizations, allowing for faster preparations.

[0810] Strengthening communication

[0811] User

[0812] Users can use a smartphone app to input information about the damage situation and requests for help in natural language.

[0813] The server analyzes the entered information, translates it appropriately, and automatically sends it to the relevant organizations.

[0814] example

[0815] If a victim requests help from a rescue team in Japanese, they can input their voice into the app, and the server will translate the message into English and quickly transmit the information to rescue teams dispatched from overseas.

[0816] Utilizing AI-equipped drones and robots

[0817] server

[0818] The server dispatches AI-equipped drones and robots to disaster-stricken areas and collects video and sensor information in real time.

[0819] The collected data is analyzed on a server and provided to relevant organizations.

[0820] example

[0821] After an earthquake occurs, the server uses drones to collect detailed images of the affected area and transmits the analysis results to rescue teams in real time, enabling them to make quick decisions and take action.

[0822] As described above, this invention is a system that combines various technologies to enable real-time disaster countermeasures and minimize damage. Implementing this system is expected to significantly improve disaster preparedness and response, protecting many lives and property.

[0823] The processing flow will be explained below.

[0824] 1. Early Warning System

[0825] server

[0826] Step 1:

[0827] The server collects weather data from weather satellites and ground observation stations.

[0828] Similarly, sensor data is collected in real time from seismometers, river water level gauges, and other sources.

[0829] Step 2:

[0830] The collected weather data and sensor data are stored in a database.

[0831] The stored data is fed into AI algorithms to detect abnormal patterns.

[0832] Step 3:

[0833] AI algorithms analyze the data and detect unusual patterns and fluctuations.

[0834] Based on the detected abnormal data, it is determined whether it is a sign of a disaster.

[0835] Step 4:

[0836] If it is determined to be a sign of a disaster, a warning message will be generated.

[0837] This warning message is sent to designated terminals and relevant organizations.

[0838] 2. Evacuation route optimization

[0839] Terminal

[0840] Step 1:

[0841] The user's device (smartphone) obtains current location information using the GPS function.

[0842] The current location information is periodically sent to the server.

[0843] server

[0844] Step 2:

[0845] The server analyzes real-time traffic and road information based on the received current location information.

[0846] Uses AI algorithms to calculate optimal evacuation routes.

[0847] Terminal

[0848] Step 3:

[0849] The optimal evacuation route information is sent to the user's terminal.

[0850] Evacuation route information is displayed on the device app and provided to the user.

[0851] Step 4:

[0852] If roads are closed during an evacuation, the server recalculates a new route and sends updates to the device.

[0853] 3. Damage prediction and risk analysis

[0854] server

[0855] Step 1:

[0856] The server collects past disaster data and current weather data and inputs it into the AI ​​model.

[0857] The AI ​​model predicts the extent and impact of disasters based on past data.

[0858] Step 2:

[0859] Based on the prediction results, a risk assessment is carried out.

[0860] Based on the results of the risk assessment, identify the amount of resources needed (relief supplies, medical resources, etc.).

[0861] Step 3:

[0862] Prepare a resource allocation plan and provide it to relevant agencies and users.

[0863] 4. Strengthening communication

[0864] User

[0865] Step 1:

[0866] Users enter disaster situations and requests for assistance into the app.

[0867] Input methods include voice input and text input.

[0868] server

[0869] Step 2:

[0870] The server feeds the entered information into natural language processing and machine translation algorithms.

[0871] Carry out translation between different languages.

[0872] Step 3:

[0873] Translated information will be sent to rescue teams and relevant agencies.

[0874] 5. Use of drones and robots

[0875] server

[0876] Step 1:

[0877] The server will control AI-equipped drones and robots and begin collecting information from the disaster site.

[0878] The drone follows a set flight path and collects data in real time.

[0879] Step 2:

[0880] The server analyzes the collected data in real time to grasp the detailed situation at the disaster site.

[0881] If necessary, the drone or robot's movements will be adjusted to gather more detailed information.

[0882] Step 3:

[0883] The analysis results will be provided to relevant agencies and rescue teams to assist in rapid response.

[0884] 6. Demand forecasting and resource allocation

[0885] server

[0886] Step 1:

[0887] The server inputs past disaster data and current situation data into the AI ​​model.

[0888] AI models predict demand (food, water, medical supplies, etc.) after a disaster occurs.

[0889] Step 2:

[0890] Calculate the amount of relief supplies and medical resources needed based on forecast data.

[0891] Develop distribution plans and support efficient relief efforts.

[0892] Step 3:

[0893] The allocation plan will be provided to relevant organizations and users to ensure appropriate resource allocation.

[0894] Based on these steps, the system of the present invention can realize rapid and effective countermeasures against natural disasters, and can save many lives and properties.

[0895] Example 1

[0896] 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."

[0897] In recent years, the frequency of natural disasters has increased, and the resulting damage has become more severe. This has created a need for systems that can quickly and accurately predict disaster risks and minimize damage. However, current disaster response systems lack the precision to collect and analyze real-time data, and are lacking in the ability to detect abnormal situations early and issue warnings. Furthermore, improvements are needed to optimize evacuation routes and improve the efficiency of damage prediction and resource allocation. Furthermore, there is a lack of means to facilitate communication between disaster-stricken areas and rescue teams.

[0898] 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.

[0899] In this invention, the server includes means for collecting and analyzing meteorological information and environmental monitoring data in real time, means for predicting future disaster risks based on past disaster information, means for detecting abnormal patterns and identifying signs of disaster, means for automatically issuing warnings based on the identified signs, means for calculating optimal evacuation routes and providing them to users in real time, means for supporting information exchange between the disaster-stricken area and rescue teams, means for providing optimal evacuation routes using current location information from the terminal, means for analyzing collected data with an AI algorithm and detecting abnormal patterns, and means for analyzing past data using a generative AI model to predict disaster risks.This enables early detection of disaster risks in real time, rapid issuance of warnings, provision of efficient evacuation routes, optimal resource allocation, and smooth communication between the disaster-stricken area and rescue teams.

[0900] "Weather information" refers to data such as temperature, humidity, wind speed, and precipitation obtained from meteorological satellites and ground observation stations.

[0901] "Environmental monitoring data" refers to data used to monitor environmental changes obtained from sensors such as seismometers and river water level gauges.

[0902] "Past disaster information" refers to data on natural disasters that have occurred in the past, including details of damage, weather patterns, and relief efforts.

[0903] "Disaster risk" indicates the possibility of a disaster occurring in a particular area in the future and the magnitude of its impact.

[0904] "Abnormal patterns" refer to fluctuations in data that differ from normal weather data or sensor data and are thought to be a sign of a disaster.

[0905] A "warning" is information that notifies that a disaster is expected to occur based on a detected abnormal pattern.

[0906] An "evacuation route" refers to a route for safe evacuation in the event of a disaster, and is calculated based on real-time traffic and environmental conditions.

[0907] "User" refers to an individual or organization that uses the disaster response system to receive data and carry out evacuation and countermeasures.

[0908] "Relief supplies" refers to food, water, medicine and other basic necessities provided to disaster victims in the event of a disaster.

[0909] "Medical resources" is a general term for medical equipment, medicines, medical staff, etc. needed for disaster response.

[0910] A "generative AI model" is a type of machine learning algorithm that learns from past disaster data and predicts future disaster risks.

[0911] A "terminal" is a device that a user uses to receive disaster information and evacuation routes in real time, such as a smartphone or PC.

[0912] The present invention is a disaster prevention system that utilizes AI, and its configuration and implementation method will be described in detail below.

[0913] Real-time collection and analysis of meteorological and environmental monitoring data

[0914] server

[0915] The server periodically retrieves weather information from weather satellites and ground observation stations via an API, using scripts written in Python and a database management system (e.g., PostgreSQL).

[0916] The server also receives environmental monitoring data from seismometers and river level gauges, which are collected using IoT platforms such as AWS IoT.

[0917] The collected data is stored in a database in real time and analyzed using AI algorithms (e.g., TensorFlow).

[0918] Specific examples

[0919] For example, during periods of heavy rain, the server can collect data from water level sensors and detect sudden rises in water levels. By analyzing this data, flood risks can be detected early and appropriate warnings can be issued.

[0920] Analysis of past disaster information and prediction of future disaster risks

[0921] server

[0922] The server collects past disaster data and inputs it into a generative AI model (e.g., GPT-4) for analysis.

[0923] The AI ​​model predicts future disaster risks based on data such as weather patterns, damage, and relief efforts.

[0924] The prediction results are stored in a database and notified to relevant agencies.

[0925] Specific examples

[0926] The server inputs past hurricane data into a generative AI model for analysis, and then predicts the hurricane's path and the extent of the damage it will cause based on current weather data. This information is then sent to relevant organizations to help with advance preparations.

[0927] Detecting abnormal patterns and raising alerts

[0928] server

[0929] The server analyzes weather and environmental monitoring data using AI algorithms to detect abnormal patterns.

[0930] If an anomaly is detected, an alert is generated and notified to the specified device, using Firebase Cloud Messaging and Twilio APIs.

[0931] Specific examples

[0932] A server that monitors data from the seismometer detects abnormal tremors that could signal a major earthquake and immediately sends a warning to the smartphones of local residents.

[0933] Providing optimal evacuation routes

[0934] Terminal

[0935] The user's smartphone uses GPS to send current location information to the server.

[0936] The server takes into account traffic conditions and road closures to calculate the best evacuation route, using the Google Maps API and OpenStreetMap API.

[0937] The terminal provides the user with the evacuation route information received from the server, displays a map, and performs real-time navigation.

[0938] Specific examples

[0939] When a fire occurs, the user launches the app on their smartphone, and the server calculates the optimal evacuation route, taking into account traffic congestion information and closed roads, and provides it to the user in real time.

[0940] Damage prediction and resource allocation optimization

[0941] server

[0942] The server analyzes the collected data and past disaster data to predict the extent of damage and the impact on each region.

[0943] Based on the forecast data, the amount of relief supplies and medical resources needed is calculated and an efficient distribution plan is developed.

[0944] The allocation plan will be communicated to relevant agencies to assist in rapid response.

[0945] Specific examples

[0946] When a major typhoon is approaching, the server creates a list of predicted affected areas and needed relief supplies, and notifies relevant agencies to help them make prompt preparations.

[0947] Strengthening information exchange between affected areas and relief teams

[0948] User

[0949] Users use a smartphone app to input information about the damage situation and requests for help in natural language.

[0950] The server analyzes the input information, translates it appropriately, and notifies the relevant authorities. Generative AI models (e.g., GPT-4) are used for natural language processing.

[0951] Specific examples

[0952] When a victim types in Japanese, "Help me, my house has collapsed," the server translates this into English and quickly notifies rescue teams.

[0953] Prompt Sentence Examples

[0954] "Based on past hurricane data, analyze current weather data and predict future hurricane risk."

[0955] "Please provide sample code for an algorithm that analyzes collected sensor information and meteorological data to detect flood risk early."

[0956] "Please show us how to translate disaster information entered in Japanese into English and notify the relief team."

[0957] In this way, the present invention is a system that uses multi-layered technologies to enable real-time disaster countermeasures and minimize damage. Implementing this system is expected to significantly improve disaster preparedness and response, saving many lives and property.

[0958] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0959] Step 1: Data collection

[0960] server

[0961] The server periodically retrieves weather data from weather satellites and ground observation stations using APIs. It also receives environmental monitoring data such as seismometers and river water level gauges. This process uses scripts written in Python and a database management system (e.g., PostgreSQL). The input is raw data retrieved from the API, and the output is data stored in a database in an organized format.

[0962] Specific actions

[0963] The server receives data from weather satellites every 30 minutes and inserts it into a database in JSON format. It also receives data from seismometers and analyzes it in real time.

[0964] Step 2: Data storage and processing

[0965] server

[0966] The collected meteorological and environmental monitoring data is stored in a database in real time. This data is then immediately analyzed by AI algorithms using Python (e.g., TensorFlow). The input is the raw data points, and the output is the analyzed indicators and abnormal pattern detection results.

[0967] Specific actions

[0968] The server periodically reads the weather and sensor data stored in the database and analyzes it using a TensorFlow model. Any abnormal patterns detected are stored in a separate table and sent to the warning system.

[0969] Step 3: Detect and alert on abnormal patterns

[0970] server

[0971] The server uses AI algorithms to detect abnormal patterns, which involves recognizing different behavior compared to normal weather patterns. The input is the pre-processed data points, and the output is the identification of abnormal patterns and a corresponding warning message.

[0972] Specific actions

[0973] If an abnormally large tremor is detected in the seismograph data, the information is analyzed and a warning message is quickly generated and sent to relevant organizations and users' terminals.

[0974] Step 4: Optimize evacuation routes

[0975] Terminal

[0976] The user's smartphone uses GPS to obtain their current location and sends that information to a server. The server then uses real-time traffic and environmental data to calculate the optimal evacuation route. The input is the GPS location and current traffic and environmental data, and the output is the optimized route information.

[0977] Specific actions

[0978] In the event of a fire, when a user launches the evacuation app, the device sends its current location information to the server, which then calculates the optimal evacuation route, taking into account real-time traffic congestion information and road closures, and displays it on the device.

[0979] Step 5: Damage prediction and resource allocation

[0980] server

[0981] The server analyzes the collected data and past disaster data to predict the extent of damage and the impact on each region. It also calculates the required quantities of relief supplies and medical resources and creates an efficient distribution plan. The input is an integrated disaster dataset, and the output is the predicted extent of damage and a resource allocation plan.

[0982] Specific actions

[0983] If a major typhoon is approaching, the server analyzes multiple data points and runs a predictive model to create a list of affected areas and needed supplies, which is then immediately communicated to the appropriate agencies.

[0984] Step 6: Strengthen communication

[0985] User

[0986] Users can use a smartphone app to input information about the disaster situation and requests for assistance in natural language. The server analyzes this information, translates it appropriately, and notifies the relevant authorities. The input is text or voice data entered in natural language, and the output is translated text information.

[0987] Specific actions

[0988] When a victim voices a request for help in Japanese, the server translates it into English and quickly notifies rescue teams from overseas.

[0989] Step 7: Dispatch drones and robots and gather information

[0990] server

[0991] The server dispatches AI-equipped unmanned aerial vehicles and autonomous mobile devices to disaster-stricken areas to collect video and sensor information in real time. The collected data is analyzed by the server and provided to relevant organizations. The input is the video and sensor information collected in real time, and the output is analyzed information and specific instructions.

[0992] Specific actions

[0993] After an earthquake occurs, the server dispatches a drone to the site to collect aerial video data, which is then analyzed immediately and provided to rescue teams as information to grasp the situation on the ground.

[0994] Through these processing steps, this system aims to enable real-time disaster response and minimize damage, significantly improving disaster response preparation and execution, and saving many lives and property.

[0995] (Application example 1)

[0996] 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."

[0997] Recently, the frequency and intensity of natural disasters have increased, necessitating rapid response and evacuation. However, current disaster response systems lack the ability to collect and analyze information in real time, provide optimal evacuation routes, detect abnormal patterns early, and effectively share information between affected areas and rescue teams. Furthermore, language barriers mean that requests for help and the transmission of information take time. This makes it difficult to respond quickly and accurately when a disaster occurs, and poses the challenge of minimizing damage to human life and property.

[0998] 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.

[0999] In this invention, the server includes means for collecting and analyzing meteorological data and sensor data in real time, means for predicting future disaster risks based on past disaster data, means for detecting abnormal patterns and identifying signs of disaster, means for automatically issuing warnings based on the identified signs, means for calculating optimal evacuation routes and providing them to users in real time, means for supporting information exchange between disaster-stricken areas and rescue teams and performing automatic translation, and means for updating and providing evacuation route information in real time. This enables early collection and analysis of information, provision of optimal evacuation routes, early detection of abnormal patterns, and efficient information sharing, thereby realizing rapid and accurate measures in the event of a disaster.

[1000] "Weather data" refers to data related to weather collected from meteorological observation equipment, satellites, etc.

[1001] "Sensor data" refers to data about the environment and situation collected from various sensors (seismic sensors, water level gauges, temperature sensors, etc.).

[1002] "Real-time" means that collection and processing occur immediately, without any time delay.

[1003] "Past disaster data" refers to records and related information relating to disasters that have occurred in the past.

[1004] "Disaster risk" refers to the scale and impact of potential future disasters.

[1005] An "abnormal pattern" refers to a specific data fluctuation or behavior that is different from the norm.

[1006] "Disaster precursors" are signs or indicators that indicate a high probability of a disaster occurring.

[1007] A "warning" is a notification or alert to notify you of danger or risk.

[1008] An "evacuation route" is a route used to evacuate to a safe place in the event of a disaster.

[1009] "Information exchange" is the act of sharing information among multiple parties.

[1010] "Translation" refers to the conversion of written or spoken content in one language into another language.

[1011] An "unmanned aerial vehicle" is an aircraft that can be remotely controlled or fly autonomously, such as a drone.

[1012] "Affected area" means the area or area affected by a disaster.

[1013] "Resources" refers to the supplies, personnel, and equipment needed for relief operations and recovery.

[1014] "Automatic" means that a machine or system operates independently, without human intervention.

[1015] The present invention is a disaster recovery system, and is implemented by the following method and configuration.

[1016] 1. Real-time collection and analysis of meteorological and sensor data

[1017] The server collects weather data from meteorological observation equipment and satellites, and sensor data from various sensors (such as seismometers, water level gauges, and temperature sensors). This data is stored in a database in real time and analyzed by AI algorithms (such as TensorFlow and PyTorch). This makes it possible to detect unusual patterns that are out of the ordinary and identify signs of disaster.

[1018] Specific examples

[1019] The server detects ongoing heavy rainfall and confirms that water levels are rising rapidly from data from water level sensors, allowing for early detection of potential floods and response.

[1020] 2. Analysis of past disaster data and prediction of disaster risks

[1021] The server collects past disaster data, inputs it into an AI model, and analyzes it. Based on the analysis results, future disaster risks can be predicted. Data such as weather patterns, damage, and relief efforts are used to assess the risk in a specific area.

[1022] Specific examples

[1023] When predicting the path of a hurricane, the server analyzes current weather data based on past hurricane data to predict the possible extent and impact of damage, allowing necessary preparations to be made in advance.

[1024] 3. Detecting abnormal patterns and issuing warnings

[1025] The server analyzes the collected data in real time to detect abnormal patterns, and automatically issues warnings based on the identified signs. These warnings are sent as push notifications to users' smartphones or computers, and simultaneously to relevant authorities.

[1026] Specific examples

[1027] Servers monitoring data from local seismometers detect the signs of an earthquake that is building up in size and send warnings to local residents' smartphones.

[1028] 4. Calculating and providing optimal evacuation routes

[1029] The device (user's smartphone) uses GPS to send its current location information to the server. The server analyzes traffic conditions, road closure information, and real-time environmental conditions to calculate the optimal evacuation route. The calculation results are provided to the user's smartphone in real time.

[1030] Specific examples

[1031] In the event of a fire, users can launch the app on their smartphones and get real-time information on the optimal evacuation route. If the route is blocked, the server will calculate and provide a new route.

[1032] 5. Supporting information exchange between disaster-stricken areas and relief teams

[1033] Users can use a smartphone app to voice-input information about the damage situation and requests for assistance. The server analyzes the input using voice recognition (Google Speech-to-Text), translates it appropriately (Google Translate API), and automatically sends it to the relevant organizations. This allows information to be shared quickly, even between different languages.

[1034] Specific examples

[1035] When a victim types "rescue needed" in Japanese into a smartphone app, the server translates the message and transmits it in English to overseas rescue teams.

[1036] 6. Control and intelligence gathering of AI-equipped unmanned aerial vehicles and robots

[1037] The server dispatches AI-equipped unmanned aerial vehicles and robots to disaster-stricken areas and collects video and sensor information in real time. The collected data is analyzed by the server and provided to relevant organizations. The collected data is also used to create optimal resource allocation plans, streamlining rescue efforts.

[1038] Specific examples

[1039] After an earthquake occurs, the server uses unmanned aerial vehicles to collect detailed images of the affected area and transmits the analysis results to rescue teams in real time, enabling them to make quick decisions and take action.

[1040] Prompt Sentence Examples

[1041] "Is there an increased risk of flooding in this location? Please advise the best evacuation route. Also, please send a request for help in English."

[1042] Combining these functions will enable adequate preparation and rapid response to disasters, making it possible to protect many lives and property.

[1043] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1044] Step 1:

[1045] The server collects weather data from meteorological observation equipment and satellites, and sensor data from various sensors (seismic sensors, water level gauges, temperature sensors, etc.). The data is sent to the server in real time via API and stored in a database. The input is various weather data and sensor data, and the output is the stored data. Specifically, the data collection script periodically sends requests to the API endpoint and stores the obtained data in the database.

[1046] Step 2:

[1047] The server analyzes the collected data in real time to detect abnormal patterns. The input is weather data and sensor data stored in a database, and the output is the results of detecting abnormal patterns. AI algorithms (such as TensorFlow and PyTorch) are used to process the data and analyze data points that fall outside of normal ranges. Specifically, the data is input into an AI model, and a function is executed to detect anomalies.

[1048] Step 3:

[1049] The server collects past disaster data, inputs it into an AI model, and analyzes it. The input is historical disaster data, and the output is disaster risk prediction results. Data processing involves comparing past disaster data with current data and applying a model that predicts future risks. Specifically, past and real-time data are input into the AI ​​model, and a risk prediction function is executed.

[1050] Step 4:

[1051] The server automatically issues an alert based on the detected anomaly pattern. The input is the anomaly pattern detection result, and the output is an alert notification. Data processing involves triggering the anomaly detection result to send an alert message to a push notification service (such as Firebase). Specifically, the alert generation function receives the anomaly detection event and sends an appropriate message to the notification service.

[1052] Step 5:

[1053] The device uses GPS to send current location information to the server. The input is the user's current location information, and the output is the location information sent to the server. Specifically, the smartphone app obtains the GPS data and sends it to the server via an HTTP request.

[1054] Step 6:

[1055] The server analyzes traffic conditions, road closure information, and real-time environmental conditions to calculate the optimal evacuation route. The input is current location information and traffic / road information, and the output is the calculated evacuation route. Data processing involves applying an algorithm that takes into account information such as road closures and traffic delays to find the shortest and most optimal route. Specifically, the route calculation function is executed, and the generated route is sent to the user's device.

[1056] Step 7:

[1057] The server predicts the extent of damage and resource allocation based on the collected data. The input is real-time data and past disaster data, and the output is a resource allocation plan. Data processing involves using a damage prediction model to generate prediction results and calculate the amount of relief resources required. In concrete terms, the prediction function processes the data, and the results are linked to the resource management system.

[1058] Step 8:

[1059] The server analyzes the user's voice input, performs automatic translation, and sends the information to the relevant organizations. The input is the user's voice data, and the output is translated text. Data processing involves converting the voice into text using voice recognition (Google Speech-to-Text), and then translating it into another language using a translation system (Google Translate API). Specifically, the voice recognition function processes the voice data, and the translation function converts the text into another language and sends it to the relevant organizations.

[1060] Step 9:

[1061] The server controls AI-equipped unmanned aerial vehicles and robots and collects information on disaster sites in real time. The input is sensor data and video data from the unmanned aerial vehicles and robots, and the output is analyzed information. Data processing involves analyzing the collected data and extracting important information. Specifically, the control program operates the unmanned aerial vehicles and robots and sends the collected data to the analysis server.

[1062] These processing steps enable a quick and accurate response in the event of a disaster, ensuring the safety of users.

[1063] 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.

[1064] This invention is a disaster prevention system that utilizes AI, and combines mainly real-time collection and analysis of meteorological data and sensor data, analysis of past disaster data, detection and warning of abnormal patterns, optimization of evacuation routes, damage prediction and resource allocation, enhanced communication, utilization of AI-equipped drones and robots, and an emotion engine that recognizes user emotions. The specific operation of each function in the system of this invention will be explained.

[1065] Real-time collection and analysis of weather and sensor data

[1066] server

[1067] The server collects weather data from weather satellites and ground observation stations, and collects sensor data in real time from seismometers, river water level gauges, and other devices.

[1068] The collected data is stored in a database and analyzed using AI algorithms.

[1069] The server analyzes weather and sensor data to detect signs of disaster.

[1070] example

[1071] For example, during periods of heavy rain, the server collects data from various sensors and detects sudden rises in water levels. By analyzing this data, it is possible to identify potential floods early and take appropriate action.

[1072] Detecting abnormal patterns and issuing alerts

[1073] server

[1074] The server analyzes the collected weather and sensor data to detect any abnormal patterns.

[1075] If the identified abnormal pattern is judged to be a sign of a disaster, a warning message will be generated and sent to designated terminals and relevant organizations.

[1076] example

[1077] Servers monitoring seismometer data detect the signs of an earthquake that is building up in size and send warnings to local residents' smartphones.

[1078] Optimizing evacuation routes

[1079] Terminal

[1080] The device (user's smartphone) uses its GPS function to obtain current location information and periodically sends it to the server.

[1081] server

[1082] Based on the received current location information, the server analyzes real-time traffic and road information and calculates the optimal evacuation route.

[1083] Terminal

[1084] Information on the optimal evacuation route is sent to the user's device and displayed on the device's app.

[1085] example

[1086] In the event of a fire, users can launch the app on their smartphones and get real-time information on the optimal evacuation route. If the route is blocked, the server will calculate and provide a new route.

[1087] Damage prediction and resource allocation optimization

[1088] server

[1089] The server inputs past disaster data and current weather data into an AI model for analysis.

[1090] The AI ​​model will generate a damage scope and resource allocation plan and provide it to relevant agencies.

[1091] example

[1092] When a major typhoon is approaching, the server creates a list of predicted affected areas and needed relief supplies, and automatically sends it to the relevant organizations, allowing for faster preparations.

[1093] Strengthening communication

[1094] User

[1095] Users can use a smartphone app to input disaster situations and requests for help.

[1096] The entered information undergoes natural language processing and machine translation on the server and is then sent to the relevant organizations.

[1097] example

[1098] If a victim requests help from a rescue team in Japanese, they can input their voice into the app, and the server will translate the message into English and quickly transmit the information to rescue teams dispatched from overseas.

[1099] Utilizing AI-equipped drones and robots

[1100] server

[1101] The server controls AI-equipped drones and robots and collects information from disaster sites in real time.

[1102] The collected data is analyzed on a server and provided to relevant organizations.

[1103] example

[1104] After an earthquake occurs, the server uses drones to collect detailed images of the affected area and transmits the analysis results to rescue teams in real time, enabling them to make quick decisions and take action.

[1105] Introducing the Emotion Engine

[1106] Terminal

[1107] An emotion engine is built into the user's device (smartphone) and recognizes emotions from the user's voice and text in real time.

[1108] server

[1109] The emotion engine assesses the user's mental state and automatically contacts experts if necessary.

[1110] The content of evacuation instructions and warnings is optimized based on user emotional data.

[1111] example

[1112] If the emotion engine detects that the user is panicking during a disaster, the server notifies a professional counselor and sends a calming message to the user, encouraging them to take action to ensure their safety.

[1113] As described above, this invention is a system that combines various technologies to realize real-time disaster countermeasures and minimize damage. Implementing this system is expected to significantly improve disaster preparedness and response, protecting many lives and property.

[1114] The processing flow will be explained below.

[1115] Real-time collection and analysis of weather and sensor data

[1116] server

[1117] Step 1:

[1118] The server collects weather data from weather satellites and ground observation stations.

[1119] Data collected includes temperature, precipitation, wind speed, and wind direction.

[1120] Step 2:

[1121] The server collects data in real time from sensors such as seismometers and river water level gauges.

[1122] The data collected includes earthquake intensity and water level fluctuations.

[1123] Step 3:

[1124] The server stores the collected weather and sensor data in a database.

[1125] The database is designed with a structure that allows fast access for analysis.

[1126] Step 4:

[1127] The server inputs the stored data into an AI algorithm and begins analysis.

[1128] AI algorithms use machine learning models to detect anomalous patterns.

[1129] Detecting abnormal patterns and issuing alerts

[1130] server

[1131] Step 1:

[1132] The server analyzes weather and sensor data to detect unusual patterns.

[1133] Unusual patterns include sudden temperature changes, unusual seismic activity, and sudden rises in river levels.

[1134] Step 2:

[1135] AI algorithms determine whether unusual patterns are a sign of disaster.

[1136] The criteria for judgment are determined by comparing with past data and whether or not it exceeds a certain threshold.

[1137] Step 3:

[1138] The server generates a warning message if it determines that a disaster is imminent.

[1139] The warning message includes information such as the type of disaster, predicted damage, and the need for evacuation.

[1140] Step 4:

[1141] The server sends the generated warning message to designated devices (smartphones, PCs) and relevant organizations.

[1142] Optimizing evacuation routes

[1143] Terminal

[1144] Step 1:

[1145] The user's device acquires current location information using the GPS function.

[1146] The acquired location information is sent to the server at regular intervals.

[1147] server

[1148] Step 2:

[1149] The server analyzes real-time traffic and road information based on the received current location information.

[1150] Analysis includes current traffic conditions, road closure information, and disaster situations.

[1151] Step 3:

[1152] The server uses AI algorithms to calculate the optimal evacuation route.

[1153] The calculation results show the safest evacuation route in the shortest time.

[1154] Terminal

[1155] Step 4:

[1156] The optimal evacuation route information received from the server is displayed on the user's terminal.

[1157] Evacuation route information can be visually confirmed in map format.

[1158] Step 5:

[1159] If a road is blocked during an evacuation, the server recalculates a new route and sends updates to the device.

[1160] Damage prediction and resource allocation optimization

[1161] server

[1162] Step 1:

[1163] The server collects past disaster data and current weather data and inputs it into the AI ​​model.

[1164] The data includes records of damage caused by past disasters, weather conditions, and resource allocation.

[1165] Step 2:

[1166] Based on the collected data, the AI ​​model identifies the extent of damage that can be predicted in the future based on the current situation.

[1167] The prediction includes the probability of occurrence and the extent of damage.

[1168] Step 3:

[1169] The server generates a resource allocation plan based on the prediction results.

[1170] The plan includes necessary relief supplies, medical resources, response times, etc.

[1171] Step 4:

[1172] The resource allocation plan will be sent to the relevant agencies to support a rapid response.

[1173] Strengthening communication

[1174] User

[1175] Step 1:

[1176] Users use a smartphone app to input information about the disaster situation and requests for help.

[1177] Input methods include voice input and text input.

[1178] server

[1179] Step 2:

[1180] The server analyzes the input information using a natural language processing algorithm and extracts the necessary information.

[1181] If information is entered in multiple languages, it is automatically machine translated.

[1182] Step 3:

[1183] The extracted and translated information is automatically sent to relevant agencies and relief teams.

[1184] Utilizing AI-equipped drones and robots

[1185] server

[1186] Step 1:

[1187] The server controls AI-equipped drones and robots and dispatches them to disaster sites.

[1188] Drones and robots operate according to set flight and movement paths.

[1189] Step 2:

[1190] Drones and robots collect footage and sensor data from disaster sites in real time.

[1191] The collected data is immediately sent to the server.

[1192] Step 3:

[1193] The server analyzes the received data and grasps the situation on site.

[1194] If necessary, the operation of drones and robots will be adjusted to gather more information.

[1195] Step 4:

[1196] The analysis results will be provided to relevant agencies and rescue teams to assist in rapid response.

[1197] Introducing the Emotion Engine

[1198] Terminal

[1199] Step 1:

[1200] The emotion engine built into the user's device analyzes the user's voice and text in real time and recognizes emotions.

[1201] server

[1202] Step 2:

[1203] The server receives the emotion data sent from the emotion engine and evaluates the mental state of the user.

[1204] If the user's emotions are judged to be dangerous, an appropriate alert or support message will be generated.

[1205] Step 3:

[1206] If necessary, emotional data will be used to automatically connect you with a specialist (medical professional or counselor).

[1207] The expert is informed of the user's current emotional state and situation.

[1208] Step 4:

[1209] The server analyzes the user's emotional data and optimizes the evacuation instructions and warnings to suit the user's psychological state.

[1210] Optimized warnings and instructions are sent to the user's device.

[1211] example

[1212] In the event of a disaster, if the system detects a panicked state in the user's voice, the emotion engine will display a message to alleviate the user's anxiety. It will also automatically notify a professional counselor based on the user's situation and provide necessary support.

[1213] Based on the above steps, the system of the present invention can realize quick and effective countermeasures against natural disasters and minimize damage, which is expected to save many lives and properties.

[1214] Example 2

[1215] 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."

[1216] In recent years, natural disasters have become more frequent, and rapid and accurate responses are required to minimize the damage they cause. However, existing systems have limitations in real-time data collection and analysis, evacuation route optimization, and user emotion recognition. Furthermore, there is a lack of information collection and damage prediction using autonomous devices equipped with AI. This can lead to delayed responses in the event of a disaster, potentially exacerbating the damage.

[1217] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting and analyzing meteorological data and sensor data in real time, means for predicting future disaster risks based on past disaster data, means for detecting abnormal patterns and identifying precursors of disasters, means for automatically issuing warnings based on the identified precursors, means for calculating optimal evacuation routes and providing them to the user in real time, means for supporting information exchange between the disaster-stricken area and rescue teams, means for acquiring user location information and calculating optimal evacuation routes based on real-time traffic data, and means for recognizing the user's emotions and providing appropriate instructions and warnings. This enables disaster countermeasures to be taken in real time, minimizing damage.

[1218] "Weather data" refers to information about atmospheric conditions, including temperature, humidity, wind speed, air pressure, and precipitation.

[1219] "Sensor data" refers to various types of measurement information obtained by sensors such as seismometers and river water level gauges, and includes data indicating environmental and physical conditions.

[1220] "Real-time" refers to data and information being collected, processed, and distributed almost instantly.

[1221] "Analysis" refers to analyzing collected data and extracting meaning and patterns from it.

[1222] "AI algorithms" refer to calculation methods and mathematical models that utilize artificial intelligence technology and are used to predict and analyze data.

[1223] "Past disaster data" refers to recorded information about disasters that have occurred in the past, and includes data such as the extent of damage, causes of occurrence, and effectiveness of countermeasures.

[1224] "Disaster risk" is an indicator that shows the possibility of a disaster occurring and the extent of its impact.

[1225] An "abnormal pattern" refers to a situation in which data movements or trends that differ from normal are observed.

[1226] "Disaster precursors" refer to abnormal changes in data or signs of actual natural phenomena that occur before a disaster occurs.

[1227] "Warning" refers to a message that alerts you to an abnormal situation or potential danger.

[1228] An "evacuation route" refers to the route that should be taken to evacuate to a safe place in the event of a disaster.

[1229] "Information exchange" refers to the exchange of data or messages between two or more parties.

[1230] A "rescue team" refers to a specialized unit formed to rescue and provide support to victims in the event of a disaster.

[1231] "Location information" refers to latitude and longitude information for a specific location or region.

[1232] "Traffic data" refers to information about current road conditions and traffic flow.

[1233] "User's emotions" refers to information that represents the emotions and mental state that the user is experiencing.

[1234] "Autonomous devices" refer to devices such as drones and robots that are equipped with AI and machine learning technology and operate automatically.

[1235] "Resource allocation" refers to the efficient distribution of necessary materials and personnel.

[1236] This invention is a disaster prevention system that utilizes AI, and combines mainly real-time collection and analysis of meteorological data and sensor data, analysis of past disaster data, detection and warning of abnormal patterns, optimization of evacuation routes, damage prediction and resource allocation, enhanced communication, utilization of autonomous devices equipped with AI, and an emotion engine that recognizes user emotions. Each function of this invention is explained in detail below.

[1237] Real-time collection and analysis of weather and sensor data

[1238] server

[1239] The server retrieves weather data provided by weather satellites and ground observation stations via an API. It also collects real-time data from various sensors, such as seismometers and river water level gauges. The API is implemented using Python to manage data requests and responses.

[1240] The collected data is stored in a PostgreSQL database and analyzed using an AI algorithm powered by TensorFlow, which allows for the detection of abnormal patterns and signs of disasters from large amounts of data.

[1241] Specific examples

[1242] For example, to detect the risk of flooding due to heavy rain at an early stage, the server analyzes meteorological data and water level gauge data to detect abnormally high water levels, and sends a warning to the relevant authorities.

[1243] Prompt Sentence Examples

[1244] "If the water level sensor detects an abnormality during heavy rainfall, please provide the analysis procedures for taking appropriate action."

[1245] Detecting abnormal patterns and issuing alerts

[1246] server

[1247] The server uses PySpark to perform distributed processing of collected weather and sensor data to detect abnormal patterns. If the detected abnormal pattern is judged to be a sign of a disaster, a warning message is generated on the spot.

[1248] The generated warning message is sent to designated user devices and relevant organizations using the Twilio API.

[1249] Specific examples

[1250] If an abnormal vibration pattern is detected from the seismometer data, the server analyzes it as a precursor to an earthquake and sends a warning message to the smartphones of local residents.

[1251] Prompt Sentence Examples

[1252] "If an anomaly is detected in the seismometer data, please explain the procedure for generating a warning message and sending it to the relevant authorities."

[1253] Optimizing evacuation routes

[1254] Terminal

[1255] The user's smartphone acquires the current location information using the GPS function and periodically sends it to the server, using HTTP requests and WebSockets for communication.

[1256] server

[1257] The server obtains real-time traffic data using the Google Maps API and Waze API, and calculates the optimal evacuation route using the Dijkstra algorithm and the A algorithm.

[1258] The calculated optimal evacuation route is sent to the user's smartphone app and displayed on the screen.

[1259] Specific examples

[1260] In the event of a fire, users can receive real-time evacuation route information via a smartphone app, and if a road is closed, the server will calculate and provide a new route.

[1261] Prompt Sentence Examples

[1262] "Explain the steps to use GPS data and real-time traffic information to calculate the optimal route to optimize evacuation routes."

[1263] Damage prediction and resource allocation optimization

[1264] server

[1265] The server collects past disaster data and current weather data and uses OpenAI's generative AI model to predict damage.

[1266] Based on the analysis results of the AI ​​model, a distribution plan for necessary relief supplies and medical resources is generated.

[1267] Specific examples

[1268] When a major typhoon is approaching, the server creates a list of areas expected to be affected and necessary supplies, and automatically notifies relevant organizations, enabling effective preparations.

[1269] Prompt Sentence Examples

[1270] "When a large typhoon is approaching, please explain the procedure for generating damage forecasts and lists of relief supplies and notifying relevant organizations."

[1271] Strengthening communication

[1272] User

[1273] Users can use a smartphone app to input information about the situation in the affected area and requests for help. Information can be entered using both voice and text.

[1274] server

[1275] The server uses the Google Cloud Natural Language API to parse the user's input and perform machine translation if necessary, then sends the translated information to the appropriate authorities.

[1276] Specific examples

[1277] When a victim requests help in Japanese, they can input their voice and the server will automatically translate it into English and transmit it to a rescue team dispatched from overseas.

[1278] Prompt Sentence Examples

[1279] "Please explain the procedure for analyzing the voice input of the victim using natural language processing and machine translation and conveying it appropriately to the rescue team."

[1280] Utilizing AI-powered autonomous machines

[1281] server

[1282] The server will control autonomous devices (e.g., drones and robots) equipped with AI technology to collect detailed information on disaster sites, including the use of autonomous devices equipped with NVIDIA Jetson.

[1283] The collected data is sent to a server in real time, and the analysis results are provided to relevant organizations.

[1284] Specific examples

[1285] After an earthquake occurs, the server uses drones to collect footage of the affected area and transmits the analysis results to rescue teams in real time.

[1286] Prompt Sentence Examples

[1287] "Please explain the procedure for using drones to collect information from disaster sites and analyze it in real time."

[1288] User Emotion Recognition

[1289] Terminal

[1290] The user's smartphone is equipped with an emotion engine that uses Amazon Rekognition, which recognizes emotions from the user's voice and text.

[1291] server

[1292] The server uses an emotion engine to assess the user's mental state and notify experts as necessary. It also optimizes evacuation instructions and warnings based on emotion data.

[1293] Specific examples

[1294] During a disaster, if the emotion engine detects that the user is in a panic, the server notifies a counselor and sends a calming message to the user.

[1295] Prompt Sentence Examples

[1296] "Please explain the procedure for analyzing user emotion data and providing appropriate responses during a disaster."

[1297] As described above, this invention is a system that combines various technologies to realize real-time disaster countermeasures and minimize damage. Implementing this system is expected to significantly improve disaster preparedness and response, protecting many lives and property.

[1298] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1299] Real-time collection and analysis of weather and sensor data

[1300] Step 1: Collect data

[1301] server

[1302] Input: Weather data from weather satellites and observation stations, sensor data such as seismometers and river level gauges.

[1303] Specific operation: The server uses a Python script to periodically execute API requests to obtain weather data and various sensor data.

[1304] Output: The retrieved data.

[1305] Step 2: Store the data in the database

[1306] server

[1307] Input: Collected weather and sensor data.

[1308] Specific operation: The server connects to a PostgreSQL database and stores data in chronological order.

[1309] Output: Database entries organized chronologically.

[1310] Step 3: Analyze the data

[1311] server

[1312] Input: Weather and sensor data stored in a database.

[1313] How it works: The server uses TensorFlow to analyze the collected data and apply machine learning models to detect anomalous patterns.

[1314] Output: Analysis results (detection of abnormal patterns).

[1315] Detecting abnormal patterns and issuing alerts

[1316] Step 4: Detecting abnormal patterns

[1317] server

[1318] Input: Analysis results (detection of abnormal patterns).

[1319] What it does: The server verifies the analysis results and flags any abnormal patterns that could be a sign of disaster.

[1320] Output: Disaster warning flag.

[1321] Step 5: Generate a warning message

[1322] server

[1323] Input: Disaster omen flag.

[1324] Specific operation: The server uses the warning message template to generate a customized warning message.

[1325] Output: A warning message.

[1326] Step 6: Sending a warning message

[1327] server

[1328] Input: Warning message.

[1329] Specific operation: The server uses the Twilio API to send a warning message to the specified device or relevant organization.

[1330] Output: The warning message sent.

[1331] Optimizing evacuation routes

[1332] Step 7: Obtaining User Location

[1333] Terminal

[1334] Input: GPS data from your smartphone.

[1335] Specific operation: The device periodically obtains its current location information and sends it to the server.

[1336] Output: The location sent.

[1337] Step 8: Acquiring real-time traffic data

[1338] server

[1339] Input: Traffic data from Google Maps API and Waze API.

[1340] Specific operation: The server executes an API request to obtain the latest traffic data and road information.

[1341] Output: Latest traffic data.

[1342] Step 9: Calculate evacuation routes

[1343] server

[1344] Input: User location and real-time traffic data.

[1345] Specific operation: Based on the acquired data, the optimal evacuation route is calculated using the Dijkstra algorithm or the A algorithm.

[1346] Output: Optimal evacuation route.

[1347] Step 10: Send and display route information

[1348] Terminal

[1349] Input: The optimal evacuation route sent by the server.

[1350] Specific operation: The device displays the received evacuation route information to the user. Specifically, a dedicated app displays the route on a map.

[1351] Output: Evacuation route displayed on the screen.

[1352] Damage prediction and resource allocation optimization

[1353] Step 11: Collect historical disaster data and current weather data

[1354] server

[1355] Input: Historical disaster data and current weather data.

[1356] Specific operation: The server retrieves past disaster data from an existing database and collects current weather data through an API.

[1357] Output: Collected data.

[1358] Step 12: Damage prediction analysis

[1359] server

[1360] Input: Historical disaster data and current weather data.

[1361] Specific operation: The server uses a generative AI model (such as OpenAI) to analyze damage predictions.

[1362] Output: Damage prediction results.

[1363] Step 13: Create a resource allocation plan

[1364] server

[1365] Input: Damage prediction results.

[1366] Specific operation: The server creates a list of needed relief supplies and medical resources and generates a resource allocation plan.

[1367] Output: Resource allocation plan.

[1368] Step 14: Notify appropriate authorities

[1369] server

[1370] Input: Resource Allocation Plan.

[1371] Specific operation: The server sends the resource allocation plan to the relevant institutions.

[1372] Output: The submitted resource allocation plan.

[1373] Strengthening communication

[1374] Step 15: Getting User Input

[1375] User

[1376] Input: Situation in the affected area and request for help (voice or text).

[1377] Specific operation: The user inputs information using a smartphone app.

[1378] Output: Data sent to the server.

[1379] Step 16: Natural Language Processing and Machine Translation

[1380] server

[1381] Input: Data entered by the user.

[1382] What it does: The server uses the Google Cloud Natural Language API to parse the data and perform machine translation if necessary.

[1383] Output: The translated data.

[1384] Step 17: Send to the appropriate authorities

[1385] server

[1386] Input: Translated data.

[1387] Specific operation: The server sends the data to the relevant organization.

[1388] Output: The data sent.

[1389] Utilizing AI-powered autonomous machines

[1390] Step 18: Controlling Autonomous Devices

[1391] server

[1392] Input: Operation instructions.

[1393] Specific operation: The server issues commands to control autonomous devices (drones, robots) equipped with AI technology.

[1394] Output: Control signals to autonomous devices.

[1395] Step 19: Data Collection

[1396] autonomous equipment

[1397] Input: Site condition data.

[1398] Specific operation: The autonomous device collects video and sensor data from the disaster site and transmits it to a server in real time.

[1399] Output: Data sent to the server.

[1400] Step 20: Data Analysis

[1401] server

[1402] Input: Transmitted data from autonomous devices.

[1403] Specific operation: The server analyzes the collected data and evaluates the situation on site in detail.

[1404] Output: Analysis results.

[1405] Step 21: Provide to relevant organizations

[1406] server

[1407] Input: Analysis results.

[1408] Specific operation: The server provides the analysis results to the relevant organizations.

[1409] Output:Notification to relevant authorities.

[1410] User Emotion Recognition

[1411] Step 22: Collect Emotional Data

[1412] Terminal

[1413] Input: User voice or text data.

[1414] What it does: The device uses Amazon Rekognition to collect user emotion data.

[1415] Output: Collected emotion data.

[1416] Step 23: Emotion Recognition

[1417] server

[1418] Input: Emotion data.

[1419] Specific operation: The server analyzes the collected emotional data and evaluates the user's mental state in real time.

[1420] Output: Emotion evaluation results.

[1421] Step 24: Collaborate with experts

[1422] server

[1423] Input: Emotion assessment results.

[1424] Specific behavior: The server uses the Twilio API to notify a professional (such as a counselor) as needed.

[1425] Output:Notification to experts.

[1426] Step 25: Optimizing Instructions and Warnings

[1427] server

[1428] Input: Emotion assessment results.

[1429] Specific operation: The server generates appropriate evacuation instructions and warning messages based on the user's mental state and sends them to the terminal.

[1430] Output: Customized instructions and warning messages to the user.

[1431] (Application example 2)

[1432] 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."

[1433] Conventional disaster response systems require a wide range of functions, including not only real-time data collection and analysis, but also the detection of abnormal patterns, the issuance of warnings, and the calculation of evacuation routes. However, these systems are limited in their functionality during disasters, when a rapid and accurate response is required. Their application to autonomous vehicles is particularly limited, and they often fail to function adequately. Furthermore, it is difficult to provide prompt and optimal evacuation instructions that take into account the situational awareness and emotional state of disaster victims, which is a major obstacle to safe evacuation.

[1434] The identification processing 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 and analyzing weather data and sensor data in real time, means for predicting future disaster risks based on past disaster data, means for detecting abnormal patterns and identifying precursors of disasters, means for automatically issuing warnings based on the identified precursors, means for calculating optimal evacuation routes and providing them to autonomously driven vehicles in real time, means for supporting information exchange between the disaster-stricken area and rescue teams, means for recognizing the user's emotions and promoting safe evacuation behavior, and means for calculating and providing optimal evacuation routes for autonomously driven vehicles in the event of a disaster. This enables autonomously driven vehicles to evacuate quickly and safely in the event of a disaster, and by instructing appropriate evacuation behavior according to the emotional state of disaster victims, it is possible to significantly improve the efficiency and safety of evacuation.

[1435] "Weather data" is a general term for information collected from meteorological satellites and ground observation stations, and includes data on weather, temperature, precipitation, wind speed, etc.

[1436] "Sensor data" is a general term for data collected in real time from seismometers, river water level gauges, and other environmental sensors.

[1437] "Historical disaster data" refers to a database of records and information about disasters that have occurred in the past, including details about the type, scale, and impact of such disasters.

[1438] An "abnormal pattern" refers to data that shows abnormal fluctuations or behavior outside the normal data range, and is detected as a sign of a disaster.

[1439] A "warning" is a warning message sent to users and relevant organizations when a sign of a disaster is detected.

[1440] The "optimal evacuation route" is a route calculated based on real-time data that will allow users to evacuate quickly and safely in the event of a disaster.

[1441] An "autonomous vehicle" is a vehicle that uses AI technology to move and drive autonomously, eliminating the need for a driver.

[1442] "Disaster area" refers to an area that has been affected by a disaster and is in need of assistance and rescue.

[1443] A "rescue team" refers to a specialized organization or group that carries out relief activities when a disaster occurs, including rescue teams and first aid medical teams.

[1444] "Emotion recognition means" refers to technologies and algorithms that analyze a user's voice or text to automatically determine their current emotional state.

[1445] A "drone" refers to an unmanned aircraft that can be remotely controlled and fly autonomously, and is used to collect information at disaster sites.

[1446] "Means to promote evacuation behavior" is a system that takes into account the user's emotional state and surrounding circumstances to instruct and guide the optimal evacuation method and behavior.

[1447] This invention is a disaster prevention system that utilizes AI, and its main functions are as follows:

[1448] Real-time collection and analysis of weather and sensor data

[1449] The server collects meteorological data from weather satellites and ground observation stations, and also acquires real-time sensor data from seismometers and river water level gauges. This data is stored in a database and analyzed by AI algorithms. For example, if heavy rain continues, a sudden rise in river water levels can be detected, allowing for early detection of the possibility of flooding.

[1450] Detecting abnormal patterns and issuing alerts

[1451] The server analyzes the collected weather and sensor data to detect any abnormal patterns. If this abnormal pattern is determined to be a sign of a disaster, a warning message is automatically generated and sent to the user's smartphone and relevant organizations. For example, if seismometer data detects a sign of a large earthquake, this warning will be sent to local residents.

[1452] Calculating and providing optimal evacuation routes

[1453] The device (user's smartphone) uses its GPS function to obtain current location information and sends it to the server. The server analyzes this current location information along with real-time traffic and road information to calculate the optimal evacuation route. This information is sent to the user's device, and the evacuation route is displayed. For example, in the event of a fire, the optimal evacuation route is provided in real time.

[1454] Damage prediction and resource allocation optimization

[1455] The server inputs past disaster data and current weather data into the AI ​​model to generate a damage scope and resource allocation plan, allowing relevant agencies to quickly prepare response measures. For example, if a typhoon is approaching, damage forecasts and a list of needed relief supplies are immediately provided.

[1456] Utilizing AI-enabled drones and robots

[1457] The server will control AI-equipped drones and robots to collect information on disaster sites in real time and provide the analysis results to relevant organizations. For example, after an earthquake, drones can collect detailed images of the affected area, enabling them to quickly provide information to rescue teams.

[1458] Introducing the Emotion Engine

[1459] The user's device is equipped with an emotion engine that recognizes emotions from the user's voice and text in real time. The server evaluates the user's emotional state and responds by contacting experts as necessary. The server also optimizes evacuation instructions and warnings based on the emotion data. For example, if the emotion engine detects that the user is panicking during a disaster, it will notify a counselor and send the user a message to calm them down.

[1460] Specific examples

[1461] For example, when a typhoon is approaching, the system will collect weather and sensor data, and calculate and provide the optimal evacuation route for autonomous vehicles in real time. Furthermore, if the emotion engine detects that the user in the car is in a state of panic, it can also send a reassuring message.

[1462] Prompt Sentence Examples

[1463] "Calculate the evacuation route for your current location and lead you to a safe location."

[1464] "Generate a damage forecast and a list of necessary relief supplies in case of a typhoon."

[1465] "When users are panicking, send them a reassuring message."

[1466] In this way, the AI-based disaster prevention system provides comprehensive support, from collecting and analyzing real-time data to providing optimal evacuation routes and responding based on the user's emotional state.

[1467] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1468] Step 1: Collect weather and sensor data

[1469] The server collects meteorological data from meteorological satellites and ground observation stations, and acquires real-time data from various sensors, such as seismometers and river water level gauges. The input data consists of meteorological information and sensor information, which are then stored in a database.

[1470] Step 2: Analyze data to detect abnormal patterns

[1471] The server analyzes the collected weather and sensor data using AI algorithms. The input is the latest weather and sensor data, and the output is the detection of abnormal patterns. If an abnormality is detected, an analysis is performed to determine whether it is a sign of a disaster.

[1472] Step 3: Automatically generate and issue warning messages

[1473] If the server determines that an abnormal pattern is a sign of a disaster, it automatically generates a warning message and sends it to a designated device (the user's smartphone or a relevant institution). The input is the abnormal pattern detection result, and the output is a warning message.

[1474] Step 4: Get the user's location

[1475] The device (user's smartphone) uses its GPS function to obtain the user's current location information and sends it to the server. The input is GPS data, and the output is the current location information.

[1476] Step 5: Calculate the optimal evacuation route

[1477] The server analyzes real-time traffic and road information based on the received current location information and calculates the optimal evacuation route. The input is current location information, traffic data, and road information, and the output is the optimal evacuation route.

[1478] Step 6: Provide evacuation routes

[1479] The server sends the calculated optimal evacuation route to the user's device and displays the route. The input is the optimal evacuation route information, and the output is the display of the evacuation route.

[1480] Step 7: Damage prediction and resource allocation planning

[1481] The server inputs past disaster data and current weather data into the AI ​​model to generate a damage extent and resource allocation plan. The input is past disaster data and weather data, and the output is a damage forecast and resource allocation plan.

[1482] Step 8: Drone and robot control and intelligence gathering

[1483] The server controls AI-equipped drones and robots and collects information on disaster sites in real time. The input is control instructions for the drones and robots, and the output is the collected on-site information.

[1484] Step 9: Recognizing user emotions with the emotion engine

[1485] The device's emotion engine recognizes emotions from the user's voice and text in real time. The input is the user's voice and text data, and the output is the emotion recognition result.

[1486] Step 10: Responding to a panic attack

[1487] If the server detects the user's emotional state (e.g., panic), it automatically contacts an expert and sends a calming message to the user. The input is the user's emotion recognition result, and the output is a notification to the expert and a message to the user.

[1488] 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.

[1489] 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.

[1490] 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.

[1491] [Third embodiment]

[1492] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1493] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1494] 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).

[1495] 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.

[1496] 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.

[1497] 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).

[1498] 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.

[1499] 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.

[1500] 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.

[1501] 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.

[1502] 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.

[1503] 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."

[1504] This invention is a disaster prevention system that utilizes AI, and its main functions are the real-time collection and analysis of meteorological and sensor data, analysis of past disaster data, detection and warning of abnormal patterns, optimization of evacuation routes, damage prediction and resource allocation, enhanced communication, and utilization of AI-equipped drones and robots. The specific operation of each function in the system of this invention will be explained below.

[1505] Real-time collection and analysis of weather and sensor data

[1506] server

[1507] The server periodically collects weather data from weather satellites and ground observation stations, as well as sensor data from seismometers, river level gauges, and other sources.

[1508] The collected data is stored in a database in real time and is then analyzed using AI algorithms.

[1509] The server can detect signs of disaster by linking weather data with sensor data.

[1510] example

[1511] During periods of heavy rain, the server collects data from various sensors and detects sudden rises in water levels. By analyzing this data, it is possible to identify potential floods early and take appropriate action.

[1512] Analysis of past disaster data and disaster risk prediction

[1513] server

[1514] The server collects data from past disasters, inputs it into an AI model, and performs analysis.

[1515] The AI ​​model predicts future disaster risks based on data such as weather patterns, damage, and relief efforts.

[1516] Based on the forecast data, we identify areas where disasters may occur and the scale of the damage.

[1517] example

[1518] When predicting the path of a hurricane, the server analyzes current weather data based on past hurricane data to predict the possible extent and intensity of damage, which can be useful for making necessary preparations in advance.

[1519] Detecting abnormal patterns and issuing alerts

[1520] server

[1521] The server analyzes normal weather and sensor data to detect any unusual patterns.

[1522] Based on the identified abnormal patterns, an alert is generated if they are deemed to be a sign of a disaster.

[1523] The warning will be sent to a designated device (smartphone, PC) and simultaneously sent to relevant agencies.

[1524] example

[1525] Servers monitoring seismometer data detect the signs of an earthquake that is building up in size and send warnings to local residents' smartphones.

[1526] Optimizing evacuation routes

[1527] Terminal

[1528] The device (user's smartphone) uses GPS to send current location information to the server.

[1529] The server analyzes traffic conditions, road closure information, and real-time environmental conditions to calculate the optimal evacuation route.

[1530] The terminal provides the user with the evacuation route received from the server, assisting in evacuation in the event of an emergency.

[1531] example

[1532] In the event of a fire, users can launch the app on their smartphones and get real-time information on the optimal evacuation route. If the route is blocked, the server will calculate and provide a new route.

[1533] Damage prediction and resource allocation optimization

[1534] server

[1535] The server uses the collected data and past disaster data to predict the extent of damage and the impact on each region.

[1536] Based on the forecast data, we calculate the amount of relief supplies and medical resources needed and create a plan for efficient allocation.

[1537] Resource allocation plans will be shared with relevant agencies, enabling rapid response.

[1538] example

[1539] When a major typhoon is approaching, the server creates a list of predicted affected areas and needed relief supplies, and automatically sends it to the relevant organizations, allowing for faster preparations.

[1540] Strengthening communication

[1541] User

[1542] Users can use a smartphone app to input information about the damage situation and requests for help in natural language.

[1543] The server analyzes the entered information, translates it appropriately, and automatically sends it to the relevant organizations.

[1544] example

[1545] If a victim requests help from a rescue team in Japanese, they can input their voice into the app, and the server will translate the message into English and quickly transmit the information to rescue teams dispatched from overseas.

[1546] Utilizing AI-equipped drones and robots

[1547] server

[1548] The server dispatches AI-equipped drones and robots to disaster-stricken areas and collects video and sensor information in real time.

[1549] The collected data is analyzed on a server and provided to relevant organizations.

[1550] example

[1551] After an earthquake occurs, the server uses drones to collect detailed images of the affected area and transmits the analysis results to rescue teams in real time, enabling them to make quick decisions and take action.

[1552] As described above, this invention is a system that combines various technologies to enable real-time disaster countermeasures and minimize damage. Implementing this system is expected to significantly improve disaster preparedness and response, protecting many lives and property.

[1553] The processing flow will be explained below.

[1554] 1. Early Warning System

[1555] server

[1556] Step 1:

[1557] The server collects weather data from weather satellites and ground observation stations.

[1558] Similarly, sensor data is collected in real time from seismometers, river water level gauges, and other sources.

[1559] Step 2:

[1560] The collected weather data and sensor data are stored in a database.

[1561] The stored data is fed into AI algorithms to detect abnormal patterns.

[1562] Step 3:

[1563] AI algorithms analyze the data and detect unusual patterns and fluctuations.

[1564] Based on the detected abnormal data, it is determined whether it is a sign of a disaster.

[1565] Step 4:

[1566] If it is determined to be a sign of a disaster, a warning message will be generated.

[1567] This warning message is sent to designated terminals and relevant organizations.

[1568] 2. Evacuation route optimization

[1569] Terminal

[1570] Step 1:

[1571] The user's device (smartphone) obtains current location information using the GPS function.

[1572] The current location information is periodically sent to the server.

[1573] server

[1574] Step 2:

[1575] The server analyzes real-time traffic and road information based on the received current location information.

[1576] Uses AI algorithms to calculate optimal evacuation routes.

[1577] Terminal

[1578] Step 3:

[1579] The optimal evacuation route information is sent to the user's terminal.

[1580] Evacuation route information is displayed on the device app and provided to the user.

[1581] Step 4:

[1582] If roads are closed during an evacuation, the server recalculates a new route and sends updates to the device.

[1583] 3. Damage prediction and risk analysis

[1584] server

[1585] Step 1:

[1586] The server collects past disaster data and current weather data and inputs it into the AI ​​model.

[1587] The AI ​​model predicts the extent and impact of disasters based on past data.

[1588] Step 2:

[1589] Based on the prediction results, a risk assessment is carried out.

[1590] Based on the results of the risk assessment, identify the amount of resources needed (relief supplies, medical resources, etc.).

[1591] Step 3:

[1592] Prepare a resource allocation plan and provide it to relevant agencies and users.

[1593] 4. Strengthening communication

[1594] User

[1595] Step 1:

[1596] Users enter disaster situations and requests for assistance into the app.

[1597] Input methods include voice input and text input.

[1598] server

[1599] Step 2:

[1600] The server feeds the entered information into natural language processing and machine translation algorithms.

[1601] Carry out translation between different languages.

[1602] Step 3:

[1603] Translated information will be sent to rescue teams and relevant agencies.

[1604] 5. Use of drones and robots

[1605] server

[1606] Step 1:

[1607] The server will control AI-equipped drones and robots and begin collecting information from the disaster site.

[1608] The drone follows a set flight path and collects data in real time.

[1609] Step 2:

[1610] The server analyzes the collected data in real time to grasp the detailed situation at the disaster site.

[1611] If necessary, the drone or robot's movements will be adjusted to gather more detailed information.

[1612] Step 3:

[1613] The analysis results will be provided to relevant agencies and rescue teams to assist in rapid response.

[1614] 6. Demand forecasting and resource allocation

[1615] server

[1616] Step 1:

[1617] The server inputs past disaster data and current situation data into the AI ​​model.

[1618] AI models predict demand (food, water, medical supplies, etc.) after a disaster occurs.

[1619] Step 2:

[1620] Calculate the amount of relief supplies and medical resources needed based on forecast data.

[1621] Develop distribution plans and support efficient relief efforts.

[1622] Step 3:

[1623] The allocation plan will be provided to relevant organizations and users to ensure appropriate resource allocation.

[1624] Based on these steps, the system of the present invention can realize rapid and effective countermeasures against natural disasters, and can save many lives and properties.

[1625] Example 1

[1626] 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."

[1627] In recent years, the frequency of natural disasters has increased, and the resulting damage has become more severe. This has created a need for systems that can quickly and accurately predict disaster risks and minimize damage. However, current disaster response systems lack the precision to collect and analyze real-time data, and are lacking in the ability to detect abnormal situations early and issue warnings. Furthermore, improvements are needed to optimize evacuation routes and improve the efficiency of damage prediction and resource allocation. Furthermore, there is a lack of means to facilitate communication between disaster-stricken areas and rescue teams.

[1628] 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.

[1629] In this invention, the server includes means for collecting and analyzing meteorological information and environmental monitoring data in real time, means for predicting future disaster risks based on past disaster information, means for detecting abnormal patterns and identifying signs of disaster, means for automatically issuing warnings based on the identified signs, means for calculating optimal evacuation routes and providing them to users in real time, means for supporting information exchange between the disaster-stricken area and rescue teams, means for providing optimal evacuation routes using current location information from the terminal, means for analyzing collected data with an AI algorithm and detecting abnormal patterns, and means for analyzing past data using a generative AI model to predict disaster risks.This enables early detection of disaster risks in real time, rapid issuance of warnings, provision of efficient evacuation routes, optimal resource allocation, and smooth communication between the disaster-stricken area and rescue teams.

[1630] "Weather information" refers to data such as temperature, humidity, wind speed, and precipitation obtained from meteorological satellites and ground observation stations.

[1631] "Environmental monitoring data" refers to data used to monitor environmental changes obtained from sensors such as seismometers and river water level gauges.

[1632] "Past disaster information" refers to data on natural disasters that have occurred in the past, including details of damage, weather patterns, and relief efforts.

[1633] "Disaster risk" indicates the possibility of a disaster occurring in a particular area in the future and the magnitude of its impact.

[1634] "Abnormal patterns" refer to fluctuations in data that differ from normal weather data or sensor data and are thought to be a sign of a disaster.

[1635] A "warning" is information that notifies that a disaster is expected to occur based on a detected abnormal pattern.

[1636] An "evacuation route" refers to a route for safe evacuation in the event of a disaster, and is calculated based on real-time traffic and environmental conditions.

[1637] "User" refers to an individual or organization that uses the disaster response system to receive data and carry out evacuation and countermeasures.

[1638] "Relief supplies" refers to food, water, medicine and other basic necessities provided to disaster victims in the event of a disaster.

[1639] "Medical resources" is a general term for medical equipment, medicines, medical staff, etc. needed for disaster response.

[1640] A "generative AI model" is a type of machine learning algorithm that learns from past disaster data and predicts future disaster risks.

[1641] A "terminal" is a device that a user uses to receive disaster information and evacuation routes in real time, such as a smartphone or PC.

[1642] The present invention is a disaster prevention system that utilizes AI, and its configuration and implementation method will be described in detail below.

[1643] Real-time collection and analysis of meteorological and environmental monitoring data

[1644] server

[1645] The server periodically retrieves weather information from weather satellites and ground observation stations via an API, using scripts written in Python and a database management system (e.g., PostgreSQL).

[1646] The server also receives environmental monitoring data from seismometers and river level gauges, which are collected using IoT platforms such as AWS IoT.

[1647] The collected data is stored in a database in real time and analyzed using AI algorithms (e.g., TensorFlow).

[1648] Specific examples

[1649] For example, during periods of heavy rain, the server can collect data from water level sensors and detect sudden rises in water levels. By analyzing this data, flood risks can be detected early and appropriate warnings can be issued.

[1650] Analysis of past disaster information and prediction of future disaster risks

[1651] server

[1652] The server collects past disaster data and inputs it into a generative AI model (e.g., GPT-4) for analysis.

[1653] The AI ​​model predicts future disaster risks based on data such as weather patterns, damage, and relief efforts.

[1654] The prediction results are stored in a database and notified to relevant agencies.

[1655] Specific examples

[1656] The server inputs past hurricane data into a generative AI model for analysis, and then predicts the hurricane's path and the extent of the damage it will cause based on current weather data. This information is then sent to relevant organizations to help with advance preparations.

[1657] Detecting abnormal patterns and raising alerts

[1658] server

[1659] The server analyzes weather and environmental monitoring data using AI algorithms to detect abnormal patterns.

[1660] If an anomaly is detected, an alert is generated and notified to the specified device, using Firebase Cloud Messaging and Twilio APIs.

[1661] Specific examples

[1662] A server that monitors data from the seismometer detects abnormal tremors that could signal a major earthquake and immediately sends a warning to the smartphones of local residents.

[1663] Providing optimal evacuation routes

[1664] Terminal

[1665] The user's smartphone uses GPS to send current location information to the server.

[1666] The server takes into account traffic conditions and road closures to calculate the best evacuation route, using the Google Maps API and OpenStreetMap API.

[1667] The terminal provides the user with the evacuation route information received from the server, displays a map, and performs real-time navigation.

[1668] Specific examples

[1669] When a fire occurs, the user launches the app on their smartphone, and the server calculates the optimal evacuation route, taking into account traffic congestion information and closed roads, and provides it to the user in real time.

[1670] Damage prediction and resource allocation optimization

[1671] server

[1672] The server analyzes the collected data and past disaster data to predict the extent of damage and the impact on each region.

[1673] Based on the forecast data, the amount of relief supplies and medical resources needed is calculated and an efficient distribution plan is developed.

[1674] The allocation plan will be communicated to relevant agencies to assist in rapid response.

[1675] Specific examples

[1676] When a major typhoon is approaching, the server creates a list of predicted affected areas and needed relief supplies, and notifies relevant agencies to help them make prompt preparations.

[1677] Strengthening information exchange between affected areas and relief teams

[1678] User

[1679] Users use a smartphone app to input information about the damage situation and requests for help in natural language.

[1680] The server analyzes the input information, translates it appropriately, and notifies the relevant authorities. Generative AI models (e.g., GPT-4) are used for natural language processing.

[1681] Specific examples

[1682] When a victim types in Japanese, "Help me, my house has collapsed," the server translates this into English and quickly notifies rescue teams.

[1683] Prompt Sentence Examples

[1684] "Based on past hurricane data, analyze current weather data and predict future hurricane risk."

[1685] "Please provide sample code for an algorithm that analyzes collected sensor information and meteorological data to detect flood risk early."

[1686] "Please show us how to translate disaster information entered in Japanese into English and notify the relief team."

[1687] In this way, the present invention is a system that uses multi-layered technologies to enable real-time disaster countermeasures and minimize damage. Implementing this system is expected to significantly improve disaster preparedness and response, saving many lives and property.

[1688] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1689] Step 1: Data collection

[1690] server

[1691] The server periodically retrieves weather data from weather satellites and ground observation stations using APIs. It also receives environmental monitoring data such as seismometers and river water level gauges. This process uses scripts written in Python and a database management system (e.g., PostgreSQL). The input is raw data retrieved from the API, and the output is data stored in a database in an organized format.

[1692] Specific actions

[1693] The server receives data from weather satellites every 30 minutes and inserts it into a database in JSON format. It also receives data from seismometers and analyzes it in real time.

[1694] Step 2: Data storage and processing

[1695] server

[1696] The collected meteorological and environmental monitoring data is stored in a database in real time. This data is then immediately analyzed by AI algorithms using Python (e.g., TensorFlow). The input is the raw data points, and the output is the analyzed indicators and abnormal pattern detection results.

[1697] Specific actions

[1698] The server periodically reads the weather and sensor data stored in the database and analyzes it using a TensorFlow model. Any abnormal patterns detected are stored in a separate table and sent to the warning system.

[1699] Step 3: Detect and alert on abnormal patterns

[1700] server

[1701] The server uses AI algorithms to detect abnormal patterns, which involves recognizing different behavior compared to normal weather patterns. The input is the pre-processed data points, and the output is the identification of abnormal patterns and a corresponding warning message.

[1702] Specific actions

[1703] If an abnormally large tremor is detected in the seismograph data, the information is analyzed and a warning message is quickly generated and sent to relevant organizations and users' terminals.

[1704] Step 4: Optimize evacuation routes

[1705] Terminal

[1706] The user's smartphone uses GPS to obtain their current location and sends that information to a server. The server then uses real-time traffic and environmental data to calculate the optimal evacuation route. The input is the GPS location and current traffic and environmental data, and the output is the optimized route information.

[1707] Specific actions

[1708] In the event of a fire, when a user launches the evacuation app, the device sends its current location information to the server, which then calculates the optimal evacuation route, taking into account real-time traffic congestion information and road closures, and displays it on the device.

[1709] Step 5: Damage prediction and resource allocation

[1710] server

[1711] The server analyzes the collected data and past disaster data to predict the extent of damage and the impact on each region. It also calculates the required quantities of relief supplies and medical resources and creates an efficient distribution plan. The input is an integrated disaster dataset, and the output is the predicted extent of damage and a resource allocation plan.

[1712] Specific actions

[1713] If a major typhoon is approaching, the server analyzes multiple data points and runs a predictive model to create a list of affected areas and needed supplies, which is then immediately communicated to the appropriate agencies.

[1714] Step 6: Strengthen communication

[1715] User

[1716] Users can use a smartphone app to input information about the disaster situation and requests for assistance in natural language. The server analyzes this information, translates it appropriately, and notifies the relevant authorities. The input is text or voice data entered in natural language, and the output is translated text information.

[1717] Specific actions

[1718] When a victim voices a request for help in Japanese, the server translates it into English and quickly notifies rescue teams from overseas.

[1719] Step 7: Dispatch drones and robots and gather information

[1720] server

[1721] The server dispatches AI-equipped unmanned aerial vehicles and autonomous mobile devices to disaster-stricken areas to collect video and sensor information in real time. The collected data is analyzed by the server and provided to relevant organizations. The input is the video and sensor information collected in real time, and the output is analyzed information and specific instructions.

[1722] Specific actions

[1723] After an earthquake occurs, the server dispatches a drone to the site to collect aerial video data, which is then analyzed immediately and provided to rescue teams as information to grasp the situation on the ground.

[1724] Through these processing steps, this system aims to enable real-time disaster response and minimize damage, significantly improving disaster response preparation and execution, and saving many lives and property.

[1725] (Application example 1)

[1726] 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."

[1727] Recently, the frequency and intensity of natural disasters have increased, necessitating rapid response and evacuation. However, current disaster response systems lack the ability to collect and analyze information in real time, provide optimal evacuation routes, detect abnormal patterns early, and effectively share information between affected areas and rescue teams. Furthermore, language barriers mean that requests for help and the transmission of information take time. This makes it difficult to respond quickly and accurately when a disaster occurs, and poses the challenge of minimizing damage to human life and property.

[1728] 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.

[1729] In this invention, the server includes means for collecting and analyzing meteorological data and sensor data in real time, means for predicting future disaster risks based on past disaster data, means for detecting abnormal patterns and identifying signs of disaster, means for automatically issuing warnings based on the identified signs, means for calculating optimal evacuation routes and providing them to users in real time, means for supporting information exchange between disaster-stricken areas and rescue teams and performing automatic translation, and means for updating and providing evacuation route information in real time. This enables early collection and analysis of information, provision of optimal evacuation routes, early detection of abnormal patterns, and efficient information sharing, thereby realizing rapid and accurate measures in the event of a disaster.

[1730] "Weather data" refers to data related to weather collected from meteorological observation equipment, satellites, etc.

[1731] "Sensor data" refers to data about the environment and situation collected from various sensors (seismic sensors, water level gauges, temperature sensors, etc.).

[1732] "Real-time" means that collection and processing occur immediately, without any time delay.

[1733] "Past disaster data" refers to records and related information relating to disasters that have occurred in the past.

[1734] "Disaster risk" refers to the scale and impact of potential future disasters.

[1735] An "abnormal pattern" refers to a specific data fluctuation or behavior that is different from the norm.

[1736] "Disaster precursors" are signs or indicators that indicate a high probability of a disaster occurring.

[1737] A "warning" is a notification or alert to notify you of danger or risk.

[1738] An "evacuation route" is a route used to evacuate to a safe place in the event of a disaster.

[1739] "Information exchange" is the act of sharing information among multiple parties.

[1740] "Translation" refers to the conversion of written or spoken content in one language into another language.

[1741] An "unmanned aerial vehicle" is an aircraft that can be remotely controlled or fly autonomously, such as a drone.

[1742] "Affected area" means the area or area affected by a disaster.

[1743] "Resources" refers to the supplies, personnel, and equipment needed for relief operations and recovery.

[1744] "Automatic" means that a machine or system operates independently, without human intervention.

[1745] The present invention is a disaster recovery system, and is implemented by the following method and configuration.

[1746] 1. Real-time collection and analysis of meteorological and sensor data

[1747] The server collects weather data from meteorological observation equipment and satellites, and sensor data from various sensors (such as seismometers, water level gauges, and temperature sensors). This data is stored in a database in real time and analyzed by AI algorithms (such as TensorFlow and PyTorch). This makes it possible to detect unusual patterns that are out of the ordinary and identify signs of disaster.

[1748] Specific examples

[1749] The server detects ongoing heavy rainfall and confirms that water levels are rising rapidly from data from water level sensors, allowing for early detection of potential floods and response.

[1750] 2. Analysis of past disaster data and prediction of disaster risks

[1751] The server collects past disaster data, inputs it into an AI model, and analyzes it. Based on the analysis results, future disaster risks can be predicted. Data such as weather patterns, damage, and relief efforts are used to assess the risk in a specific area.

[1752] Specific examples

[1753] When predicting the path of a hurricane, the server analyzes current weather data based on past hurricane data to predict the possible extent and impact of damage, allowing necessary preparations to be made in advance.

[1754] 3. Detecting abnormal patterns and issuing warnings

[1755] The server analyzes the collected data in real time to detect abnormal patterns, and automatically issues warnings based on the identified signs. These warnings are sent as push notifications to users' smartphones or computers, and simultaneously to relevant authorities.

[1756] Specific examples

[1757] Servers monitoring data from local seismometers detect the signs of an earthquake that is building up in size and send warnings to local residents' smartphones.

[1758] 4. Calculating and providing optimal evacuation routes

[1759] The device (user's smartphone) uses GPS to send its current location information to the server. The server analyzes traffic conditions, road closure information, and real-time environmental conditions to calculate the optimal evacuation route. The calculation results are provided to the user's smartphone in real time.

[1760] Specific examples

[1761] In the event of a fire, users can launch the app on their smartphones and get real-time information on the optimal evacuation route. If the route is blocked, the server will calculate and provide a new route.

[1762] 5. Supporting information exchange between disaster-stricken areas and relief teams

[1763] Users can use a smartphone app to voice-input information about the damage situation and requests for assistance. The server analyzes the input using voice recognition (Google Speech-to-Text), translates it appropriately (Google Translate API), and automatically sends it to the relevant organizations. This allows information to be shared quickly, even between different languages.

[1764] Specific examples

[1765] When a victim types "rescue needed" in Japanese into a smartphone app, the server translates the message and transmits it in English to overseas rescue teams.

[1766] 6. Control and intelligence gathering of AI-equipped unmanned aerial vehicles and robots

[1767] The server dispatches AI-equipped unmanned aerial vehicles and robots to disaster-stricken areas and collects video and sensor information in real time. The collected data is analyzed by the server and provided to relevant organizations. The collected data is also used to create optimal resource allocation plans, streamlining rescue efforts.

[1768] Specific examples

[1769] After an earthquake occurs, the server uses unmanned aerial vehicles to collect detailed images of the affected area and transmits the analysis results to rescue teams in real time, enabling them to make quick decisions and take action.

[1770] Prompt Sentence Examples

[1771] "Is there an increased risk of flooding in this location? Please advise the best evacuation route. Also, please send a request for help in English."

[1772] Combining these functions will enable adequate preparation and rapid response to disasters, making it possible to protect many lives and property.

[1773] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1774] Step 1:

[1775] The server collects weather data from meteorological observation equipment and satellites, and sensor data from various sensors (seismic sensors, water level gauges, temperature sensors, etc.). The data is sent to the server in real time via API and stored in a database. The input is various weather data and sensor data, and the output is the stored data. Specifically, the data collection script periodically sends requests to the API endpoint and stores the obtained data in the database.

[1776] Step 2:

[1777] The server analyzes the collected data in real time to detect abnormal patterns. The input is weather data and sensor data stored in a database, and the output is the results of detecting abnormal patterns. AI algorithms (such as TensorFlow and PyTorch) are used to process the data and analyze data points that fall outside of normal ranges. Specifically, the data is input into an AI model, and a function is executed to detect anomalies.

[1778] Step 3:

[1779] The server collects past disaster data, inputs it into an AI model, and analyzes it. The input is historical disaster data, and the output is disaster risk prediction results. Data processing involves comparing past disaster data with current data and applying a model that predicts future risks. Specifically, past and real-time data are input into the AI ​​model, and a risk prediction function is executed.

[1780] Step 4:

[1781] The server automatically issues an alert based on the detected anomaly pattern. The input is the anomaly pattern detection result, and the output is an alert notification. Data processing involves triggering the anomaly detection result to send an alert message to a push notification service (such as Firebase). Specifically, the alert generation function receives the anomaly detection event and sends an appropriate message to the notification service.

[1782] Step 5:

[1783] The device uses GPS to send current location information to the server. The input is the user's current location information, and the output is the location information sent to the server. Specifically, the smartphone app obtains the GPS data and sends it to the server via an HTTP request.

[1784] Step 6:

[1785] The server analyzes traffic conditions, road closure information, and real-time environmental conditions to calculate the optimal evacuation route. The input is current location information and traffic / road information, and the output is the calculated evacuation route. Data processing involves applying an algorithm that takes into account information such as road closures and traffic delays to find the shortest and most optimal route. Specifically, the route calculation function is executed, and the generated route is sent to the user's device.

[1786] Step 7:

[1787] The server predicts the extent of damage and resource allocation based on the collected data. The input is real-time data and past disaster data, and the output is a resource allocation plan. Data processing involves using a damage prediction model to generate prediction results and calculate the amount of relief resources required. In concrete terms, the prediction function processes the data, and the results are linked to the resource management system.

[1788] Step 8:

[1789] The server analyzes the user's voice input, performs automatic translation, and sends the information to the relevant organizations. The input is the user's voice data, and the output is translated text. Data processing involves converting the voice into text using voice recognition (Google Speech-to-Text), and then translating it into another language using a translation system (Google Translate API). Specifically, the voice recognition function processes the voice data, and the translation function converts the text into another language and sends it to the relevant organizations.

[1790] Step 9:

[1791] The server controls AI-equipped unmanned aerial vehicles and robots and collects information on disaster sites in real time. The input is sensor data and video data from the unmanned aerial vehicles and robots, and the output is analyzed information. Data processing involves analyzing the collected data and extracting important information. Specifically, the control program operates the unmanned aerial vehicles and robots and sends the collected data to the analysis server.

[1792] These processing steps enable a quick and accurate response in the event of a disaster, ensuring the safety of users.

[1793] 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.

[1794] This invention is a disaster prevention system that utilizes AI, and combines mainly real-time collection and analysis of meteorological data and sensor data, analysis of past disaster data, detection and warning of abnormal patterns, optimization of evacuation routes, damage prediction and resource allocation, enhanced communication, utilization of AI-equipped drones and robots, and an emotion engine that recognizes user emotions. The specific operation of each function in the system of this invention will be explained.

[1795] Real-time collection and analysis of weather and sensor data

[1796] server

[1797] The server collects weather data from weather satellites and ground observation stations, and collects sensor data in real time from seismometers, river water level gauges, and other devices.

[1798] The collected data is stored in a database and analyzed using AI algorithms.

[1799] The server analyzes weather and sensor data to detect signs of disaster.

[1800] example

[1801] For example, during periods of heavy rain, the server collects data from various sensors and detects sudden rises in water levels. By analyzing this data, it is possible to identify potential floods early and take appropriate action.

[1802] Detecting abnormal patterns and issuing alerts

[1803] server

[1804] The server analyzes the collected weather and sensor data to detect any abnormal patterns.

[1805] If the identified abnormal pattern is judged to be a sign of a disaster, a warning message will be generated and sent to designated terminals and relevant organizations.

[1806] example

[1807] Servers monitoring seismometer data detect the signs of an earthquake that is building up in size and send warnings to local residents' smartphones.

[1808] Optimizing evacuation routes

[1809] Terminal

[1810] The device (user's smartphone) uses its GPS function to obtain current location information and periodically sends it to the server.

[1811] server

[1812] Based on the received current location information, the server analyzes real-time traffic and road information and calculates the optimal evacuation route.

[1813] Terminal

[1814] Information on the optimal evacuation route is sent to the user's device and displayed on the device's app.

[1815] example

[1816] In the event of a fire, users can launch the app on their smartphones and get real-time information on the optimal evacuation route. If the route is blocked, the server will calculate and provide a new route.

[1817] Damage prediction and resource allocation optimization

[1818] server

[1819] The server inputs past disaster data and current weather data into an AI model for analysis.

[1820] The AI ​​model will generate a damage scope and resource allocation plan and provide it to relevant agencies.

[1821] example

[1822] When a major typhoon is approaching, the server creates a list of predicted affected areas and needed relief supplies, and automatically sends it to the relevant organizations, allowing for faster preparations.

[1823] Strengthening communication

[1824] User

[1825] Users can use a smartphone app to input disaster situations and requests for help.

[1826] The entered information undergoes natural language processing and machine translation on the server and is then sent to the relevant organizations.

[1827] example

[1828] If a victim requests help from a rescue team in Japanese, they can input their voice into the app, and the server will translate the message into English and quickly transmit the information to rescue teams dispatched from overseas.

[1829] Utilizing AI-equipped drones and robots

[1830] server

[1831] The server controls AI-equipped drones and robots and collects information from disaster sites in real time.

[1832] The collected data is analyzed on a server and provided to relevant organizations.

[1833] example

[1834] After an earthquake occurs, the server uses drones to collect detailed images of the affected area and transmits the analysis results to rescue teams in real time, enabling them to make quick decisions and take action.

[1835] Introducing the Emotion Engine

[1836] Terminal

[1837] An emotion engine is built into the user's device (smartphone) and recognizes emotions from the user's voice and text in real time.

[1838] server

[1839] The emotion engine assesses the user's mental state and automatically contacts experts if necessary.

[1840] The content of evacuation instructions and warnings is optimized based on user emotional data.

[1841] example

[1842] If the emotion engine detects that the user is panicking during a disaster, the server notifies a professional counselor and sends a calming message to the user, encouraging them to take action to ensure their safety.

[1843] As described above, this invention is a system that combines various technologies to realize real-time disaster countermeasures and minimize damage. Implementing this system is expected to significantly improve disaster preparedness and response, protecting many lives and property.

[1844] The processing flow will be explained below.

[1845] Real-time collection and analysis of weather and sensor data

[1846] server

[1847] Step 1:

[1848] The server collects weather data from weather satellites and ground observation stations.

[1849] Data collected includes temperature, precipitation, wind speed, and wind direction.

[1850] Step 2:

[1851] The server collects data in real time from sensors such as seismometers and river water level gauges.

[1852] The data collected includes earthquake intensity and water level fluctuations.

[1853] Step 3:

[1854] The server stores the collected weather and sensor data in a database.

[1855] The database is designed with a structure that allows fast access for analysis.

[1856] Step 4:

[1857] The server inputs the stored data into an AI algorithm and begins analysis.

[1858] AI algorithms use machine learning models to detect anomalous patterns.

[1859] Detecting abnormal patterns and issuing alerts

[1860] server

[1861] Step 1:

[1862] The server analyzes weather and sensor data to detect unusual patterns.

[1863] Unusual patterns include sudden temperature changes, unusual seismic activity, and sudden rises in river levels.

[1864] Step 2:

[1865] AI algorithms determine whether unusual patterns are a sign of disaster.

[1866] The criteria for judgment are determined by comparing with past data and whether or not it exceeds a certain threshold.

[1867] Step 3:

[1868] The server generates a warning message if it determines that a disaster is imminent.

[1869] The warning message includes information such as the type of disaster, predicted damage, and the need for evacuation.

[1870] Step 4:

[1871] The server sends the generated warning message to designated devices (smartphones, PCs) and relevant organizations.

[1872] Optimizing evacuation routes

[1873] Terminal

[1874] Step 1:

[1875] The user's device acquires current location information using the GPS function.

[1876] The acquired location information is sent to the server at regular intervals.

[1877] server

[1878] Step 2:

[1879] The server analyzes real-time traffic and road information based on the received current location information.

[1880] Analysis includes current traffic conditions, road closure information, and disaster situations.

[1881] Step 3:

[1882] The server uses AI algorithms to calculate the optimal evacuation route.

[1883] The calculation results show the safest evacuation route in the shortest time.

[1884] Terminal

[1885] Step 4:

[1886] The optimal evacuation route information received from the server is displayed on the user's terminal.

[1887] Evacuation route information can be visually confirmed in map format.

[1888] Step 5:

[1889] If a road is blocked during an evacuation, the server recalculates a new route and sends updates to the device.

[1890] Damage prediction and resource allocation optimization

[1891] server

[1892] Step 1:

[1893] The server collects past disaster data and current weather data and inputs it into the AI ​​model.

[1894] The data includes records of damage caused by past disasters, weather conditions, and resource allocation.

[1895] Step 2:

[1896] Based on the collected data, the AI ​​model identifies the extent of damage that can be predicted in the future based on the current situation.

[1897] The prediction includes the probability of occurrence and the extent of damage.

[1898] Step 3:

[1899] The server generates a resource allocation plan based on the prediction results.

[1900] The plan includes necessary relief supplies, medical resources, response times, etc.

[1901] Step 4:

[1902] The resource allocation plan will be sent to the relevant agencies to support a rapid response.

[1903] Strengthening communication

[1904] User

[1905] Step 1:

[1906] Users use a smartphone app to input information about the disaster situation and requests for help.

[1907] Input methods include voice input and text input.

[1908] server

[1909] Step 2:

[1910] The server analyzes the input information using a natural language processing algorithm and extracts the necessary information.

[1911] If information is entered in multiple languages, it is automatically machine translated.

[1912] Step 3:

[1913] The extracted and translated information is automatically sent to relevant agencies and relief teams.

[1914] Utilizing AI-equipped drones and robots

[1915] server

[1916] Step 1:

[1917] The server controls AI-equipped drones and robots and dispatches them to disaster sites.

[1918] Drones and robots operate according to set flight and movement paths.

[1919] Step 2:

[1920] Drones and robots collect footage and sensor data from disaster sites in real time.

[1921] The collected data is immediately sent to the server.

[1922] Step 3:

[1923] The server analyzes the received data and grasps the situation on site.

[1924] If necessary, the operation of drones and robots will be adjusted to gather more information.

[1925] Step 4:

[1926] The analysis results will be provided to relevant agencies and rescue teams to assist in rapid response.

[1927] Introducing the Emotion Engine

[1928] Terminal

[1929] Step 1:

[1930] The emotion engine built into the user's device analyzes the user's voice and text in real time and recognizes emotions.

[1931] server

[1932] Step 2:

[1933] The server receives the emotion data sent from the emotion engine and evaluates the mental state of the user.

[1934] If the user's emotions are judged to be dangerous, an appropriate alert or support message will be generated.

[1935] Step 3:

[1936] If necessary, emotional data will be used to automatically connect you with a specialist (medical professional or counselor).

[1937] The expert is informed of the user's current emotional state and situation.

[1938] Step 4:

[1939] The server analyzes the user's emotional data and optimizes the evacuation instructions and warnings to suit the user's psychological state.

[1940] Optimized warnings and instructions are sent to the user's device.

[1941] example

[1942] In the event of a disaster, if the system detects a panicked state in the user's voice, the emotion engine will display a message to alleviate the user's anxiety. It will also automatically notify a professional counselor based on the user's situation and provide necessary support.

[1943] Based on the above steps, the system of the present invention can realize quick and effective countermeasures against natural disasters and minimize damage, which is expected to save many lives and properties.

[1944] Example 2

[1945] 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."

[1946] In recent years, natural disasters have become more frequent, and rapid and accurate responses are required to minimize the damage they cause. However, existing systems have limitations in real-time data collection and analysis, evacuation route optimization, and user emotion recognition. Furthermore, there is a lack of information collection and damage prediction using autonomous devices equipped with AI. This can lead to delayed responses in the event of a disaster, potentially exacerbating the damage.

[1947] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting and analyzing meteorological data and sensor data in real time, means for predicting future disaster risks based on past disaster data, means for detecting abnormal patterns and identifying precursors of disasters, means for automatically issuing warnings based on the identified precursors, means for calculating optimal evacuation routes and providing them to the user in real time, means for supporting information exchange between the disaster-stricken area and rescue teams, means for acquiring user location information and calculating optimal evacuation routes based on real-time traffic data, and means for recognizing the user's emotions and providing appropriate instructions and warnings. This enables disaster countermeasures to be taken in real time, minimizing damage.

[1948] "Weather data" refers to information about atmospheric conditions, including temperature, humidity, wind speed, air pressure, and precipitation.

[1949] "Sensor data" refers to various types of measurement information obtained by sensors such as seismometers and river water level gauges, and includes data indicating environmental and physical conditions.

[1950] "Real-time" refers to data and information being collected, processed, and distributed almost instantly.

[1951] "Analysis" refers to analyzing collected data and extracting meaning and patterns from it.

[1952] "AI algorithms" refer to calculation methods and mathematical models that utilize artificial intelligence technology and are used to predict and analyze data.

[1953] "Past disaster data" refers to recorded information about disasters that have occurred in the past, and includes data such as the extent of damage, causes of occurrence, and effectiveness of countermeasures.

[1954] "Disaster risk" is an indicator that shows the possibility of a disaster occurring and the extent of its impact.

[1955] An "abnormal pattern" refers to a situation in which data movements or trends that differ from normal are observed.

[1956] "Disaster precursors" refer to abnormal changes in data or signs of actual natural phenomena that occur before a disaster occurs.

[1957] "Warning" refers to a message that alerts you to an abnormal situation or potential danger.

[1958] An "evacuation route" refers to the route that should be taken to evacuate to a safe place in the event of a disaster.

[1959] "Information exchange" refers to the exchange of data or messages between two or more parties.

[1960] A "rescue team" refers to a specialized unit formed to rescue and provide support to victims in the event of a disaster.

[1961] "Location information" refers to latitude and longitude information for a specific location or region.

[1962] "Traffic data" refers to information about current road conditions and traffic flow.

[1963] "User's emotions" refers to information that represents the emotions and mental state that the user is experiencing.

[1964] "Autonomous devices" refer to devices such as drones and robots that are equipped with AI and machine learning technology and operate automatically.

[1965] "Resource allocation" refers to the efficient distribution of necessary materials and personnel.

[1966] This invention is a disaster prevention system that utilizes AI, and combines mainly real-time collection and analysis of meteorological data and sensor data, analysis of past disaster data, detection and warning of abnormal patterns, optimization of evacuation routes, damage prediction and resource allocation, enhanced communication, utilization of autonomous devices equipped with AI, and an emotion engine that recognizes user emotions. Each function of this invention is explained in detail below.

[1967] Real-time collection and analysis of weather and sensor data

[1968] server

[1969] The server retrieves weather data provided by weather satellites and ground observation stations via an API. It also collects real-time data from various sensors, such as seismometers and river water level gauges. The API is implemented using Python to manage data requests and responses.

[1970] The collected data is stored in a PostgreSQL database and analyzed using an AI algorithm powered by TensorFlow, which allows for the detection of abnormal patterns and signs of disasters from large amounts of data.

[1971] Specific examples

[1972] For example, to detect the risk of flooding due to heavy rain at an early stage, the server analyzes meteorological data and water level gauge data to detect abnormally high water levels, and sends a warning to the relevant authorities.

[1973] Prompt Sentence Examples

[1974] "If the water level sensor detects an abnormality during heavy rainfall, please provide the analysis procedures for taking appropriate action."

[1975] Detecting abnormal patterns and issuing alerts

[1976] server

[1977] The server uses PySpark to perform distributed processing of collected weather and sensor data to detect abnormal patterns. If the detected abnormal pattern is judged to be a sign of a disaster, a warning message is generated on the spot.

[1978] The generated warning message is sent to designated user devices and relevant organizations using the Twilio API.

[1979] Specific examples

[1980] If an abnormal vibration pattern is detected from the seismometer data, the server analyzes it as a precursor to an earthquake and sends a warning message to the smartphones of local residents.

[1981] Prompt Sentence Examples

[1982] "If an anomaly is detected in the seismometer data, please explain the procedure for generating a warning message and sending it to the relevant authorities."

[1983] Optimizing evacuation routes

[1984] Terminal

[1985] The user's smartphone acquires the current location information using the GPS function and periodically sends it to the server, using HTTP requests and WebSockets for communication.

[1986] server

[1987] The server obtains real-time traffic data using the Google Maps API and Waze API, and calculates the optimal evacuation route using the Dijkstra algorithm and the A algorithm.

[1988] The calculated optimal evacuation route is sent to the user's smartphone app and displayed on the screen.

[1989] Specific examples

[1990] In the event of a fire, users can receive real-time evacuation route information via a smartphone app, and if a road is closed, the server will calculate and provide a new route.

[1991] Prompt Sentence Examples

[1992] "Explain the steps to use GPS data and real-time traffic information to calculate the optimal route to optimize evacuation routes."

[1993] Damage prediction and resource allocation optimization

[1994] server

[1995] The server collects past disaster data and current weather data and uses OpenAI's generative AI model to predict damage.

[1996] Based on the analysis results of the AI ​​model, a distribution plan for necessary relief supplies and medical resources is generated.

[1997] Specific examples

[1998] When a major typhoon is approaching, the server creates a list of areas expected to be affected and necessary supplies, and automatically notifies relevant organizations, enabling effective preparations.

[1999] Prompt Sentence Examples

[2000] "When a large typhoon is approaching, please explain the procedure for generating damage forecasts and lists of relief supplies and notifying relevant organizations."

[2001] Strengthening communication

[2002] User

[2003] Users can use a smartphone app to input information about the situation in the affected area and requests for help. Information can be entered using both voice and text.

[2004] server

[2005] The server uses the Google Cloud Natural Language API to parse the user's input and perform machine translation if necessary, then sends the translated information to the appropriate authorities.

[2006] Specific examples

[2007] When a victim requests help in Japanese, they can input their voice and the server will automatically translate it into English and transmit it to a rescue team dispatched from overseas.

[2008] Prompt Sentence Examples

[2009] "Please explain the procedure for analyzing the voice input of the victim using natural language processing and machine translation and conveying it appropriately to the rescue team."

[2010] Utilizing AI-powered autonomous machines

[2011] server

[2012] The server will control autonomous devices (e.g., drones and robots) equipped with AI technology to collect detailed information on disaster sites, including the use of autonomous devices equipped with NVIDIA Jetson.

[2013] The collected data is sent to a server in real time, and the analysis results are provided to relevant organizations.

[2014] Specific examples

[2015] After an earthquake occurs, the server uses drones to collect footage of the affected area and transmits the analysis results to rescue teams in real time.

[2016] Prompt Sentence Examples

[2017] "Please explain the procedure for using drones to collect information from disaster sites and analyze it in real time."

[2018] User Emotion Recognition

[2019] Terminal

[2020] The user's smartphone is equipped with an emotion engine that uses Amazon Rekognition, which recognizes emotions from the user's voice and text.

[2021] server

[2022] The server uses an emotion engine to assess the user's mental state and notify experts as necessary. It also optimizes evacuation instructions and warnings based on emotion data.

[2023] Specific examples

[2024] During a disaster, if the emotion engine detects that the user is in a panic, the server notifies a counselor and sends a calming message to the user.

[2025] Prompt Sentence Examples

[2026] "Please explain the procedure for analyzing user emotion data and providing appropriate responses during a disaster."

[2027] As described above, this invention is a system that combines various technologies to realize real-time disaster countermeasures and minimize damage. Implementing this system is expected to significantly improve disaster preparedness and response, protecting many lives and property.

[2028] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2029] Real-time collection and analysis of weather and sensor data

[2030] Step 1: Collect data

[2031] server

[2032] Input: Weather data from weather satellites and observation stations, sensor data such as seismometers and river level gauges.

[2033] Specific operation: The server uses a Python script to periodically execute API requests to obtain weather data and various sensor data.

[2034] Output: The retrieved data.

[2035] Step 2: Store the data in the database

[2036] server

[2037] Input: Collected weather and sensor data.

[2038] Specific operation: The server connects to a PostgreSQL database and stores data in chronological order.

[2039] Output: Database entries organized chronologically.

[2040] Step 3: Analyze the data

[2041] server

[2042] Input: Weather and sensor data stored in a database.

[2043] How it works: The server uses TensorFlow to analyze the collected data and apply machine learning models to detect anomalous patterns.

[2044] Output: Analysis results (detection of abnormal patterns).

[2045] Detecting abnormal patterns and issuing alerts

[2046] Step 4: Detecting abnormal patterns

[2047] server

[2048] Input: Analysis results (detection of abnormal patterns).

[2049] What it does: The server verifies the analysis results and flags any abnormal patterns that could be a sign of disaster.

[2050] Output: Disaster warning flag.

[2051] Step 5: Generate a warning message

[2052] server

[2053] Input: Disaster omen flag.

[2054] Specific operation: The server uses the warning message template to generate a customized warning message.

[2055] Output: A warning message.

[2056] Step 6: Sending a warning message

[2057] server

[2058] Input: Warning message.

[2059] Specific operation: The server uses the Twilio API to send a warning message to the specified device or relevant organization.

[2060] Output: The warning message sent.

[2061] Optimizing evacuation routes

[2062] Step 7: Obtaining User Location

[2063] Terminal

[2064] Input: GPS data from your smartphone.

[2065] Specific operation: The device periodically obtains its current location information and sends it to the server.

[2066] Output: The location sent.

[2067] Step 8: Acquiring real-time traffic data

[2068] server

[2069] Input: Traffic data from Google Maps API and Waze API.

[2070] Specific operation: The server executes an API request to obtain the latest traffic data and road information.

[2071] Output: Latest traffic data.

[2072] Step 9: Calculate evacuation routes

[2073] server

[2074] Input: User location and real-time traffic data.

[2075] Specific operation: Based on the acquired data, the optimal evacuation route is calculated using the Dijkstra algorithm or the A algorithm.

[2076] Output: Optimal evacuation route.

[2077] Step 10: Send and display route information

[2078] Terminal

[2079] Input: The optimal evacuation route sent by the server.

[2080] Specific operation: The device displays the received evacuation route information to the user. Specifically, a dedicated app displays the route on a map.

[2081] Output: Evacuation route displayed on the screen.

[2082] Damage prediction and resource allocation optimization

[2083] Step 11: Collect historical disaster data and current weather data

[2084] server

[2085] Input: Historical disaster data and current weather data.

[2086] Specific operation: The server retrieves past disaster data from an existing database and collects current weather data through an API.

[2087] Output: Collected data.

[2088] Step 12: Damage prediction analysis

[2089] server

[2090] Input: Historical disaster data and current weather data.

[2091] Specific operation: The server uses a generative AI model (such as OpenAI) to analyze damage predictions.

[2092] Output: Damage prediction results.

[2093] Step 13: Create a resource allocation plan

[2094] server

[2095] Input: Damage prediction results.

[2096] Specific operation: The server creates a list of needed relief supplies and medical resources and generates a resource allocation plan.

[2097] Output: Resource allocation plan.

[2098] Step 14: Notify appropriate authorities

[2099] server

[2100] Input: Resource Allocation Plan.

[2101] Specific operation: The server sends the resource allocation plan to the relevant institutions.

[2102] Output: The submitted resource allocation plan.

[2103] Strengthening communication

[2104] Step 15: Getting User Input

[2105] User

[2106] Input: Situation in the affected area and request for help (voice or text).

[2107] Specific operation: The user inputs information using a smartphone app.

[2108] Output: Data sent to the server.

[2109] Step 16: Natural Language Processing and Machine Translation

[2110] server

[2111] Input: Data entered by the user.

[2112] What it does: The server uses the Google Cloud Natural Language API to parse the data and perform machine translation if necessary.

[2113] Output: The translated data.

[2114] Step 17: Send to the appropriate authorities

[2115] server

[2116] Input: Translated data.

[2117] Specific operation: The server sends the data to the relevant organization.

[2118] Output: The data sent.

[2119] Utilizing AI-powered autonomous machines

[2120] Step 18: Controlling Autonomous Devices

[2121] server

[2122] Input: Operation instructions.

[2123] Specific operation: The server issues commands to control autonomous devices (drones, robots) equipped with AI technology.

[2124] Output: Control signals to autonomous devices.

[2125] Step 19: Data Collection

[2126] autonomous equipment

[2127] Input: Site condition data.

[2128] Specific operation: The autonomous device collects video and sensor data from the disaster site and transmits it to a server in real time.

[2129] Output: Data sent to the server.

[2130] Step 20: Data Analysis

[2131] server

[2132] Input: Transmitted data from autonomous devices.

[2133] Specific operation: The server analyzes the collected data and evaluates the situation on site in detail.

[2134] Output: Analysis results.

[2135] Step 21: Provide to relevant organizations

[2136] server

[2137] Input: Analysis results.

[2138] Specific operation: The server provides the analysis results to the relevant organizations.

[2139] Output:Notification to relevant authorities.

[2140] User Emotion Recognition

[2141] Step 22: Collect Emotional Data

[2142] Terminal

[2143] Input: User voice or text data.

[2144] What it does: The device uses Amazon Rekognition to collect user emotion data.

[2145] Output: Collected emotion data.

[2146] Step 23: Emotion Recognition

[2147] server

[2148] Input: Emotion data.

[2149] Specific operation: The server analyzes the collected emotional data and evaluates the user's mental state in real time.

[2150] Output: Emotion evaluation results.

[2151] Step 24: Collaborate with experts

[2152] server

[2153] Input: Emotion assessment results.

[2154] Specific behavior: The server uses the Twilio API to notify a professional (such as a counselor) as needed.

[2155] Output:Notification to experts.

[2156] Step 25: Optimizing Instructions and Warnings

[2157] server

[2158] Input: Emotion assessment results.

[2159] Specific operation: The server generates appropriate evacuation instructions and warning messages based on the user's mental state and sends them to the terminal.

[2160] Output: Customized instructions and warning messages to the user.

[2161] (Application example 2)

[2162] 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."

[2163] Conventional disaster response systems require a wide range of functions, including not only real-time data collection and analysis, but also the detection of abnormal patterns, the issuance of warnings, and the calculation of evacuation routes. However, these systems are limited in their functionality during disasters, when a rapid and accurate response is required. Their application to autonomous vehicles is particularly limited, and they often fail to function adequately. Furthermore, it is difficult to provide prompt and optimal evacuation instructions that take into account the situational awareness and emotional state of disaster victims, which is a major obstacle to safe evacuation.

[2164] The identification processing 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 and analyzing weather data and sensor data in real time, means for predicting future disaster risks based on past disaster data, means for detecting abnormal patterns and identifying precursors of disasters, means for automatically issuing warnings based on the identified precursors, means for calculating optimal evacuation routes and providing them to autonomously driven vehicles in real time, means for supporting information exchange between the disaster-stricken area and rescue teams, means for recognizing the user's emotions and promoting safe evacuation behavior, and means for calculating and providing optimal evacuation routes for autonomously driven vehicles in the event of a disaster. This enables autonomously driven vehicles to evacuate quickly and safely in the event of a disaster, and by instructing appropriate evacuation behavior according to the emotional state of disaster victims, it is possible to significantly improve the efficiency and safety of evacuation.

[2165] "Weather data" is a general term for information collected from meteorological satellites and ground observation stations, and includes data on weather, temperature, precipitation, wind speed, etc.

[2166] "Sensor data" is a general term for data collected in real time from seismometers, river water level gauges, and other environmental sensors.

[2167] "Historical disaster data" refers to a database of records and information about disasters that have occurred in the past, including details about the type, scale, and impact of such disasters.

[2168] An "abnormal pattern" refers to data that shows abnormal fluctuations or behavior outside the normal data range, and is detected as a sign of a disaster.

[2169] A "warning" is a warning message sent to users and relevant organizations when a sign of a disaster is detected.

[2170] The "optimal evacuation route" is a route calculated based on real-time data that will allow users to evacuate quickly and safely in the event of a disaster.

[2171] An "autonomous vehicle" is a vehicle that uses AI technology to move and drive autonomously, eliminating the need for a driver.

[2172] "Disaster area" refers to an area that has been affected by a disaster and is in need of assistance and rescue.

[2173] A "rescue team" refers to a specialized organization or group that carries out relief activities when a disaster occurs, including rescue teams and first aid medical teams.

[2174] "Emotion recognition means" refers to technologies and algorithms that analyze a user's voice or text to automatically determine their current emotional state.

[2175] A "drone" refers to an unmanned aircraft that can be remotely controlled and fly autonomously, and is used to collect information at disaster sites.

[2176] "Means to promote evacuation behavior" is a system that takes into account the user's emotional state and surrounding circumstances to instruct and guide the optimal evacuation method and behavior.

[2177] This invention is a disaster prevention system that utilizes AI, and its main functions are as follows:

[2178] Real-time collection and analysis of weather and sensor data

[2179] The server collects meteorological data from weather satellites and ground observation stations, and also acquires real-time sensor data from seismometers and river water level gauges. This data is stored in a database and analyzed by AI algorithms. For example, if heavy rain continues, a sudden rise in river water levels can be detected, allowing for early detection of the possibility of flooding.

[2180] Detecting abnormal patterns and issuing alerts

[2181] The server analyzes the collected weather and sensor data to detect any abnormal patterns. If this abnormal pattern is determined to be a sign of a disaster, a warning message is automatically generated and sent to the user's smartphone and relevant organizations. For example, if seismometer data detects a sign of a large earthquake, this warning will be sent to local residents.

[2182] Calculating and providing optimal evacuation routes

[2183] The device (user's smartphone) uses its GPS function to obtain current location information and sends it to the server. The server analyzes this current location information along with real-time traffic and road information to calculate the optimal evacuation route. This information is sent to the user's device, and the evacuation route is displayed. For example, in the event of a fire, the optimal evacuation route is provided in real time.

[2184] Damage prediction and resource allocation optimization

[2185] The server inputs past disaster data and current weather data into the AI ​​model to generate a damage scope and resource allocation plan, allowing relevant agencies to quickly prepare response measures. For example, if a typhoon is approaching, damage forecasts and a list of needed relief supplies are immediately provided.

[2186] Utilizing AI-enabled drones and robots

[2187] The server will control AI-equipped drones and robots to collect information on disaster sites in real time and provide the analysis results to relevant organizations. For example, after an earthquake, drones can collect detailed images of the affected area, enabling them to quickly provide information to rescue teams.

[2188] Introducing the Emotion Engine

[2189] The user's device is equipped with an emotion engine that recognizes emotions from the user's voice and text in real time. The server evaluates the user's emotional state and responds by contacting experts as necessary. The server also optimizes evacuation instructions and warnings based on the emotion data. For example, if the emotion engine detects that the user is panicking during a disaster, it will notify a counselor and send the user a message to calm them down.

[2190] Specific examples

[2191] For example, when a typhoon is approaching, the system will collect weather and sensor data, and calculate and provide the optimal evacuation route for autonomous vehicles in real time. Furthermore, if the emotion engine detects that the user in the car is in a state of panic, it can also send a reassuring message.

[2192] Prompt Sentence Examples

[2193] "Calculate the evacuation route for your current location and lead you to a safe location."

[2194] "Generate a damage forecast and a list of necessary relief supplies in case of a typhoon."

[2195] "When users are panicking, send them a reassuring message."

[2196] In this way, the AI-based disaster prevention system provides comprehensive support, from collecting and analyzing real-time data to providing optimal evacuation routes and responding based on the user's emotional state.

[2197] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2198] Step 1: Collect weather and sensor data

[2199] The server collects meteorological data from meteorological satellites and ground observation stations, and acquires real-time data from various sensors, such as seismometers and river water level gauges. The input data consists of meteorological information and sensor information, which are then stored in a database.

[2200] Step 2: Analyze data to detect abnormal patterns

[2201] The server analyzes the collected weather and sensor data using AI algorithms. The input is the latest weather and sensor data, and the output is the detection of abnormal patterns. If an abnormality is detected, an analysis is performed to determine whether it is a sign of a disaster.

[2202] Step 3: Automatically generate and issue warning messages

[2203] If the server determines that an abnormal pattern is a sign of a disaster, it automatically generates a warning message and sends it to a designated device (the user's smartphone or a relevant institution). The input is the abnormal pattern detection result, and the output is a warning message.

[2204] Step 4: Get the user's location

[2205] The device (user's smartphone) uses its GPS function to obtain the user's current location information and sends it to the server. The input is GPS data, and the output is the current location information.

[2206] Step 5: Calculate the optimal evacuation route

[2207] The server analyzes real-time traffic and road information based on the received current location information and calculates the optimal evacuation route. The input is current location information, traffic data, and road information, and the output is the optimal evacuation route.

[2208] Step 6: Provide evacuation routes

[2209] The server sends the calculated optimal evacuation route to the user's device and displays the route. The input is the optimal evacuation route information, and the output is the display of the evacuation route.

[2210] Step 7: Damage prediction and resource allocation planning

[2211] The server inputs past disaster data and current weather data into the AI ​​model to generate a damage extent and resource allocation plan. The input is past disaster data and weather data, and the output is a damage forecast and resource allocation plan.

[2212] Step 8: Drone and robot control and intelligence gathering

[2213] The server controls AI-equipped drones and robots and collects information on disaster sites in real time. The input is control instructions for the drones and robots, and the output is the collected on-site information.

[2214] Step 9: Recognizing user emotions with the emotion engine

[2215] The device's emotion engine recognizes emotions from the user's voice and text in real time. The input is the user's voice and text data, and the output is the emotion recognition result.

[2216] Step 10: Responding to a panic attack

[2217] If the server detects the user's emotional state (e.g., panic), it automatically contacts an expert and sends a calming message to the user. The input is the user's emotion recognition result, and the output is a notification to the expert and a message to the user.

[2218] 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.

[2219] 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.

[2220] 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.

[2221] [Fourth embodiment]

[2222] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2223] 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.

[2224] 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).

[2225] 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.

[2226] 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.

[2227] 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).

[2228] 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.

[2229] 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.

[2230] 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.

[2231] 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.

[2232] 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.

[2233] 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.

[2234] 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."

[2235] This invention is a disaster prevention system that utilizes AI, and its main functions are the real-time collection and analysis of meteorological and sensor data, analysis of past disaster data, detection and warning of abnormal patterns, optimization of evacuation routes, damage prediction and resource allocation, enhanced communication, and utilization of AI-equipped drones and robots. The specific operation of each function in the system of this invention will be explained below.

[2236] Real-time collection and analysis of weather and sensor data

[2237] server

[2238] The server periodically collects weather data from weather satellites and ground observation stations, as well as sensor data from seismometers, river level gauges, and other sources.

[2239] The collected data is stored in a database in real time and is then analyzed using AI algorithms.

[2240] The server can detect signs of disaster by linking weather data with sensor data.

[2241] example

[2242] During periods of heavy rain, the server collects data from various sensors and detects sudden rises in water levels. By analyzing this data, it is possible to identify potential floods early and take appropriate action.

[2243] Analysis of past disaster data and disaster risk prediction

[2244] server

[2245] The server collects data from past disasters, inputs it into an AI model, and performs analysis.

[2246] The AI ​​model predicts future disaster risks based on data such as weather patterns, damage, and relief efforts.

[2247] Based on the forecast data, we identify areas where disasters may occur and the scale of the damage.

[2248] example

[2249] When predicting the path of a hurricane, the server analyzes current weather data based on past hurricane data to predict the possible extent and intensity of damage, which can be useful for making necessary preparations in advance.

[2250] Detecting abnormal patterns and issuing alerts

[2251] server

[2252] The server analyzes normal weather and sensor data to detect any unusual patterns.

[2253] Based on the identified abnormal patterns, an alert is generated if they are deemed to be a sign of a disaster.

[2254] The warning will be sent to a designated device (smartphone, PC) and simultaneously sent to relevant agencies.

[2255] example

[2256] Servers monitoring seismometer data detect the signs of an earthquake that is building up in size and send warnings to local residents' smartphones.

[2257] Optimizing evacuation routes

[2258] Terminal

[2259] The device (user's smartphone) uses GPS to send current location information to the server.

[2260] The server analyzes traffic conditions, road closure information, and real-time environmental conditions to calculate the optimal evacuation route.

[2261] The terminal provides the user with the evacuation route received from the server, assisting in evacuation in the event of an emergency.

[2262] example

[2263] In the event of a fire, users can launch the app on their smartphones and get real-time information on the optimal evacuation route. If the route is blocked, the server will calculate and provide a new route.

[2264] Damage prediction and resource allocation optimization

[2265] server

[2266] The server uses the collected data and past disaster data to predict the extent of damage and the impact on each region.

[2267] Based on the forecast data, we calculate the amount of relief supplies and medical resources needed and create a plan for efficient allocation.

[2268] Resource allocation plans will be shared with relevant agencies, enabling rapid response.

[2269] example

[2270] When a major typhoon is approaching, the server creates a list of predicted affected areas and needed relief supplies, and automatically sends it to the relevant organizations, allowing for faster preparations.

[2271] Strengthening communication

[2272] User

[2273] Users can use a smartphone app to input information about the damage situation and requests for help in natural language.

[2274] The server analyzes the entered information, translates it appropriately, and automatically sends it to the relevant organizations.

[2275] example

[2276] If a victim requests help from a rescue team in Japanese, they can input their voice into the app, and the server will translate the message into English and quickly transmit the information to rescue teams dispatched from overseas.

[2277] Utilizing AI-equipped drones and robots

[2278] server

[2279] The server dispatches AI-equipped drones and robots to disaster-stricken areas and collects video and sensor information in real time.

[2280] The collected data is analyzed on a server and provided to relevant organizations.

[2281] example

[2282] After an earthquake occurs, the server uses drones to collect detailed images of the affected area and transmits the analysis results to rescue teams in real time, enabling them to make quick decisions and take action.

[2283] As described above, this invention is a system that combines various technologies to enable real-time disaster countermeasures and minimize damage. Implementing this system is expected to significantly improve disaster preparedness and response, protecting many lives and property.

[2284] The processing flow will be explained below.

[2285] 1. Early Warning System

[2286] server

[2287] Step 1:

[2288] The server collects weather data from weather satellites and ground observation stations.

[2289] Similarly, sensor data is collected in real time from seismometers, river water level gauges, and other sources.

[2290] Step 2:

[2291] The collected weather data and sensor data are stored in a database.

[2292] The stored data is fed into AI algorithms to detect abnormal patterns.

[2293] Step 3:

[2294] AI algorithms analyze the data and detect unusual patterns and fluctuations.

[2295] Based on the detected abnormal data, it is determined whether it is a sign of a disaster.

[2296] Step 4:

[2297] If it is determined to be a sign of a disaster, a warning message will be generated.

[2298] This warning message is sent to designated terminals and relevant organizations.

[2299] 2. Evacuation route optimization

[2300] Terminal

[2301] Step 1:

[2302] The user's device (smartphone) obtains current location information using the GPS function.

[2303] The current location information is periodically sent to the server.

[2304] server

[2305] Step 2:

[2306] The server analyzes real-time traffic and road information based on the received current location information.

[2307] Uses AI algorithms to calculate optimal evacuation routes.

[2308] Terminal

[2309] Step 3:

[2310] The optimal evacuation route information is sent to the user's terminal.

[2311] Evacuation route information is displayed on the device app and provided to the user.

[2312] Step 4:

[2313] If roads are closed during an evacuation, the server recalculates a new route and sends updates to the device.

[2314] 3. Damage prediction and risk analysis

[2315] server

[2316] Step 1:

[2317] The server collects past disaster data and current weather data and inputs it into the AI ​​model.

[2318] The AI ​​model predicts the extent and impact of disasters based on past data.

[2319] Step 2:

[2320] Based on the prediction results, a risk assessment is carried out.

[2321] Based on the results of the risk assessment, identify the amount of resources needed (relief supplies, medical resources, etc.).

[2322] Step 3:

[2323] Prepare a resource allocation plan and provide it to relevant agencies and users.

[2324] 4. Strengthening communication

[2325] User

[2326] Step 1:

[2327] Users enter disaster situations and requests for assistance into the app.

[2328] Input methods include voice input and text input.

[2329] server

[2330] Step 2:

[2331] The server feeds the entered information into natural language processing and machine translation algorithms.

[2332] Carry out translation between different languages.

[2333] Step 3:

[2334] Translated information will be sent to rescue teams and relevant agencies.

[2335] 5. Use of drones and robots

[2336] server

[2337] Step 1:

[2338] The server will control AI-equipped drones and robots and begin collecting information from the disaster site.

[2339] The drone follows a set flight path and collects data in real time.

[2340] Step 2:

[2341] The server analyzes the collected data in real time to grasp the detailed situation at the disaster site.

[2342] If necessary, the drone or robot's movements will be adjusted to gather more detailed information.

[2343] Step 3:

[2344] The analysis results will be provided to relevant agencies and rescue teams to assist in rapid response.

[2345] 6. Demand forecasting and resource allocation

[2346] server

[2347] Step 1:

[2348] The server inputs past disaster data and current situation data into the AI ​​model.

[2349] AI models predict demand (food, water, medical supplies, etc.) after a disaster occurs.

[2350] Step 2:

[2351] Calculate the amount of relief supplies and medical resources needed based on forecast data.

[2352] Develop distribution plans and support efficient relief efforts.

[2353] Step 3:

[2354] The allocation plan will be provided to relevant organizations and users to ensure appropriate resource allocation.

[2355] Based on these steps, the system of the present invention can realize rapid and effective countermeasures against natural disasters, and can save many lives and properties.

[2356] Example 1

[2357] 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."

[2358] In recent years, the frequency of natural disasters has increased, and the resulting damage has become more severe. This has created a need for systems that can quickly and accurately predict disaster risks and minimize damage. However, current disaster response systems lack the precision to collect and analyze real-time data, and are lacking in the ability to detect abnormal situations early and issue warnings. Furthermore, improvements are needed to optimize evacuation routes and improve the efficiency of damage prediction and resource allocation. Furthermore, there is a lack of means to facilitate communication between disaster-stricken areas and rescue teams.

[2359] 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.

[2360] In this invention, the server includes means for collecting and analyzing meteorological information and environmental monitoring data in real time, means for predicting future disaster risks based on past disaster information, means for detecting abnormal patterns and identifying signs of disaster, means for automatically issuing warnings based on the identified signs, means for calculating optimal evacuation routes and providing them to users in real time, means for supporting information exchange between the disaster-stricken area and rescue teams, means for providing optimal evacuation routes using current location information from the terminal, means for analyzing collected data with an AI algorithm and detecting abnormal patterns, and means for analyzing past data using a generative AI model to predict disaster risks.This enables early detection of disaster risks in real time, rapid issuance of warnings, provision of efficient evacuation routes, optimal resource allocation, and smooth communication between the disaster-stricken area and rescue teams.

[2361] "Weather information" refers to data such as temperature, humidity, wind speed, and precipitation obtained from meteorological satellites and ground observation stations.

[2362] "Environmental monitoring data" refers to data used to monitor environmental changes obtained from sensors such as seismometers and river water level gauges.

[2363] "Past disaster information" refers to data on natural disasters that have occurred in the past, including details of damage, weather patterns, and relief efforts.

[2364] "Disaster risk" indicates the possibility of a disaster occurring in a particular area in the future and the magnitude of its impact.

[2365] "Abnormal patterns" refer to fluctuations in data that differ from normal weather data or sensor data and are thought to be a sign of a disaster.

[2366] A "warning" is information that notifies that a disaster is expected to occur based on a detected abnormal pattern.

[2367] An "evacuation route" refers to a route for safe evacuation in the event of a disaster, and is calculated based on real-time traffic and environmental conditions.

[2368] "User" refers to an individual or organization that uses the disaster response system to receive data and carry out evacuation and countermeasures.

[2369] "Relief supplies" refers to food, water, medicine and other basic necessities provided to disaster victims in the event of a disaster.

[2370] "Medical resources" is a general term for medical equipment, medicines, medical staff, etc. needed for disaster response.

[2371] A "generative AI model" is a type of machine learning algorithm that learns from past disaster data and predicts future disaster risks.

[2372] A "terminal" is a device that a user uses to receive disaster information and evacuation routes in real time, such as a smartphone or PC.

[2373] The present invention is a disaster prevention system that utilizes AI, and its configuration and implementation method will be described in detail below.

[2374] Real-time collection and analysis of meteorological and environmental monitoring data

[2375] server

[2376] The server periodically retrieves weather information from weather satellites and ground observation stations via an API, using scripts written in Python and a database management system (e.g., PostgreSQL).

[2377] The server also receives environmental monitoring data from seismometers and river level gauges, which are collected using IoT platforms such as AWS IoT.

[2378] The collected data is stored in a database in real time and analyzed using AI algorithms (e.g., TensorFlow).

[2379] Specific examples

[2380] For example, during periods of heavy rain, the server can collect data from water level sensors and detect sudden rises in water levels. By analyzing this data, flood risks can be detected early and appropriate warnings can be issued.

[2381] Analysis of past disaster information and prediction of future disaster risks

[2382] server

[2383] The server collects past disaster data and inputs it into a generative AI model (e.g., GPT-4) for analysis.

[2384] The AI ​​model predicts future disaster risks based on data such as weather patterns, damage, and relief efforts.

[2385] The prediction results are stored in a database and notified to relevant agencies.

[2386] Specific examples

[2387] The server inputs past hurricane data into a generative AI model for analysis, and then predicts the hurricane's path and the extent of the damage it will cause based on current weather data. This information is then sent to relevant organizations to help with advance preparations.

[2388] Detecting abnormal patterns and raising alerts

[2389] server

[2390] The server analyzes weather and environmental monitoring data using AI algorithms to detect abnormal patterns.

[2391] If an anomaly is detected, an alert is generated and notified to the specified device, using Firebase Cloud Messaging and Twilio APIs.

[2392] Specific examples

[2393] A server that monitors data from the seismometer detects abnormal tremors that could signal a major earthquake and immediately sends a warning to the smartphones of local residents.

[2394] Providing optimal evacuation routes

[2395] Terminal

[2396] The user's smartphone uses GPS to send current location information to the server.

[2397] The server takes into account traffic conditions and road closures to calculate the best evacuation route, using the Google Maps API and OpenStreetMap API.

[2398] The terminal provides the user with the evacuation route information received from the server, displays a map, and performs real-time navigation.

[2399] Specific examples

[2400] When a fire occurs, the user launches the app on their smartphone, and the server calculates the optimal evacuation route, taking into account traffic congestion information and closed roads, and provides it to the user in real time.

[2401] Damage prediction and resource allocation optimization

[2402] server

[2403] The server analyzes the collected data and past disaster data to predict the extent of damage and the impact on each region.

[2404] Based on the forecast data, the amount of relief supplies and medical resources needed is calculated and an efficient distribution plan is developed.

[2405] The allocation plan will be communicated to relevant agencies to assist in rapid response.

[2406] Specific examples

[2407] When a major typhoon is approaching, the server creates a list of predicted affected areas and needed relief supplies, and notifies relevant agencies to help them make prompt preparations.

[2408] Strengthening information exchange between affected areas and relief teams

[2409] User

[2410] Users use a smartphone app to input information about the damage situation and requests for help in natural language.

[2411] The server analyzes the input information, translates it appropriately, and notifies the relevant authorities. Generative AI models (e.g., GPT-4) are used for natural language processing.

[2412] Specific examples

[2413] When a victim types in Japanese, "Help me, my house has collapsed," the server translates this into English and quickly notifies rescue teams.

[2414] Prompt Sentence Examples

[2415] "Based on past hurricane data, analyze current weather data and predict future hurricane risk."

[2416] "Please provide sample code for an algorithm that analyzes collected sensor information and meteorological data to detect flood risk early."

[2417] "Please show us how to translate disaster information entered in Japanese into English and notify the relief team."

[2418] In this way, the present invention is a system that uses multi-layered technologies to enable real-time disaster countermeasures and minimize damage. Implementing this system is expected to significantly improve disaster preparedness and response, saving many lives and property.

[2419] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2420] Step 1: Data collection

[2421] server

[2422] The server periodically retrieves weather data from weather satellites and ground observation stations using APIs. It also receives environmental monitoring data such as seismometers and river water level gauges. This process uses scripts written in Python and a database management system (e.g., PostgreSQL). The input is raw data retrieved from the API, and the output is data stored in a database in an organized format.

[2423] Specific actions

[2424] The server receives data from weather satellites every 30 minutes and inserts it into a database in JSON format. It also receives data from seismometers and analyzes it in real time.

[2425] Step 2: Data storage and processing

[2426] server

[2427] The collected meteorological and environmental monitoring data is stored in a database in real time. This data is then immediately analyzed by AI algorithms using Python (e.g., TensorFlow). The input is the raw data points, and the output is the analyzed indicators and abnormal pattern detection results.

[2428] Specific actions

[2429] The server periodically reads the weather and sensor data stored in the database and analyzes it using a TensorFlow model. Any abnormal patterns detected are stored in a separate table and sent to the warning system.

[2430] Step 3: Detect and alert on abnormal patterns

[2431] server

[2432] The server uses AI algorithms to detect abnormal patterns, which inv...

Claims

1. a means for collecting and analyzing meteorological and sensor data in real time; A means of predicting future disaster risks based on past disaster data, A means of detecting abnormal patterns and identifying precursors to disaster; means for automatically issuing alerts based on the identified precursors; and A means of calculating optimal evacuation routes and providing them to users in real time; A system that includes means to support the exchange of information between disaster-stricken areas and relief teams.

2. Controlling AI-equipped drones and robots, collecting information on disaster sites in real time, The system of claim 1 further comprising a means for analyzing.

3. Based on the collected and analyzed data, we predict the extent of damage and risks, 10. The system of claim 1, further comprising means for systematically allocating needed relief supplies and medical resources.

4. 10. The system of claim 1, further comprising means for performing natural language processing and translation between different languages ​​to enhance communication between disaster-stricken areas and relief teams.

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