system

Abstract

Provide a system. 【Solution means】 Means for receiving data from a satellite observation device and a sensor device, Means for preprocessing the received data to complement deficiencies and remove noise, Means for analyzing the preprocessed data to detect abnormal patterns, Means for aggregating the analysis results in a database and updating them in real time on the cloud, Means for notifying warning information to a terminal device, Means for automatically generating a support plan based on the analysis data and transmitting it to relevant institutions, Means for receiving feedback obtained from on-site response and improving the analysis accuracy, A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] The present invention relates to a technology for achieving rapid and accurate information sharing and smooth support activities in the event of natural disasters, and aims to provide a system that realizes the provision of efficient support measures based on real - time collection and analysis of disaster information. Also, it is required to continuously improve the analysis accuracy by incorporating feedback based on the actual local situation.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides means for receiving data from satellite observation devices and sensor devices, means for preprocessing the received data to fill in missing data and remove noise, and means for analyzing the preprocessed data to detect abnormal patterns. Furthermore, it provides means for aggregating the analysis results in a database and updating it in real time on the cloud to notify terminal devices of warning information, and for automatically generating support plans based on the analysis data and transmitting them to relevant organizations. In addition, it includes means for receiving feedback obtained from on-site responses and improving the accuracy of the analysis, thereby constituting a system that enables efficient disaster response.

[0006] A "satellite observation device" is a device that uses artificial satellites orbiting the Earth to observe the Earth's surface and acquire various data, including signs of natural disasters.

[0007] A "sensor device" is a device installed to detect physical and environmental changes and collect data from them, and includes seismometers, weather sensors, and the like.

[0008] "Preprocessing data" refers to the process of preparing received raw data to make it analyzable, which involves tasks such as data interpolation and noise reduction.

[0009] An "anomalous pattern" is a data trend that shows unusual changes that deviate from normal data patterns, and may indicate an impending disaster.

[0010] A "database" is an information system that systematically stores and manages aggregated information, making it quickly accessible as needed.

[0011] "Cloud computing" refers to a form of computing resources provided via the internet, enabling the remote management and use of data and programs.

[0012] A "terminal device" refers to a device used by a user to receive or manipulate information, such as a smartphone or computer.

[0013] "Warning information" refers to notifications regarding the occurrence of disasters or risks, including content that urges users and related organizations to be vigilant.

[0014] A "support plan" is a plan for providing support quickly and effectively during a disaster, including the provision of supplies and arrangements for personnel assistance.

[0015] "Feedback" refers to a series of information provision activities where reports and information from the field are re-entered into the system to help improve the accuracy of the system's analysis. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a system for enabling rapid and accurate information gathering and support activities in the event of a natural disaster. Specific embodiments are described below.

[0038] This system is primarily composed of the following elements: "server," "terminal," and "user."

[0039] Server roles and processing

[0040] The server is responsible for receiving various data from satellite observation and sensor devices. The received data relates to natural phenomena such as crustal deformation, climate change, and sea level information. This data is processed to prepare it for analysis from its raw state, undergoing noise reduction and data completion.

[0041] Subsequently, the server processes the data using AI analysis algorithms to detect anomaly patterns. For example, if an anomaly is detected in crustal deformation data, the server evaluates the possibility that the anomaly is a precursor to an earthquake and predicts the disaster risk.

[0042] The analysis results are aggregated in a database and updated in real time. This updated information is uploaded to the cloud and made accessible to relevant organizations and users. The server also automatically generates efficient support plans based on this information and provides them to the relevant organizations. This ensures optimal distribution of supplies and expedites support activities.

[0043] Terminal role

[0044] The terminal receives alarm information transmitted from the server and notifies the user of that information. The user can then check these alarms via their smartphone or computer and take emergency action as needed. In this way, the terminal plays a crucial role in supporting on-site responses.

[0045] User roles and processes

[0046] When users conduct on-site support activities, they send feedback from the field to the server. For example, they might report the actual extent of damage or the shortage of supplies via their devices. This feedback information becomes important data for improving the accuracy of the server's analysis.

[0047] Thus, the present invention provides a specific system configuration for achieving an efficient response to natural disasters.

[0048] The following describes the processing flow.

[0049] Step 1:

[0050] The server periodically receives data from satellite observation and sensor devices. Specifically, it acquires data including crustal deformation data, climate data, and sea level information.

[0051] Step 2:

[0052] The server preprocesses the received raw data. Specifically, it performs processes to fill in missing data and remove noise, preparing the data for analysis.

[0053] Step 3:

[0054] The server feeds pre-processed data into an AI analysis algorithm to detect anomalous patterns and signs of disaster. This analysis uses a machine learning model based on historical data, and if an anomaly is detected, its location and the risk level of its occurrence are evaluated.

[0055] Step 4:

[0056] The server aggregates the analysis results into a database and updates it to the cloud in real time. This information will be made available to relevant organizations and users through access.

[0057] Step 5:

[0058] The device receives alarm information transmitted from the server and sends emergency notifications to the user. The device uses push notifications to deliver warnings such as earthquakes and floods to the user, prompting them to take prompt action.

[0059] Step 6:

[0060] The server automatically generates optimal support plans based on the analysis data and provides them to relevant aid organizations and agencies. These plans include distribution routes for supplies and the placement of shelters.

[0061] Step 7:

[0062] Users provide feedback to the server based on information gathered through on-site situation assessments and support activities. This feedback is reported via smartphones and other devices and is used to continuously improve the system and enhance the accuracy of the analysis.

[0063] (Example 1)

[0064] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0065] Conventional natural disaster response systems suffered from delays in processing data after reception, making it difficult to quickly generate effective support plans. Furthermore, they were unable to adequately detect anomalies such as crustal movements and weather changes in real time and conduct risk assessments, resulting in delays in immediate response during disasters. In addition, there was a lack of mechanisms to effectively incorporate feedback from the field to improve analysis accuracy.

[0066] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0067] In this invention, the server includes means for receiving data from observation equipment, means for preprocessing the received data to fill in missing data and remove noise, and means for analyzing the preprocessed data to detect abnormal patterns. This makes it possible to efficiently and quickly perform everything from data reception and analysis to alarm notification and support plan generation. Furthermore, by utilizing on-site feedback to continuously improve analysis accuracy, more appropriate disaster response can be achieved.

[0068] "Observation equipment" refers to devices used to monitor natural phenomena and collect data on them. Examples include satellites, seismometers, and weather sensors.

[0069] "Preprocessing" refers to the process of preparing raw data for analysis by removing noise and imputing missing information.

[0070] An "anomalous pattern" refers to a portion of the data being analyzed that exhibits unusual movements or changes that deviate from the normal range.

[0071] "Risk assessment" involves analyzing the likelihood that detected abnormal patterns could lead to disasters and quantifying that likelihood.

[0072] An "information repository" refers to a database used to collect and store data within a system.

[0073] An "information network" refers to the entire network of communications used to share data with users and related organizations.

[0074] A "mobile communication terminal" is a device that receives and allows users to view mobile information, and includes smartphones and tablets.

[0075] A "support plan" refers to a plan generated based on analytical data to efficiently carry out actions such as the distribution of supplies and the dispatch of personnel in the event of a disaster.

[0076] "Feedback" refers to information that reports on reactions and areas for improvement, such as the situation on the ground and the results of support activities.

[0077] "Geospatial technology" refers to technologies for processing and analyzing geographical information, including mapmaking and spatial analysis.

[0078] This system invention has a complex configuration including observation equipment, servers, terminals, and users, in order to realize rapid and accurate information gathering and support activities in response to natural disasters.

[0079] The server functions as a central integrator, receiving and efficiently processing data from observation instruments. These instruments include satellites, seismometers, weather sensors, and river level gauges, from which data on natural phenomena such as crustal deformation, weather patterns, and water levels are acquired. The received data is preprocessed using dedicated analysis software. This software utilizes noise reduction algorithms and missing data imputation techniques to improve data accuracy. Furthermore, by integrating AI analysis algorithms, it detects anomalous patterns and assesses disaster risk. The analysis results are aggregated in a database and then distributed in real time to relevant organizations and users via a cloud-based information network.

[0080] The terminal functions as a means of providing users with alert information. Users can receive alerts via smartphones or computers, enabling them to respond quickly. Along with alert notifications, the system automatically generates support plans based on analysis results and sends them to relevant organizations, thereby streamlining support activities such as supply distribution and evacuation guidance.

[0081] Users are responsible for transmitting feedback from the field to the server via their devices. This includes detailed reports on support activities carried out on-site and up-to-date information on the extent of the damage, which improves the accuracy of the server's analysis.

[0082] As a concrete example, when a typhoon occurs, the server analyzes data such as wind speed, precipitation, and trajectory obtained from observation equipment to predict areas that may be affected. Based on this information, swift evacuation orders are sent to residents via terminals. Furthermore, the generated support plan is used for distributing relief supplies and determining where to dispatch personnel.

[0083] Examples of prompt messages include specific instructions such as, "Analyze the data from the following sensors, identify anomaly patterns, and assess the risk level. Use the AI ​​model to generate the optimal support plan."

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The server receives data such as crustal deformation, weather patterns, and water level information from observation instruments. This data is raw observational data and requires preprocessing to improve accuracy. The input is raw data, and the output is data suitable for preprocessing.

[0087] Step 2:

[0088] The server preprocesses the received data. Specifically, it removes noise from the data using a denoising algorithm and estimates and fills in missing data using missing data imputation techniques. This process outputs clean data suitable for analysis.

[0089] Step 3:

[0090] The server inputs clean data into an AI analysis algorithm to detect anomalous patterns. If unusual movements are observed in the crustal deformation data, these movements are identified as anomalies, and the disaster risk is assessed. The output after analysis is the anomalous pattern and its risk level.

[0091] Step 4:

[0092] The server aggregates the analysis results into a database and transmits them in real time over the information network. This information effectively serves as a risk dashboard, making it immediately accessible to users and relevant organizations. The output exists as accessible real-time data.

[0093] Step 5:

[0094] The terminal receives alarm information sent from the server and notifies the user. For example, in the event of a potential earthquake, it immediately warns users living in a specific area. The input is alarm data, and the output is an alert notification to the user.

[0095] Step 6:

[0096] The server generates a support plan based on the analysis data and automatically transmits it to the relevant organizations. This support plan includes actionable guidelines regarding the distribution routes of supplies and the optimal allocation of resources. The output is a concrete and actionable support plan.

[0097] Step 7:

[0098] Users provide feedback to the server via their terminals, based on information gathered on-site. They send detailed reports to the server, including the local situation, the results of the support provided, and areas for improvement. This feedback is used for re-evaluation, promoting continuous improvement in analysis accuracy. The input is on-site feedback information, and the output is the improved analysis results.

[0099] (Application Example 1)

[0100] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0101] During natural disasters, rapid and accurate information gathering and support activities are crucial. However, conventional systems struggle to efficiently process large amounts of data, provide real-time information, and optimize support plans. In particular, there is a lack of safe and optimal route calculations for moving objects during disasters, posing a risk of delays in support activities. A system is needed to solve these problems and enable more effective disaster response.

[0102] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0103] In this invention, the server includes means for receiving data from satellite observation devices and sensor devices; means for preprocessing the received data to fill in gaps and remove noise; means for analyzing the preprocessed data and detecting abnormal patterns; means for aggregating the analysis results in a database and updating the information infrastructure in real time; means for notifying terminal devices of alarm information; means for automatically generating support plans based on the analysis data and transmitting them to relevant organizations; means for receiving return information obtained from on-site responses and improving analysis accuracy; and means for optimizing the trajectory of moving objects and automatically calculating safe routes based on obstacle information. This enables accurate information provision in real time and safe delivery of support supplies.

[0104] A "satellite observation device" is a device installed on an artificial satellite orbiting the Earth that observes data on the Earth's surface and weather, and transmits it to the ground as a signal.

[0105] A "sensor device" is a device used to measure physical phenomena and environmental conditions and acquire corresponding data. For example, it is used to detect crustal movements and changes in weather.

[0106] "Preprocessing" refers to the process of preparing raw observational data into a format suitable for analysis, and includes tasks such as imputing missing data and removing noise.

[0107] "Analysis" refers to data processing performed on received and pre-processed data to detect specific patterns or anomalies.

[0108] A "database" is a system that centrally manages structured information and allows for rapid access and updating.

[0109] "Information infrastructure" refers to the fundamental systems and networks for processing and utilizing data safely and quickly.

[0110] "Warning information" refers to important information regarding disasters or anomalies that is sent to devices to encourage a quick response.

[0111] A "support plan" is a plan that automatically generates action guidelines and resource allocations for appropriate disaster response.

[0112] "Related organizations" refers to various groups and institutions involved in disaster response and support activities, with the aim of ensuring effective coordination among them.

[0113] "On-site response" refers to the specific actions taken at actual disaster sites, and the related feedback information is used to improve overall accuracy.

[0114] "Return information" refers to performance data and feedback obtained from the field, and this information is used to improve the accuracy of the system.

[0115] A "mobile vehicle" refers to an unmanned or manned vehicle used for transporting supplies or gathering information during a disaster.

[0116] A "safe route" is the safest and most efficient path that a moving object should take, taking into account various environments and circumstances.

[0117] "Track optimization" refers to the process of optimizing the route plan so that a moving object can reach its destination efficiently and safely.

[0118] The system of this invention consists of multiple devices and programs designed to quickly and accurately collect information during natural disasters and to streamline support activities. At the heart of the system is a server that processes data received from satellite observation and sensor devices. The server uses specialized software to preprocess large amounts of raw data, filling in gaps and removing noise. Next, the preprocessed data is analyzed using AI analysis algorithms to detect anomaly patterns. Machine learning platforms such as TENSORFLOW® are used in this process.

[0119] The analyzed data is aggregated in real time in a database on the information infrastructure, and alert information is automatically sent to the relevant organizations and institutions. Terminals receive this alert information and notify the user. Users send feedback to the server as information upon their return from the field, contributing to improving the accuracy of the system's analysis.

[0120] Furthermore, in the operation of the mobile vehicles, the server utilizes algorithms that incorporate obstacle information and geographical information to optimize safe and efficient routes. This enables autonomous vehicles to safely deliver supplies during disasters.

[0121] As a concrete example, in the delivery of relief supplies during a disaster, the system calculates the optimal route considering road closure information and weather conditions. Autonomous vehicles then efficiently deliver supplies based on this route, accelerating relief efforts. An example of a prompt using a generative AI model is: "Calculate the latest route to the disaster area and optimize safe and efficient supply delivery. Consider current location and traffic information, and suggest a route that avoids obstacles."

[0122] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0123] Step 1:

[0124] The server receives raw data, including crustal deformation, weather, and water level information, from satellite observation and sensor devices. The input is the raw data, and the output is the pre-processed result of the data. In this step, the server performs noise reduction and missing data imputation to prepare the data for analysis. Specifically, noise is removed using a filtering algorithm, and missing values ​​are filled in by an imputation algorithm.

[0125] Step 2:

[0126] The server processes pre-processed data through an AI analysis algorithm to detect anomalous patterns. The input is pre-processed data, and the output is the analysis result of the anomalous patterns. The server uses machine learning models to analyze anomalous trends within the data and assess potential disaster risks. In particular, it utilizes TensorFlow to predict earthquake and flood precursors.

[0127] Step 3:

[0128] The server aggregates the analysis results into a database on the information infrastructure and updates them in real time via a cloud service. The input is the analysis results, and the output is notification information for relevant organizations. The analysis results are immediately uploaded to the cloud and made available for relevant organizations to access at any time. Specifically, the update work is performed using a database update API.

[0129] Step 4:

[0130] The terminal checks information in the cloud and notifies the user of important alerts. The input is alert information retrieved from the cloud, and the output is an alert notification to the user. The terminal uses a notification mechanism to send warnings to smartphones and dedicated devices. At this time, it is provided with a user-friendly interface so that the user can immediately understand the situation.

[0131] Step 5:

[0132] The server calculates the optimal route for the moving object based on the analysis data and obstacle information. The input for this step is the analysis data, and the output is the optimal route. The route calculation incorporates real-time map data through integration with a geographic information system, and also takes traffic conditions and weather into consideration. The calculated route is then transmitted to the autonomous vehicle.

[0133] Step 6:

[0134] The autonomous vehicle receives the optimal route transmitted from the server and safely delivers the relief supplies. The input is the optimal route information from the server, and the output is the result of the supplies delivery. Specifically, the automated control system accurately guides the vehicle to the target location, and sensors are used to avoid obstacles.

[0135] Step 7:

[0136] Users send feedback to the server regarding the local situation and any additional requests after the delivery of supplies. The input is information collected on-site, and the output is feedback information. Users submit feedback using a smart device with simple operations. This feedback is used to improve the accuracy of the system's analysis and is utilized in subsequent support activities.

[0137] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0138] This invention relates to a system for facilitating rapid information gathering and support activities during natural disasters, and further provides more personalized support by incorporating an emotion engine that recognizes the user's emotions.

[0139] The system is primarily composed of three elements: "server," "terminal," and "user."

[0140] Server roles and processing

[0141] The server receives crustal deformation data, climate data, water level information, etc., from satellite observation and sensor devices, and preprocesses this data. This processing includes denoising and imputing missing data. Subsequently, the preprocessed data is analyzed using an AI analysis algorithm to detect anomaly patterns.

[0142] The server aggregates the analysis results into a database and updates them to the cloud in real time. This data becomes immediately available to users through relevant organizations and devices, enabling the automated generation of efficient support plans. Crucially, this involves the use of an emotion engine. The server receives feedback from users and analyzes the text and audio data within that feedback using the emotion engine to understand the user's emotional state.

[0143] Based on the results of the emotion analysis, the server adjusts the support plan and provides the user with the most appropriate information. For example, if the user is experiencing high levels of stress, it can provide a more reassuring message.

[0144] Terminal role

[0145] The device receives alert information and optimized support plans transmitted from the server and notifies the user. Furthermore, it can provide content to promote relaxation based on the results of the emotion engine. This reduces the user's psychological burden and encourages a quick and appropriate response.

[0146] User roles and processes

[0147] Users use their devices to check the situation on-site and send feedback to the server as needed. This feedback can include written text or voice messages, which are analyzed in detail by an emotion engine. This allows for personalized support and enables rapid decision-making.

[0148] In this way, by incorporating an emotion engine, the system provides flexible and effective support that takes into account the user's emotional state.

[0149] The following describes the processing flow.

[0150] Step 1:

[0151] The server receives data in real time from satellite observation and sensor devices. This data includes information on crustal deformation, climate change, and sea level.

[0152] Step 2:

[0153] The server preprocesses the received data. Specifically, it fills in missing parts, removes noise, and prepares the data for analysis.

[0154] Step 3:

[0155] The server processes pre-processed data using an AI analysis algorithm to detect anomalous patterns. This analysis allows for the early identification of natural disaster risks.

[0156] Step 4:

[0157] The server aggregates the analysis results into a cloud-based database and updates it in real time. This makes the data accessible to relevant organizations and users.

[0158] Step 5:

[0159] The server receives feedback from users. This feedback can be sent in text or audio format and is analyzed by an emotion engine.

[0160] Step 6:

[0161] The server uses an emotion engine to recognize emotions from user feedback. The analysis results are used to understand the user's psychological state.

[0162] Step 7:

[0163] The server adjusts the support plan based on the emotion analysis results. For users experiencing high stress levels, messages and information designed to provide reassurance are generated.

[0164] Step 8:

[0165] The device notifies the user of alert information and optimized support plans sent from the server. Furthermore, it provides content that promotes relaxation based on the user's emotions.

[0166] Step 9:

[0167] Users review the information provided on their devices and take action as needed. User feedback is sent to the server in the next cycle to help the system make decisions.

[0168] (Example 2)

[0169] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0170] While there is a need for rapid and appropriate information gathering and smooth support activities during natural disasters, conventional systems have challenges in real-time data processing and flexible support based on user sentiment, resulting in a lack of measures tailored to individual needs.

[0171] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0172] In this invention, the server includes means for receiving data from observation and detection devices, means for preprocessing the received data to fill in missing data and remove noise, and means for analyzing the preprocessed data to detect abnormal patterns. This enables real-time, high-precision data analysis, and by utilizing an emotion analysis engine that analyzes user emotions, it becomes possible to provide flexible and effective support tailored to individual needs.

[0173] An "observation device" is a device installed on satellites or on the ground to quantitatively capture changes in the environment or the Earth's surface.

[0174] A "detection device" is a device that senses specific phenomena, physical quantities, or environmental changes and acquires data related to them.

[0175] "Preprocessing" refers to the process of preparing data for analysis by performing tasks such as noise reduction, data interpolation, and format conversion.

[0176] "Noise" refers to unwanted information contained in data that negatively impacts the analysis results.

[0177] An "anomalous pattern" is a data trend that deviates from normal data and exhibits unusual behavior or phenomena.

[0178] An "information management device" is a device or system for aggregating data and storing and organizing analysis results.

[0179] An "information processing infrastructure" is a platform for instantly managing, processing, and sharing data on the cloud or network.

[0180] A "computing device" is a device that electronically processes information and provides that information to the user.

[0181] A "support plan" is an action plan created to efficiently provide the necessary support for a specific situation.

[0182] An "emotion analysis engine" is a system that extracts emotional information from text and audio to analyze the user's emotional state.

[0183] "User emotional state" refers to the mental reactions and sensitivities that a user is experiencing.

[0184] The embodiment of the invention is a system that enables rapid and effective information gathering and support during natural disasters. This system is constructed using observation devices, detection devices, servers, terminals, and generative AI models.

[0185] The server acquires data in real time from observation and detection devices. The received data is preprocessed to remove noise and impute missing data. This preprocessing uses statistical methods and filtering techniques. After preprocessing is complete, the server analyzes the data using AI analysis algorithms to detect anomalous patterns. The AI ​​analysis algorithms used typically include machine learning or deep learning techniques.

[0186] The analysis results are aggregated in an information management device and updated in real time on the information processing infrastructure. The terminal functions as a computing device and notifies the user of alarms and optimized support plans transmitted from the server. Furthermore, it utilizes an emotion analysis engine to analyze user feedback and adjust the support plan based on the user's emotional state.

[0187] Users check the local situation through their devices and send feedback to the server in voice or text format. This feedback is further analyzed by an emotion analysis engine to provide optimal information tailored to the user's individual needs.

[0188] A concrete example is the rapid analysis of crustal deformation and climate data in specific areas during disasters, and the provision of evacuation orders based on that analysis. An example of a prompt for the generating AI model is, "Please show the data processing flow for creating an appropriate support plan during a disaster."

[0189] This system enables a rapid and appropriate response in specific disaster situations, improving user safety and peace of mind.

[0190] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0191] Step 1:

[0192] The server receives crustal deformation data, meteorological data, and water level information in real time from observation and detection devices. Input is raw data from each device, and output is a collection of raw data sets. The data is converted into a structured format and prepared for further processing.

[0193] Step 2:

[0194] The server preprocesses the received raw data. It applies noise reduction filtering to remove outliers. Furthermore, it uses statistical methods to impute missing data. The preprocessed data is generated as output and prepared for analysis. As a specific example, data lost due to sensor communication failures is supplemented with average values ​​from a surrounding database.

[0195] Step 3:

[0196] The server inputs pre-processed data into an AI analysis algorithm to detect anomalous patterns. The input is a clean dataset, and the output is a list of detected anomalous patterns. The detection process uses machine learning models and employs pattern recognition techniques. For example, it can automatically identify waveform patterns that indicate an impending earthquake.

[0197] Step 4:

[0198] The server aggregates the analysis results in the information management device and updates the data in the information processing infrastructure in real time. The input is a list of abnormal patterns, and the output is an updated database. This allows relevant organizations and terminals to access the new information immediately.

[0199] Step 5:

[0200] The terminal receives analysis results and support plans from the server. The input is optimization information sent from the server, and the output is alarms and support plans notified to the user. The terminal immediately notifies the user and, if necessary, alerts them with an alarm sound or vibration.

[0201] Step 6:

[0202] Users check the on-site situation and send feedback to the server via their terminal. Input is text or audio describing the on-site situation, and output is user feedback data. The feedback is transcribed into text using speech recognition technology and used for sentiment analysis.

[0203] Step 7:

[0204] The server analyzes user feedback using an emotion analysis engine to assess the user's emotional state. The input is text data of the feedback, and the output is an evaluation result indicating the emotional state. Based on these results, the support plan is adjusted to match the user's psychological state. For example, information that provides reassurance is added for users who are feeling anxious.

[0205] (Application Example 2)

[0206] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0207] In today's information society, the rapid and accurate provision of information during natural disasters is essential for social stability. However, conventional systems provide uniform information without considering the individual emotional state of users, making it difficult to provide support tailored to individual circumstances. Therefore, there is a need to develop systems that reduce the psychological burden on users during disasters and provide more accurate information.

[0208] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0209] In this invention, the server includes means for receiving data from satellite observation devices and sensor devices; means for preprocessing the received data to fill in missing data and remove noise; means for analyzing the preprocessed data and detecting abnormal patterns; means for aggregating the analysis results in a database and updating it in real time on the cloud; means for notifying terminal devices of alarm information; means for automatically generating support plans based on the analysis data and transmitting them to relevant organizations; means for receiving feedback obtained from on-site responses and improving analysis accuracy; and means for recognizing emotions using a personal display device and providing personalized services based on the analysis results. This enables the provision of rapid and personalized information and support in accordance with the user's emotional state.

[0210] A "satellite observation device" is a device installed on an artificial satellite orbiting the Earth to observe the conditions of the Earth's surface and atmosphere.

[0211] A "sensor device" is a device that detects physical phenomena or states and outputs them as digital data.

[0212] "Preprocessing" refers to steps such as data imputation and noise reduction performed to improve the accuracy of data analysis.

[0213] An "anomalous pattern" is a data pattern that shows unusual movements or states that differ from normal observation data.

[0214] A "database" is a structured collection of data used to efficiently store, search, and manage information.

[0215] "Real-time updating" is an update method in which information is instantly reflected in the database or cloud the moment it is generated.

[0216] A "terminal device" is a device used by users to receive and manipulate digital information.

[0217] "Warning information" refers to information that is sent out to quickly draw attention when danger or abnormality occurs.

[0218] A "support plan" is a plan for providing support and assistance in a specific situation efficiently and effectively.

[0219] "Feedback" is the act of returning a response or evaluation of the information or service received to the sender.

[0220] "Personalized service" refers to the provision of services that are customized according to each individual's characteristics and circumstances.

[0221] "Emotional recognition" means analyzing and interpreting the user's emotional state from their facial expressions and voice.

[0222] To implement this system, it is necessary to build a network environment that handles information between servers, terminals, and users.

[0223] The server receives surface and weather data acquired from satellite observation and sensor devices. This data is often raw and therefore unsuitable for direct analysis. First, preprocessing is performed to remove noise and fill in missing data. This improves the quality of the data and increases the accuracy of the analysis. The preprocessed data is then analyzed by AI analysis algorithms to attempt to detect anomaly patterns. If an anomaly is detected, the information is aggregated in a database in real time and immediately updated on the cloud.

[0224] The terminal receives alert information and support plans from the server and notifies the user. Personalized display devices used here include smart glasses. The terminal recognizes the user's emotions in real time and provides personalized services accordingly. For example, if the emotional state indicates the user is experiencing high stress, it can display content with a relaxing effect.

[0225] Users use information obtained through their devices to check the situation and send their opinions and impressions to the server through feedback. This feedback is analyzed by an emotion engine and used to improve the service and optimize further support plans. User feedback becomes a new data source, contributing to improved analysis accuracy.

[0226] As a concrete example, a server might send an instruction to a terminal saying, "Provide this customer with music to help them relax." An example of a prompt using a generative AI model would be, "Analyze the customer's facial expression data to determine if they are relaxed. If they are tense, tell me what you should suggest to them."

[0227] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0228] Step 1:

[0229] The server receives crustal deformation data, climate data, and sea level information from satellite observation and sensor devices. Raw data is acquired as input and preprocessed. By denoising the data and imputing missing parts, a clean dataset is output.

[0230] Step 2:

[0231] The server feeds pre-processed data into an AI analysis algorithm to detect anomaly patterns. The input is clean, pre-processed data, and the AI ​​model analyzes this data to output the anomaly detection results. A generative AI model is utilized in this process.

[0232] Step 3:

[0233] The server aggregates detected anomaly patterns into a database and updates it in real time on the cloud. The input is the result of anomaly detection, and aggregation into the database and cloud updates are performed to efficiently manage this data.

[0234] Step 4:

[0235] The terminal receives alarm information and support plans sent from the server and notifies the user. The input is alarm information and support plans from the server, and the output is in the form of a notification to the user. This allows the user to immediately obtain guidance on what to do.

[0236] Step 5:

[0237] The device recognizes the user's emotions and provides personalized services. Input consists of the user's facial expressions and voice data, which are analyzed by an emotion engine. Output includes relaxing music and messages. Prompts generated using a generative AI model select the most appropriate content based on the user's situation.

[0238] Step 6:

[0239] Users check the current status based on information obtained through their devices and send feedback to the server. The input consists of services and information from the device, which are returned to the server as feedback. This feedback is then analyzed by the server and contributes to improving the support plan.

[0240] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0241] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0242] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0243] [Second Embodiment]

[0244] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0245] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0246] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0247] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0248] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0249] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0250] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0251] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0252] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0253] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0254] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0255] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0256] This invention is a system for enabling rapid and accurate information gathering and support activities in the event of a natural disaster. Specific embodiments are described below.

[0257] This system is primarily composed of the following elements: "server," "terminal," and "user."

[0258] Server roles and processing

[0259] The server is responsible for receiving various data from satellite observation and sensor devices. The received data relates to natural phenomena such as crustal deformation, climate change, and sea level information. This data is processed to prepare it for analysis from its raw state, undergoing noise reduction and data completion.

[0260] Subsequently, the server processes the data using AI analysis algorithms to detect anomaly patterns. For example, if an anomaly is detected in crustal deformation data, the server evaluates the possibility that the anomaly is a precursor to an earthquake and predicts the disaster risk.

[0261] The analysis results are aggregated in a database and updated in real time. This updated information is uploaded to the cloud and made accessible to relevant organizations and users. The server also automatically generates efficient support plans based on this information and provides them to relevant organizations. This ensures optimal allocation of supplies and expedites support activities.

[0262] Terminal role

[0263] The terminal receives alarm information transmitted from the server and notifies the user of that information. The user can then check these alarms via their smartphone or computer and take emergency action as needed. In this way, the terminal plays a crucial role in supporting on-site responses.

[0264] User roles and processes

[0265] When users conduct on-site support activities, they send feedback from the field to the server. For example, they might report the actual extent of damage or the shortage of supplies via their devices. This feedback information becomes important data for improving the accuracy of the server's analysis.

[0266] Thus, the present invention provides a specific system configuration for achieving an efficient response to natural disasters.

[0267] The following describes the processing flow.

[0268] Step 1:

[0269] The server periodically receives data from satellite observation and sensor devices. Specifically, it acquires data including crustal deformation data, climate data, and sea level information.

[0270] Step 2:

[0271] The server preprocesses the received raw data. Specifically, it performs processes to fill in missing data and remove noise, preparing the data for analysis.

[0272] Step 3:

[0273] The server feeds pre-processed data into an AI analysis algorithm to detect anomalous patterns and signs of disaster. This analysis uses a machine learning model based on historical data, and if an anomaly is detected, its location and the risk level of its occurrence are evaluated.

[0274] Step 4:

[0275] The server aggregates the analysis results into a database and updates it to the cloud in real time. This information will be made available to relevant organizations and users through access.

[0276] Step 5:

[0277] The device receives alarm information transmitted from the server and sends emergency notifications to the user. The device uses push notifications to deliver warnings such as earthquakes and floods to the user, prompting them to take prompt action.

[0278] Step 6:

[0279] The server automatically generates an optimal support plan based on the analysis data and provides it to relevant support groups and organizations. This plan includes the distribution route of supplies and the location of shelters, etc.

[0280] Step 7:

[0281] The user feeds back the information obtained through on-site situation confirmation and support activities to the server. The feedback is reported via a smartphone or other terminal and is used to continuously improve the system and enhance the analysis accuracy.

[0282] (Example 1)

[0283] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0284] The conventional natural disaster countermeasure system had problems such as delays in processing after data reception and difficulty in quickly generating an effective support plan. Also, since it was not sufficient to detect abnormalities such as crustal movements and weather changes in real time and perform risk assessments, there was a problem that the immediate response at the time of a disaster was delayed. Furthermore, there was a lack of a mechanism to effectively reflect the feedback from the site to improve the analysis accuracy.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following respective means.

[0286] In this invention, the server includes means for receiving data from observation devices, means for preprocessing the received data to complement deficiencies and remove noise, and means for analyzing the preprocessed data to detect abnormal patterns. Thereby, it becomes possible to efficiently and quickly perform from data reception to analysis, alarm notification, and support plan generation. Furthermore, by utilizing the feedback from the site and continuously improving the analysis accuracy, a more appropriate disaster response can be realized.

[0287] "Observation equipment" refers to devices used to monitor natural phenomena and collect data on them. Examples include satellites, seismometers, and weather sensors.

[0288] "Preprocessing" refers to the process of preparing raw data for analysis by removing noise and imputing missing information.

[0289] An "anomalous pattern" refers to a portion of the data being analyzed that exhibits unusual movements or changes that deviate from the normal range.

[0290] "Risk assessment" involves analyzing the likelihood that detected abnormal patterns could lead to disasters and quantifying that likelihood.

[0291] An "information repository" refers to a database used to collect and store data within a system.

[0292] An "information network" refers to the entire network of communications used to share data with users and related organizations.

[0293] A "mobile communication terminal" is a device that receives and allows users to view mobile information, and includes smartphones and tablets.

[0294] A "support plan" refers to a plan generated based on analytical data to efficiently carry out actions such as the distribution of supplies and the dispatch of personnel in the event of a disaster.

[0295] "Feedback" refers to information that reports on reactions and areas for improvement, such as the situation on the ground and the results of support activities.

[0296] "Geospatial technology" refers to technologies for processing and analyzing geographical information, including mapmaking and spatial analysis.

[0297] This system invention has a complex configuration including observation equipment, servers, terminals, and users, in order to realize rapid and accurate information gathering and support activities in response to natural disasters.

[0298] The server functions as a central integrator, receiving and efficiently processing data from observation instruments. These instruments include satellites, seismometers, weather sensors, and river level gauges, from which data on natural phenomena such as crustal deformation, weather patterns, and water levels are acquired. The received data is preprocessed using dedicated analysis software. This software utilizes noise reduction algorithms and missing data imputation techniques to improve data accuracy. Furthermore, by integrating AI analysis algorithms, it detects anomalous patterns and assesses disaster risk. The analysis results are aggregated in a database and then distributed in real time to relevant organizations and users via a cloud-based information network.

[0299] The terminal functions as a means of providing users with alert information. Users can receive alerts via smartphones or computers, enabling them to respond quickly. Along with alert notifications, the system automatically generates support plans based on analysis results and sends them to relevant organizations, thereby streamlining support activities such as supply distribution and evacuation guidance.

[0300] Users are responsible for transmitting feedback from the field to the server via their devices. This includes detailed reports on support activities carried out on-site and up-to-date information on the extent of the damage, which improves the accuracy of the server's analysis.

[0301] As a concrete example, when a typhoon occurs, the server analyzes data such as wind speed, precipitation, and trajectory obtained from observation equipment to predict areas that may be affected. Based on this information, swift evacuation orders are sent to residents via terminals. Furthermore, the generated support plan is used for distributing relief supplies and determining where to dispatch personnel.

[0302] Examples of prompt messages include specific instructions such as, "Analyze the data from the following sensors, identify anomaly patterns, and assess the risk level. Use the AI ​​model to generate the optimal support plan."

[0303] The flow of the specific process in Example 1 will be described using FIG. 11.

[0304] Step 1:

[0305] The server receives data such as crustal movement, weather patterns, and water level information from the observation devices. These data are raw observation data and require preprocessing for accuracy improvement. The input is raw data, and the output is data suitable for preprocessing.

[0306] Step 2:

[0307] The server performs preprocessing on the received data. Specifically, it removes the noise of the data using a noise removal algorithm and estimates and complements the missing data using a missing data completion technique. This process outputs clean data suitable for analysis.

[0308] Step 3:

[0309] The server inputs the clean data into an AI analysis algorithm to detect abnormal patterns. If a specific movement is found in the crustal movement data, that movement is identified as an anomaly and the disaster risk is evaluated. The output after analysis is the abnormal pattern and its risk level.

[0310] Step 4:

[0311] The server aggregates the analysis results in a database and transmits them onto the information network in real time. This information is made accessible to users and relevant institutions as a virtual risk dashboard. The output exists as accessible real-time data.

[0312] Step 5:

[0313] The terminal receives the warning information sent from the server and notifies the user. For example, when there is a possibility of an earthquake, it immediately warns the users living in a specific area. The input is warning data, and the output is an alert notification to the user.

[0314] Step 6:

[0315] The server generates a support plan based on the analysis data and automatically transmits it to the relevant organizations. This support plan includes actionable guidelines regarding the distribution routes of supplies and the optimal allocation of resources. The output is a concrete and actionable support plan.

[0316] Step 7:

[0317] Users provide feedback to the server via their terminals, based on information gathered on-site. They send detailed reports to the server, including the local situation, the results of the support provided, and areas for improvement. This feedback is used for re-evaluation, promoting continuous improvement in analysis accuracy. The input is on-site feedback information, and the output is the improved analysis results.

[0318] (Application Example 1)

[0319] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0320] During natural disasters, rapid and accurate information gathering and support activities are crucial. However, conventional systems struggle to efficiently process large amounts of data, provide real-time information, and optimize support plans. In particular, there is a lack of safe and optimal route calculations for moving objects during disasters, posing a risk of delays in support activities. A system is needed to solve these problems and enable more effective disaster response.

[0321] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0322] In this invention, the server includes means for receiving data from satellite observation devices and sensor devices; means for preprocessing the received data to fill in gaps and remove noise; means for analyzing the preprocessed data and detecting abnormal patterns; means for aggregating the analysis results in a database and updating the information infrastructure in real time; means for notifying terminal devices of alarm information; means for automatically generating support plans based on the analysis data and transmitting them to relevant organizations; means for receiving return information obtained from on-site responses and improving analysis accuracy; and means for optimizing the trajectory of moving objects and automatically calculating safe routes based on obstacle information. This enables accurate information provision in real time and safe delivery of support supplies.

[0323] A "satellite observation device" is a device installed on an artificial satellite orbiting the Earth that observes data on the Earth's surface and weather, and transmits it to the ground as a signal.

[0324] A "sensor device" is a device used to measure physical phenomena and environmental conditions and acquire corresponding data. For example, it is used to detect crustal movements and changes in weather.

[0325] "Preprocessing" refers to the process of preparing raw observational data into a format suitable for analysis, and includes tasks such as imputing missing data and removing noise.

[0326] "Analysis" refers to data processing performed on received and pre-processed data to detect specific patterns or anomalies.

[0327] A "database" is a system that centrally manages structured information and allows for rapid access and updating.

[0328] "Information infrastructure" refers to the fundamental systems and networks for processing and utilizing data safely and quickly.

[0329] "Warning information" refers to important information regarding disasters or anomalies that is sent to devices to encourage a quick response.

[0330] A "support plan" is a plan that automatically generates action guidelines and resource allocations for appropriate disaster response.

[0331] "Related organizations" refers to various groups and institutions involved in disaster response and support activities, with the aim of ensuring effective coordination among them.

[0332] "On-site response" refers to the specific actions taken at actual disaster sites, and the related feedback information is used to improve overall accuracy.

[0333] "Return information" refers to performance data and feedback obtained from the field, and this information is used to improve the accuracy of the system.

[0334] A "mobile vehicle" refers to an unmanned or manned vehicle used for transporting supplies or gathering information during a disaster.

[0335] A "safe route" is the safest and most efficient path that a moving object should take, taking into account various environments and circumstances.

[0336] "Track optimization" refers to the process of optimizing the route plan so that a moving object can reach its destination efficiently and safely.

[0337] The system of this invention consists of multiple devices and programs designed to quickly and accurately collect information during natural disasters and to streamline support activities. At the heart of the system is a server that processes data received from satellite observation and sensor devices. The server uses specialized software to preprocess large amounts of raw data, filling in gaps and removing noise. Next, the preprocessed data is analyzed using AI analysis algorithms to detect anomalous patterns. Machine learning platforms such as TensorFlow are used in this process.

[0338] The analyzed data is aggregated in real time in a database on the information infrastructure, and alert information is automatically sent to the relevant organizations and institutions. Terminals receive this alert information and notify the user. Users send feedback to the server as information upon their return from the field, contributing to improving the accuracy of the system's analysis.

[0339] Furthermore, in the operation of the mobile vehicles, the server utilizes algorithms that incorporate obstacle and geographical information to optimize safe and efficient routes. This enables autonomous vehicles to safely deliver supplies during disasters.

[0340] As a concrete example, in the delivery of relief supplies during a disaster, the system calculates the optimal route considering road closure information and weather conditions. Autonomous vehicles then efficiently deliver supplies based on this route, accelerating relief efforts. An example of a prompt using a generative AI model is: "Calculate the latest route to the disaster area and optimize safe and efficient supply delivery. Consider current location and traffic information, and suggest a route that avoids obstacles."

[0341] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0342] Step 1:

[0343] The server receives raw data, including crustal deformation, weather, and water level information, from satellite observation and sensor devices. The input is the raw data, and the output is the pre-processed result of the data. In this step, the server performs noise reduction and missing data imputation to prepare the data for analysis. Specifically, noise is removed using a filtering algorithm, and missing values ​​are filled in by an imputation algorithm.

[0344] Step 2:

[0345] The server processes pre-processed data through an AI analysis algorithm to detect anomalous patterns. The input is pre-processed data, and the output is the analysis result of the anomalous patterns. The server uses machine learning models to analyze anomalous trends within the data and assess potential disaster risks. In particular, it utilizes TensorFlow to predict earthquake and flood precursors.

[0346] Step 3:

[0347] The server aggregates the analysis results into a database on the information infrastructure and updates them in real time via a cloud service. The input is the analysis results, and the output is notification information for relevant organizations. The analysis results are immediately uploaded to the cloud and made available for relevant organizations to access at any time. Specifically, the update work is performed using a database update API.

[0348] Step 4:

[0349] The terminal checks information in the cloud and notifies the user of important alerts. The input is alert information retrieved from the cloud, and the output is an alert notification to the user. The terminal uses a notification mechanism to send warnings to smartphones and dedicated devices. At this time, it is provided with a user-friendly interface so that the user can immediately understand the situation.

[0350] Step 5:

[0351] The server calculates the optimal route for the moving object based on the analysis data and obstacle information. The input for this step is the analysis data, and the output is the optimal route. The route calculation incorporates real-time map data through integration with a geographic information system, and also takes traffic conditions and weather into consideration. The calculated route is then transmitted to the autonomous vehicle.

[0352] Step 6:

[0353] The autonomous vehicle receives the optimal route transmitted from the server and safely delivers the relief supplies. The input is the optimal route information from the server, and the output is the result of the supplies delivery. Specifically, the automated control system accurately guides the vehicle to the target location, and sensors are used to avoid obstacles.

[0354] Step 7:

[0355] Users send feedback to the server regarding the local situation and any additional requests after the delivery of supplies. The input is information collected on-site, and the output is feedback information. Users submit feedback using a smart device with simple operations. This feedback is used to improve the accuracy of the system's analysis and is utilized in subsequent support activities.

[0356] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0357] This invention relates to a system for facilitating rapid information gathering and support activities during natural disasters, and further provides more personalized support by incorporating an emotion engine that recognizes the user's emotions.

[0358] The system is primarily composed of three elements: "server," "terminal," and "user."

[0359] Server roles and processing

[0360] The server receives crustal deformation data, climate data, water level information, etc., from satellite observation and sensor devices, and preprocesses this data. This processing includes denoising and imputing missing data. Subsequently, the preprocessed data is analyzed using an AI analysis algorithm to detect anomaly patterns.

[0361] The server aggregates the analysis results into a database and updates them to the cloud in real time. This data becomes immediately available to users through relevant organizations and devices, enabling the automated generation of efficient support plans. Crucially, this involves the use of an emotion engine. The server receives feedback from users and analyzes the text and audio data within that feedback using the emotion engine to understand the user's emotional state.

[0362] Based on the results of the emotion analysis, the server adjusts the support plan and provides the user with the most appropriate information. For example, if the user is experiencing high levels of stress, it can provide a more reassuring message.

[0363] Terminal role

[0364] The device receives alert information and optimized support plans transmitted from the server and notifies the user. Furthermore, it can provide content to promote relaxation based on the results of the emotion engine. This reduces the user's psychological burden and encourages a quick and appropriate response.

[0365] User roles and processes

[0366] Users use their devices to check the situation on-site and send feedback to the server as needed. This feedback can include written text or voice messages, which are analyzed in detail by an emotion engine. This allows for personalized support and enables rapid decision-making.

[0367] In this way, by incorporating an emotion engine, the system provides flexible and effective support that takes into account the user's emotional state.

[0368] The following describes the processing flow.

[0369] Step 1:

[0370] The server receives data in real time from satellite observation and sensor devices. This data includes information on crustal deformation, climate change, and sea level.

[0371] Step 2:

[0372] The server preprocesses the received data. Specifically, it fills in missing parts, removes noise, and prepares the data for analysis.

[0373] Step 3:

[0374] The server processes pre-processed data using an AI analysis algorithm to detect anomalous patterns. This analysis allows for the early identification of natural disaster risks.

[0375] Step 4:

[0376] The server aggregates the analysis results into a cloud-based database and updates it in real time. This makes the data accessible to relevant organizations and users.

[0377] Step 5:

[0378] The server receives feedback from users. This feedback can be sent in text or audio format and is analyzed by an emotion engine.

[0379] Step 6:

[0380] The server uses an emotion engine to recognize emotions from user feedback. The analysis results are used to understand the user's psychological state.

[0381] Step 7:

[0382] The server adjusts the support plan based on the emotion analysis results. For users experiencing high stress levels, messages and information designed to provide reassurance are generated.

[0383] Step 8:

[0384] The device notifies the user of alert information and optimized support plans sent from the server. Furthermore, it provides content that promotes relaxation based on the user's emotions.

[0385] Step 9:

[0386] Users review the information provided on their devices and take action as needed. User feedback is sent to the server in the next cycle to help the system make decisions.

[0387] (Example 2)

[0388] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0389] While there is a need for rapid and appropriate information gathering and smooth support activities during natural disasters, conventional systems have challenges in real-time data processing and flexible support based on user sentiment, resulting in a lack of measures tailored to individual needs.

[0390] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0391] In this invention, the server includes means for receiving data from observation and detection devices, means for preprocessing the received data to fill in missing data and remove noise, and means for analyzing the preprocessed data to detect abnormal patterns. This enables real-time, high-precision data analysis, and by utilizing an emotion analysis engine that analyzes user emotions, it becomes possible to provide flexible and effective support tailored to individual needs.

[0392] An "observation device" is a device installed on satellites or on the ground to quantitatively capture changes in the environment or the Earth's surface.

[0393] A "detection device" is a device that senses specific phenomena, physical quantities, or environmental changes and acquires data related to them.

[0394] "Preprocessing" refers to the process of preparing data for analysis by performing tasks such as noise reduction, data interpolation, and format conversion.

[0395] "Noise" refers to unwanted information contained in data that negatively impacts the analysis results.

[0396] An "anomalous pattern" is a data trend that deviates from normal data and exhibits unusual behavior or phenomena.

[0397] An "information management device" is a device or system for aggregating data and storing and organizing analysis results.

[0398] An "information processing infrastructure" is a platform for instantly managing, processing, and sharing data on the cloud or network.

[0399] A "computing device" is a device that electronically processes information and provides that information to the user.

[0400] A "support plan" is an action plan created to efficiently provide the necessary support for a specific situation.

[0401] An "emotion analysis engine" is a system that extracts emotional information from text and audio to analyze the user's emotional state.

[0402] "User emotional state" refers to the mental reactions and sensitivities that a user is experiencing.

[0403] The embodiment of the invention is a system that enables rapid and effective information gathering and support during natural disasters. This system is constructed using observation devices, detection devices, servers, terminals, and generative AI models.

[0404] The server acquires data in real time from observation and detection devices. The received data is preprocessed to remove noise and impute missing data. This preprocessing uses statistical methods and filtering techniques. After preprocessing is complete, the server analyzes the data using AI analysis algorithms to detect anomalous patterns. The AI ​​analysis algorithms used typically include machine learning or deep learning techniques.

[0405] The analysis results are aggregated in an information management device and updated in real time on the information processing infrastructure. The terminal functions as a computing device and notifies the user of alarms and optimized support plans transmitted from the server. Furthermore, it utilizes an emotion analysis engine to analyze user feedback and adjust the support plan based on the user's emotional state.

[0406] Users check the local situation through their devices and send feedback to the server in voice or text format. This feedback is further analyzed by an emotion analysis engine to provide optimal information tailored to the user's individual needs.

[0407] A concrete example is the rapid analysis of crustal deformation and climate data in specific areas during disasters, and the provision of evacuation orders based on that analysis. An example of a prompt for the generating AI model is, "Please show the data processing flow for creating an appropriate support plan during a disaster."

[0408] This system enables a rapid and appropriate response in specific disaster situations, improving user safety and peace of mind.

[0409] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0410] Step 1:

[0411] The server receives crustal deformation data, meteorological data, and water level information in real time from observation and detection devices. Input is raw data from each device, and output is a collection of raw data. The data is converted into a structured format and prepared for further processing.

[0412] Step 2:

[0413] The server preprocesses the received raw data. It applies noise reduction filtering to remove outliers. Furthermore, it uses statistical methods to impute missing data. The preprocessed data is generated as output and prepared for analysis. As a specific example, data lost due to sensor communication failures is supplemented with average values ​​from a surrounding database.

[0414] Step 3:

[0415] The server inputs pre-processed data into an AI analysis algorithm to detect anomalous patterns. The input is a clean dataset, and the output is a list of detected anomalous patterns. The detection process uses machine learning models and employs pattern recognition techniques. For example, it can automatically identify waveform patterns that indicate an impending earthquake.

[0416] Step 4:

[0417] The server aggregates the analysis results in the information management device and updates the data in the information processing infrastructure in real time. The input is a list of abnormal patterns, and the output is an updated database. This allows relevant organizations and terminals to access the new information immediately.

[0418] Step 5:

[0419] The terminal receives analysis results and support plans from the server. The input is optimization information sent from the server, and the output is alarms and support plans notified to the user. The terminal immediately notifies the user and, if necessary, alerts them with an alarm sound or vibration.

[0420] Step 6:

[0421] Users check the on-site situation and send feedback to the server via their terminal. Input is text or audio describing the on-site situation, and output is user feedback data. The feedback is transcribed into text using speech recognition technology and used for sentiment analysis.

[0422] Step 7:

[0423] The server analyzes user feedback using an emotion analysis engine to assess the user's emotional state. The input is text data of the feedback, and the output is an evaluation result indicating the emotional state. Based on these results, the support plan is adjusted to match the user's psychological state. For example, information that provides reassurance is added for users who are feeling anxious.

[0424] (Application Example 2)

[0425] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0426] In today's information society, the rapid and accurate provision of information during natural disasters is essential for social stability. However, conventional systems provide uniform information without considering the individual emotional state of users, making it difficult to provide support tailored to individual circumstances. Therefore, there is a need to develop systems that reduce the psychological burden on users during disasters and provide more accurate information.

[0427] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0428] In this invention, the server includes means for receiving data from satellite observation devices and sensor devices; means for preprocessing the received data to fill in missing data and remove noise; means for analyzing the preprocessed data and detecting abnormal patterns; means for aggregating the analysis results in a database and updating it in real time on the cloud; means for notifying terminal devices of alarm information; means for automatically generating support plans based on the analysis data and transmitting them to relevant organizations; means for receiving feedback obtained from on-site responses and improving analysis accuracy; and means for recognizing emotions using a personal display device and providing personalized services based on the analysis results. This enables the provision of rapid and personalized information and support in accordance with the user's emotional state.

[0429] A "satellite observation device" is a device installed on an artificial satellite orbiting the Earth to observe the conditions of the Earth's surface and atmosphere.

[0430] A "sensor device" is a device that detects physical phenomena or states and outputs them as digital data.

[0431] "Preprocessing" refers to steps such as data imputation and noise reduction performed to improve the accuracy of data analysis.

[0432] An "anomalous pattern" is a data pattern that shows unusual movements or states that differ from normal observation data.

[0433] A "database" is a structured collection of data used to efficiently store, search, and manage information.

[0434] "Real-time updating" is an update method in which information is instantly reflected in the database or cloud the moment it is generated.

[0435] A "terminal device" is a device used by users to receive and manipulate digital information.

[0436] "Warning information" refers to information that is sent out to quickly draw attention when danger or abnormality occurs.

[0437] A "support plan" is a plan for providing support and assistance in a specific situation efficiently and effectively.

[0438] "Feedback" is the act of returning a response or evaluation of the information or service received to the sender.

[0439] "Personalized service" refers to the provision of services that are customized according to each individual's characteristics and circumstances.

[0440] "Emotional recognition" means analyzing and interpreting the user's emotional state from their facial expressions and voice.

[0441] To implement this system, it is necessary to build a network environment that handles information between servers, terminals, and users.

[0442] The server receives surface and weather data acquired from satellite observation and sensor devices. This data is often raw and therefore unsuitable for direct analysis. First, preprocessing is performed to remove noise and fill in missing data. This improves the quality of the data and increases the accuracy of the analysis. The preprocessed data is then analyzed by AI analysis algorithms to attempt to detect anomaly patterns. If an anomaly is detected, the information is aggregated in a database in real time and immediately updated on the cloud.

[0443] The terminal receives alert information and support plans from the server and notifies the user. Personalized display devices used here include smart glasses. The terminal recognizes the user's emotions in real time and provides personalized services accordingly. For example, if the emotional state indicates the user is experiencing high stress, it can display content with a relaxing effect.

[0444] Users use information obtained through their devices to check the situation and send their opinions and impressions to the server through feedback. This feedback is analyzed by an emotion engine and used to improve the service and optimize further support plans. User feedback becomes a new data source, contributing to improved analysis accuracy.

[0445] As a concrete example, a server might send an instruction to a terminal saying, "Provide this customer with music to help them relax." An example of a prompt using a generative AI model would be, "Analyze the customer's facial expression data to determine if they are relaxed. If they are tense, tell me what you should suggest to them."

[0446] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0447] Step 1:

[0448] The server receives crustal deformation data, climate data, and sea level information from satellite observation and sensor devices. Raw data is acquired as input and preprocessed. By removing noise and imputing missing parts of the data, a clean dataset is output.

[0449] Step 2:

[0450] The server feeds pre-processed data into an AI analysis algorithm to detect anomaly patterns. The input is clean, pre-processed data, and the AI ​​model analyzes this data to output the anomaly detection results. A generative AI model is utilized in this process.

[0451] Step 3:

[0452] The server aggregates detected anomaly patterns into a database and updates it in real time on the cloud. The input is the result of anomaly detection, and aggregation into the database and cloud updates are performed to efficiently manage this data.

[0453] Step 4:

[0454] The terminal receives alarm information and support plans sent from the server and notifies the user. The input is alarm information and support plans from the server, and the output is in the form of a notification to the user. This allows the user to immediately obtain guidance on what to do.

[0455] Step 5:

[0456] The device recognizes the user's emotions and provides personalized services. Input consists of the user's facial expressions and voice data, which are analyzed by an emotion engine. Output includes relaxing music and messages. Prompts generated using a generative AI model select the most appropriate content based on the user's situation.

[0457] Step 6:

[0458] Users check the current status based on information obtained through their devices and send feedback to the server. The input consists of services and information from the device, which are returned to the server as feedback. This feedback is then analyzed by the server and contributes to improving the support plan.

[0459] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0460] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0461] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0462] [Third Embodiment]

[0463] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0464] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0465] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0466] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0467] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0468] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0469] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0470] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0471] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0472] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0473] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0474] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0475] This invention is a system for enabling rapid and accurate information gathering and support activities in the event of a natural disaster. Specific embodiments are described below.

[0476] This system is primarily composed of the following elements: "server," "terminal," and "user."

[0477] Server roles and processing

[0478] The server is responsible for receiving various data from satellite observation and sensor devices. The received data relates to natural phenomena such as crustal deformation, climate change, and sea level information. This data is processed to prepare it for analysis from its raw state, undergoing noise reduction and data completion.

[0479] Subsequently, the server processes the data using AI analysis algorithms to detect anomaly patterns. For example, if an anomaly is detected in crustal deformation data, the server evaluates the possibility that the anomaly is a precursor to an earthquake and predicts the disaster risk.

[0480] The analysis results are aggregated in a database and updated in real time. This updated information is uploaded to the cloud and made accessible to relevant organizations and users. The server also automatically generates efficient support plans based on this information and provides them to relevant organizations. This ensures optimal allocation of supplies and expedites support activities.

[0481] Terminal role

[0482] The terminal receives alarm information transmitted from the server and notifies the user of that information. The user can then check these alarms via their smartphone or computer and take emergency action as needed. In this way, the terminal plays a crucial role in supporting on-site responses.

[0483] User roles and processes

[0484] When users conduct on-site support activities, they send feedback from the field to the server. For example, they might report the actual extent of damage or the shortage of supplies via their devices. This feedback information becomes important data for improving the accuracy of the server's analysis.

[0485] Thus, the present invention provides a specific system configuration for achieving an efficient response to natural disasters.

[0486] The following describes the processing flow.

[0487] Step 1:

[0488] The server periodically receives data from satellite observation and sensor devices. Specifically, it acquires data including crustal deformation data, climate data, and sea level information.

[0489] Step 2:

[0490] The server preprocesses the received raw data. Specifically, it performs processes to fill in missing data and remove noise, preparing the data for analysis.

[0491] Step 3:

[0492] The server feeds pre-processed data into an AI analysis algorithm to detect anomalous patterns and signs of disaster. This analysis uses a machine learning model based on historical data, and if an anomaly is detected, its location and the risk level of its occurrence are evaluated.

[0493] Step 4:

[0494] The server aggregates the analysis results into a database and updates it to the cloud in real time. This information will be made available to relevant organizations and users through access.

[0495] Step 5:

[0496] The device receives alarm information transmitted from the server and sends emergency notifications to the user. The device uses push notifications to deliver warnings such as earthquakes and floods to the user, prompting them to take prompt action.

[0497] Step 6:

[0498] The server automatically generates optimal support plans based on the analysis data and provides them to relevant aid organizations and agencies. These plans include distribution routes for supplies and the placement of shelters.

[0499] Step 7:

[0500] Users provide feedback to the server based on information gathered through on-site situation assessments and support activities. This feedback is reported via smartphones and other devices and is used to continuously improve the system and enhance the accuracy of the analysis.

[0501] (Example 1)

[0502] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0503] Conventional natural disaster response systems suffered from delays in processing data after reception, making it difficult to quickly generate effective support plans. Furthermore, they were unable to adequately detect anomalies such as crustal movements and weather changes in real time and conduct risk assessments, resulting in delays in immediate response during disasters. In addition, there was a lack of mechanisms to effectively incorporate feedback from the field to improve analysis accuracy.

[0504] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0505] In this invention, the server includes means for receiving data from observation equipment, means for preprocessing the received data to fill in missing data and remove noise, and means for analyzing the preprocessed data to detect abnormal patterns. This makes it possible to efficiently and quickly perform everything from data reception and analysis to alarm notification and support plan generation. Furthermore, by utilizing on-site feedback to continuously improve analysis accuracy, more appropriate disaster response can be achieved.

[0506] "Observation equipment" refers to devices used to monitor natural phenomena and collect data on them. Examples include satellites, seismometers, and weather sensors.

[0507] "Preprocessing" refers to the process of preparing raw data for analysis by removing noise and imputing missing information.

[0508] An "anomalous pattern" refers to a portion of the data being analyzed that exhibits unusual movements or changes that deviate from the normal range.

[0509] "Risk assessment" involves analyzing the likelihood that detected abnormal patterns could lead to disasters and quantifying that likelihood.

[0510] An "information repository" refers to a database used to collect and store data within a system.

[0511] An "information network" refers to the entire network of communications used to share data with users and related organizations.

[0512] A "mobile communication terminal" is a device that receives and allows users to view mobile information, and includes smartphones and tablets.

[0513] A "support plan" refers to a plan generated based on analytical data to efficiently carry out actions such as the distribution of supplies and the dispatch of personnel in the event of a disaster.

[0514] "Feedback" refers to information that reports on reactions and areas for improvement, such as the situation on the ground and the results of support activities.

[0515] "Geospatial technology" refers to technologies for processing and analyzing geographical information, including mapmaking and spatial analysis.

[0516] This system invention has a complex configuration including observation equipment, servers, terminals, and users, in order to realize rapid and accurate information gathering and support activities in response to natural disasters.

[0517] The server functions as a central integrator, receiving and efficiently processing data from observation instruments. These instruments include satellites, seismometers, weather sensors, and river level gauges, from which data on natural phenomena such as crustal deformation, weather patterns, and water levels are acquired. The received data is preprocessed using dedicated analysis software. This software utilizes noise reduction algorithms and missing data imputation techniques to improve data accuracy. Furthermore, by integrating AI analysis algorithms, it detects anomalous patterns and assesses disaster risk. The analysis results are aggregated in a database and then distributed in real time to relevant organizations and users via a cloud-based information network.

[0518] The terminal functions as a means of providing users with alert information. Users can receive alerts via smartphones or computers, enabling them to respond quickly. Along with alert notifications, the system automatically generates support plans based on analysis results and sends them to relevant organizations, thereby streamlining support activities such as supply distribution and evacuation guidance.

[0519] Users are responsible for transmitting feedback from the field to the server via their devices. This includes detailed reports on support activities carried out on-site and up-to-date information on the extent of the damage, which improves the accuracy of the server's analysis.

[0520] As a concrete example, when a typhoon occurs, the server analyzes data such as wind speed, precipitation, and trajectory obtained from observation equipment to predict areas that may be affected. Based on this information, swift evacuation orders are sent to residents via terminals. Furthermore, the generated support plan is used for distributing relief supplies and determining where to dispatch personnel.

[0521] Examples of prompt messages include specific instructions such as, "Analyze the data from the following sensors, identify anomaly patterns, and assess the risk level. Use the AI ​​model to generate the optimal support plan."

[0522] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0523] Step 1:

[0524] The server receives data such as crustal deformation, weather patterns, and water level information from observation instruments. This data is raw observational data and requires preprocessing to improve accuracy. The input is raw data, and the output is data suitable for preprocessing.

[0525] Step 2:

[0526] The server preprocesses the received data. Specifically, it removes noise from the data using a denoising algorithm and estimates and fills in missing data using missing data imputation techniques. This process outputs clean data suitable for analysis.

[0527] Step 3:

[0528] The server inputs clean data into an AI analysis algorithm to detect anomalous patterns. If unusual movements are observed in the crustal deformation data, these movements are identified as anomalies, and the disaster risk is assessed. The output after analysis is the anomalous pattern and its risk level.

[0529] Step 4:

[0530] The server aggregates the analysis results into a database and transmits them over the information network in real time. This information effectively serves as a risk dashboard, making it immediately accessible to users and relevant organizations. The output exists as accessible real-time data.

[0531] Step 5:

[0532] The terminal receives alarm information sent from the server and notifies the user. For example, in the event of a potential earthquake, it immediately warns users living in a specific area. The input is alarm data, and the output is an alert notification to the user.

[0533] Step 6:

[0534] The server generates a support plan based on the analysis data and automatically transmits it to the relevant organizations. This support plan includes actionable guidelines regarding the distribution routes of supplies and the optimal allocation of resources. The output is a concrete and actionable support plan.

[0535] Step 7:

[0536] Users provide feedback to the server via their terminals, based on information gathered on-site. They send detailed reports to the server, including the local situation, the results of the support provided, and areas for improvement. This feedback is used for re-evaluation, promoting continuous improvement in analysis accuracy. The input is on-site feedback information, and the output is the improved analysis results.

[0537] (Application Example 1)

[0538] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0539] During natural disasters, rapid and accurate information gathering and support activities are crucial. However, conventional systems struggle to efficiently process large amounts of data, provide real-time information, and optimize support plans. In particular, there is a lack of safe and optimal route calculations for moving objects during disasters, posing a risk of delays in support activities. A system is needed to solve these problems and enable more effective disaster response.

[0540] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0541] In this invention, the server includes means for receiving data from satellite observation devices and sensor devices; means for preprocessing the received data to fill in gaps and remove noise; means for analyzing the preprocessed data and detecting abnormal patterns; means for aggregating the analysis results in a database and updating the information infrastructure in real time; means for notifying terminal devices of alarm information; means for automatically generating support plans based on the analysis data and transmitting them to relevant organizations; means for receiving return information obtained from on-site responses and improving analysis accuracy; and means for optimizing the trajectory of moving objects and automatically calculating safe routes based on obstacle information. This enables accurate information provision in real time and safe delivery of support supplies.

[0542] A "satellite observation device" is a device installed on an artificial satellite orbiting the Earth that observes data on the Earth's surface and weather, and transmits it to the ground as a signal.

[0543] A "sensor device" is a device used to measure physical phenomena and environmental conditions and acquire corresponding data. For example, it is used to detect crustal movements and changes in weather.

[0544] "Preprocessing" refers to the process of preparing raw observational data into a format suitable for analysis, and includes tasks such as imputing missing data and removing noise.

[0545] "Analysis" refers to data processing performed on received and pre-processed data to detect specific patterns or anomalies.

[0546] A "database" is a system that centrally manages structured information and allows for rapid access and updating.

[0547] "Information infrastructure" refers to the fundamental systems and networks for processing and utilizing data safely and quickly.

[0548] "Warning information" refers to important information regarding disasters or anomalies that is sent to devices to encourage a quick response.

[0549] A "support plan" is a plan that automatically generates action guidelines and resource allocations for appropriate disaster response.

[0550] "Related organizations" refers to various groups and institutions involved in disaster response and support activities, with the aim of ensuring effective coordination among them.

[0551] "On-site response" refers to the specific actions taken at actual disaster sites, and the related feedback information is used to improve overall accuracy.

[0552] "Return information" refers to performance data and feedback obtained from the field, and this information is used to improve the accuracy of the system.

[0553] A "mobile vehicle" refers to an unmanned or manned vehicle used for transporting supplies or gathering information during a disaster.

[0554] A "safe route" is the safest and most efficient path that a moving object should take, taking into account various environments and circumstances.

[0555] "Track optimization" refers to the process of optimizing the route plan so that a moving object can reach its destination efficiently and safely.

[0556] The system of this invention consists of multiple devices and programs designed to quickly and accurately collect information during natural disasters and to streamline support activities. At the heart of the system is a server that processes data received from satellite observation and sensor devices. The server uses specialized software to preprocess large amounts of raw data, filling in gaps and removing noise. Next, the preprocessed data is analyzed using AI analysis algorithms to detect anomalous patterns. Machine learning platforms such as TensorFlow are used in this process.

[0557] The analyzed data is aggregated in real time in a database on the information infrastructure, and alert information is automatically sent to the relevant organizations and institutions. Terminals receive this alert information and notify the user. Users send feedback to the server as information upon their return from the field, contributing to improving the accuracy of the system's analysis.

[0558] Furthermore, in the operation of the mobile vehicles, the server utilizes algorithms that incorporate obstacle and geographical information to optimize safe and efficient routes. This enables autonomous vehicles to safely deliver supplies during disasters.

[0559] As a concrete example, in the delivery of relief supplies during a disaster, the system calculates the optimal route considering road closure information and weather conditions. Autonomous vehicles then efficiently deliver supplies based on this route, accelerating relief efforts. An example of a prompt using a generative AI model is: "Calculate the latest route to the disaster area and optimize safe and efficient supply delivery. Consider current location and traffic information, and suggest a route that avoids obstacles."

[0560] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0561] Step 1:

[0562] The server receives raw data, including crustal deformation, weather, and water level information, from satellite observation and sensor devices. The input is the raw data, and the output is the pre-processed result of the data. In this step, the server performs noise reduction and missing data imputation to prepare the data for analysis. Specifically, noise is removed using a filtering algorithm, and missing values ​​are filled in by an imputation algorithm.

[0563] Step 2:

[0564] The server processes pre-processed data through an AI analysis algorithm to detect anomalous patterns. The input is pre-processed data, and the output is the analysis result of the anomalous patterns. The server uses machine learning models to analyze anomalous trends within the data and assess potential disaster risks. In particular, it utilizes TensorFlow to predict earthquake and flood precursors.

[0565] Step 3:

[0566] The server aggregates the analysis results into a database on the information infrastructure and updates them in real time via a cloud service. The input is the analysis results, and the output is notification information for relevant organizations. The analysis results are immediately uploaded to the cloud and made available for relevant organizations to access at any time. Specifically, the update work is performed using a database update API.

[0567] Step 4:

[0568] The terminal checks information in the cloud and notifies the user of important alerts. The input is alert information retrieved from the cloud, and the output is an alert notification to the user. The terminal uses a notification mechanism to send warnings to smartphones and dedicated devices. At this time, it is provided with a user-friendly interface so that the user can immediately understand the situation.

[0569] Step 5:

[0570] The server calculates the optimal route for the moving object based on the analysis data and obstacle information. The input for this step is the analysis data, and the output is the optimal route. The route calculation incorporates real-time map data through integration with a geographic information system, and also takes traffic conditions and weather into consideration. The calculated route is then transmitted to the autonomous vehicle.

[0571] Step 6:

[0572] The autonomous vehicle receives the optimal route transmitted from the server and safely delivers the relief supplies. The input is the optimal route information from the server, and the output is the result of the supplies delivery. Specifically, the automated control system accurately guides the vehicle to the target location, and sensors are used to avoid obstacles.

[0573] Step 7:

[0574] Users send feedback to the server regarding the local situation and any additional requests after the delivery of supplies. The input is information collected on-site, and the output is feedback information. Users submit feedback using a smart device with simple operations. This feedback is used to improve the accuracy of the system's analysis and is utilized in subsequent support activities.

[0575] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0576] This invention relates to a system for facilitating rapid information gathering and support activities during natural disasters, and further provides more personalized support by incorporating an emotion engine that recognizes the user's emotions.

[0577] The system is primarily composed of three elements: "server," "terminal," and "user."

[0578] Server roles and processing

[0579] The server receives crustal deformation data, climate data, water level information, etc., from satellite observation and sensor devices, and preprocesses this data. This processing includes denoising and imputing missing data. Subsequently, the preprocessed data is analyzed using an AI analysis algorithm to detect anomaly patterns.

[0580] The server aggregates the analysis results into a database and updates them to the cloud in real time. This data becomes immediately available to users through relevant organizations and devices, enabling the automated generation of efficient support plans. Crucially, this involves the use of an emotion engine. The server receives feedback from users and analyzes the text and audio data within that feedback using the emotion engine to understand the user's emotional state.

[0581] Based on the results of the emotion analysis, the server adjusts the support plan and provides the user with the most appropriate information. For example, if the user is experiencing high levels of stress, it can provide a more reassuring message.

[0582] Terminal role

[0583] The device receives alert information and optimized support plans transmitted from the server and notifies the user. Furthermore, it can provide content to promote relaxation based on the results of the emotion engine. This reduces the user's psychological burden and encourages a quick and appropriate response.

[0584] User roles and processes

[0585] Users use their devices to check the situation on-site and send feedback to the server as needed. This feedback can include written text or voice messages, which are analyzed in detail by an emotion engine. This allows for personalized support and enables rapid decision-making.

[0586] In this way, by incorporating an emotion engine, the system provides flexible and effective support that takes into account the user's emotional state.

[0587] The following describes the processing flow.

[0588] Step 1:

[0589] The server receives data in real time from satellite observation and sensor devices. This data includes information on crustal deformation, climate change, and sea level.

[0590] Step 2:

[0591] The server preprocesses the received data. Specifically, it fills in missing parts, removes noise, and prepares the data for analysis.

[0592] Step 3:

[0593] The server processes pre-processed data using an AI analysis algorithm to detect anomalous patterns. This analysis allows for the early identification of natural disaster risks.

[0594] Step 4:

[0595] The server aggregates the analysis results into a cloud-based database and updates it in real time. This makes the data accessible to relevant organizations and users.

[0596] Step 5:

[0597] The server receives feedback from users. This feedback can be sent in text or audio format and is analyzed by an emotion engine.

[0598] Step 6:

[0599] The server uses an emotion engine to recognize emotions from user feedback. The analysis results are used to understand the user's psychological state.

[0600] Step 7:

[0601] The server adjusts the support plan based on the emotion analysis results. For users experiencing high stress levels, messages and information designed to provide reassurance are generated.

[0602] Step 8:

[0603] The device notifies the user of alert information and optimized support plans sent from the server. Furthermore, it provides content that promotes relaxation based on the user's emotions.

[0604] Step 9:

[0605] Users review the information provided on their devices and take action as needed. User feedback is sent to the server in the next cycle to help the system make decisions.

[0606] (Example 2)

[0607] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0608] While there is a need for rapid and appropriate information gathering and smooth support activities during natural disasters, conventional systems have challenges in real-time data processing and flexible support based on user sentiment, resulting in a lack of measures tailored to individual needs.

[0609] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0610] In this invention, the server includes means for receiving data from observation and detection devices, means for preprocessing the received data to fill in missing data and remove noise, and means for analyzing the preprocessed data to detect abnormal patterns. This enables real-time, high-precision data analysis, and by utilizing an emotion analysis engine that analyzes user emotions, it becomes possible to provide flexible and effective support tailored to individual needs.

[0611] An "observation device" is a device installed on satellites or on the ground to quantitatively capture changes in the environment or the Earth's surface.

[0612] A "detection device" is a device that senses specific phenomena, physical quantities, or environmental changes and acquires data related to them.

[0613] "Preprocessing" refers to the process of preparing data for analysis by performing tasks such as noise reduction, data interpolation, and format conversion.

[0614] "Noise" refers to unwanted information contained in data that negatively impacts the analysis results.

[0615] An "anomalous pattern" is a data trend that deviates from normal data and exhibits unusual behavior or phenomena.

[0616] An "information management device" is a device or system for aggregating data and storing and organizing analysis results.

[0617] An "information processing infrastructure" is a platform for instantly managing, processing, and sharing data on the cloud or network.

[0618] A "computing device" is a device that electronically processes information and provides that information to the user.

[0619] A "support plan" is an action plan created to efficiently provide the necessary support for a specific situation.

[0620] An "emotion analysis engine" is a system that extracts emotional information from text and audio to analyze the user's emotional state.

[0621] "User emotional state" refers to the mental reactions and sensitivities that a user is experiencing.

[0622] The embodiment of the invention is a system that enables rapid and effective information gathering and support during natural disasters. This system is constructed using observation devices, detection devices, servers, terminals, and generative AI models.

[0623] The server acquires data in real time from observation and detection devices. The received data is preprocessed to remove noise and impute missing data. This preprocessing uses statistical methods and filtering techniques. After preprocessing is complete, the server analyzes the data using AI analysis algorithms to detect anomalous patterns. The AI ​​analysis algorithms used typically include machine learning or deep learning techniques.

[0624] The analysis results are aggregated in an information management device and updated in real time on the information processing infrastructure. The terminal functions as a computing device and notifies the user of alarms and optimized support plans transmitted from the server. Furthermore, it utilizes an emotion analysis engine to analyze user feedback and adjust the support plan based on the user's emotional state.

[0625] Users check the local situation through their devices and send feedback to the server in voice or text format. This feedback is further analyzed by an emotion analysis engine to provide optimal information tailored to the user's individual needs.

[0626] A concrete example is the rapid analysis of crustal deformation and climate data in specific areas during disasters, and the provision of evacuation orders based on that analysis. An example of a prompt for the generating AI model is, "Please show the data processing flow for creating an appropriate support plan during a disaster."

[0627] This system enables a rapid and appropriate response in specific disaster situations, improving user safety and peace of mind.

[0628] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0629] Step 1:

[0630] The server receives crustal deformation data, meteorological data, and water level information in real time from observation and detection devices. Input is raw data from each device, and output is a collection of raw data. The data is converted into a structured format and prepared for further processing.

[0631] Step 2:

[0632] The server preprocesses the received raw data. It applies noise reduction filtering to remove outliers. Furthermore, it uses statistical methods to impute missing data. The preprocessed data is generated as output and prepared for analysis. As a specific example, data lost due to sensor communication failures is supplemented with average values ​​from a surrounding database.

[0633] Step 3:

[0634] The server inputs pre-processed data into an AI analysis algorithm to detect anomalous patterns. The input is a clean dataset, and the output is a list of detected anomalous patterns. The detection process uses machine learning models and employs pattern recognition techniques. For example, it can automatically identify waveform patterns that indicate an impending earthquake.

[0635] Step 4:

[0636] The server aggregates the analysis results in the information management device and updates the data in the information processing infrastructure in real time. The input is a list of abnormal patterns, and the output is an updated database. This allows relevant organizations and terminals to access the new information immediately.

[0637] Step 5:

[0638] The terminal receives analysis results and support plans from the server. The input is optimization information sent from the server, and the output is alarms and support plans notified to the user. The terminal immediately notifies the user and, if necessary, alerts them with an alarm sound or vibration.

[0639] Step 6:

[0640] Users check the on-site situation and send feedback to the server via their terminal. Input is text or audio describing the on-site situation, and output is user feedback data. The feedback is transcribed into text using speech recognition technology and used for sentiment analysis.

[0641] Step 7:

[0642] The server analyzes user feedback using an emotion analysis engine to assess the user's emotional state. The input is text data of the feedback, and the output is an evaluation result indicating the emotional state. Based on these results, the support plan is adjusted to match the user's psychological state. For example, information that provides reassurance is added for users who are feeling anxious.

[0643] (Application Example 2)

[0644] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0645] In today's information society, the rapid and accurate provision of information during natural disasters is essential for social stability. However, conventional systems provide uniform information without considering the individual emotional state of users, making it difficult to provide support tailored to individual circumstances. Therefore, there is a need to develop systems that reduce the psychological burden on users during disasters and provide more accurate information.

[0646] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0647] In this invention, the server includes means for receiving data from satellite observation devices and sensor devices; means for preprocessing the received data to fill in missing data and remove noise; means for analyzing the preprocessed data and detecting abnormal patterns; means for aggregating the analysis results in a database and updating it in real time on the cloud; means for notifying terminal devices of alarm information; means for automatically generating support plans based on the analysis data and transmitting them to relevant organizations; means for receiving feedback obtained from on-site responses and improving analysis accuracy; and means for recognizing emotions using a personal display device and providing personalized services based on the analysis results. This enables the provision of rapid and personalized information and support in accordance with the user's emotional state.

[0648] A "satellite observation device" is a device installed on an artificial satellite orbiting the Earth to observe the conditions of the Earth's surface and atmosphere.

[0649] A "sensor device" is a device that detects physical phenomena or states and outputs them as digital data.

[0650] "Preprocessing" refers to steps such as data imputation and noise reduction performed to improve the accuracy of data analysis.

[0651] An "anomalous pattern" is a data pattern that shows unusual movements or states that differ from normal observation data.

[0652] A "database" is a structured collection of data used to efficiently store, search, and manage information.

[0653] "Real-time updating" is an update method in which information is instantly reflected in the database or cloud the moment it is generated.

[0654] A "terminal device" is a device used by users to receive and manipulate digital information.

[0655] "Warning information" refers to information that is sent out to quickly draw attention when danger or abnormality occurs.

[0656] A "support plan" is a plan for providing support and assistance in a specific situation efficiently and effectively.

[0657] "Feedback" is the act of returning a response or evaluation of the information or service received to the sender.

[0658] "Personalized service" refers to the provision of services that are customized according to each individual's characteristics and circumstances.

[0659] "Emotional recognition" means analyzing and interpreting the user's emotional state from their facial expressions and voice.

[0660] To implement this system, it is necessary to build a network environment that handles information between servers, terminals, and users.

[0661] The server receives surface and weather data acquired from satellite observation and sensor devices. This data is often raw and therefore unsuitable for direct analysis. First, preprocessing is performed to remove noise and fill in missing data. This improves the quality of the data and increases the accuracy of the analysis. The preprocessed data is then analyzed by AI analysis algorithms to attempt to detect anomaly patterns. If an anomaly is detected, the information is aggregated in a database in real time and immediately updated on the cloud.

[0662] The terminal receives alert information and support plans from the server and notifies the user. Personalized display devices used here include smart glasses. The terminal recognizes the user's emotions in real time and provides personalized services accordingly. For example, if the emotional state indicates the user is experiencing high stress, it can display content with a relaxing effect.

[0663] Users use information obtained through their devices to check the situation and send their opinions and impressions to the server through feedback. This feedback is analyzed by an emotion engine and used to improve the service and optimize further support plans. User feedback becomes a new data source, contributing to improved analysis accuracy.

[0664] As a concrete example, a server might send an instruction to a terminal saying, "Provide this customer with music to help them relax." An example of a prompt using a generative AI model would be, "Analyze the customer's facial expression data to determine if they are relaxed. If they are tense, tell me what you should suggest to them."

[0665] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0666] Step 1:

[0667] The server receives crustal deformation data, climate data, and sea level information from satellite observation and sensor devices. Raw data is acquired as input and preprocessed. By removing noise and imputing missing parts of the data, a clean dataset is output.

[0668] Step 2:

[0669] The server feeds pre-processed data into an AI analysis algorithm to detect anomaly patterns. The input is clean, pre-processed data, and the AI ​​model analyzes this data to output the anomaly detection results. A generative AI model is utilized in this process.

[0670] Step 3:

[0671] The server aggregates detected anomaly patterns into a database and updates it in real time on the cloud. The input is the result of anomaly detection, and aggregation into the database and cloud updates are performed to efficiently manage this data.

[0672] Step 4:

[0673] The terminal receives alarm information and support plans sent from the server and notifies the user. The input is alarm information and support plans from the server, and the output is in the form of a notification to the user. This allows the user to immediately obtain guidance on what to do.

[0674] Step 5:

[0675] The device recognizes the user's emotions and provides personalized services. Input consists of the user's facial expressions and voice data, which are analyzed by an emotion engine. Output includes relaxing music and messages. Prompts generated using a generative AI model select the most appropriate content based on the user's situation.

[0676] Step 6:

[0677] Users check the current status based on information obtained through their devices and send feedback to the server. The input consists of services and information from the device, which are returned to the server as feedback. This feedback is then analyzed by the server and contributes to improving the support plan.

[0678] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0679] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0680] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0681] [Fourth Embodiment]

[0682] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0683] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0684] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0685] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0686] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0687] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0688] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0689] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0690] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0691] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0692] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0693] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0694] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0695] This invention is a system for enabling rapid and accurate information gathering and support activities in the event of a natural disaster. Specific embodiments are described below.

[0696] This system is primarily composed of the following elements: "server," "terminal," and "user."

[0697] Server roles and processing

[0698] The server is responsible for receiving various data from satellite observation and sensor devices. The received data relates to natural phenomena such as crustal deformation, climate change, and sea level information. This data is processed to prepare it for analysis from its raw state, undergoing noise reduction and data completion.

[0699] Subsequently, the server processes the data using AI analysis algorithms to detect anomaly patterns. For example, if an anomaly is detected in crustal deformation data, the server evaluates the possibility that the anomaly is a precursor to an earthquake and predicts the disaster risk.

[0700] The analysis results are aggregated in a database and updated in real time. This updated information is uploaded to the cloud and made accessible to relevant organizations and users. The server also automatically generates efficient support plans based on this information and provides them to relevant organizations. This ensures optimal allocation of supplies and expedites support activities.

[0701] Terminal role

[0702] The terminal receives alarm information transmitted from the server and notifies the user of that information. The user can then check these alarms via their smartphone or computer and take emergency action as needed. In this way, the terminal plays a crucial role in supporting on-site responses.

[0703] User roles and processes

[0704] When users conduct on-site support activities, they send feedback from the field to the server. For example, they might report the actual extent of damage or the shortage of supplies via their devices. This feedback information becomes important data for improving the accuracy of the server's analysis.

[0705] Thus, the present invention provides a specific system configuration for achieving an efficient response to natural disasters.

[0706] The following describes the processing flow.

[0707] Step 1:

[0708] The server periodically receives data from satellite observation and sensor devices. Specifically, it acquires data including crustal deformation data, climate data, and sea level information.

[0709] Step 2:

[0710] The server preprocesses the received raw data. Specifically, it performs processes to fill in missing data and remove noise, preparing the data for analysis.

[0711] Step 3:

[0712] The server feeds pre-processed data into an AI analysis algorithm to detect anomalous patterns and signs of disaster. This analysis uses a machine learning model based on historical data, and if an anomaly is detected, its location and the risk level of its occurrence are evaluated.

[0713] Step 4:

[0714] The server aggregates the analysis results into a database and updates it to the cloud in real time. This information will be made available to relevant organizations and users through access.

[0715] Step 5:

[0716] The device receives alarm information transmitted from the server and sends emergency notifications to the user. The device uses push notifications to deliver warnings such as earthquakes and floods to the user, prompting them to take prompt action.

[0717] Step 6:

[0718] The server automatically generates optimal support plans based on the analysis data and provides them to relevant aid organizations and agencies. These plans include distribution routes for supplies and the placement of shelters.

[0719] Step 7:

[0720] Users provide feedback to the server based on information gathered through on-site situation assessments and support activities. This feedback is reported via smartphones and other devices and is used to continuously improve the system and enhance the accuracy of the analysis.

[0721] (Example 1)

[0722] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0723] Conventional natural disaster response systems suffered from delays in processing data after reception, making it difficult to quickly generate effective support plans. Furthermore, they were unable to adequately detect anomalies such as crustal movements and weather changes in real time and conduct risk assessments, resulting in delays in immediate response during disasters. In addition, there was a lack of mechanisms to effectively incorporate feedback from the field to improve analysis accuracy.

[0724] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0725] In this invention, the server includes means for receiving data from observation equipment, means for preprocessing the received data to fill in missing data and remove noise, and means for analyzing the preprocessed data to detect abnormal patterns. This makes it possible to efficiently and quickly perform everything from data reception and analysis to alarm notification and support plan generation. Furthermore, by utilizing on-site feedback to continuously improve analysis accuracy, more appropriate disaster response can be achieved.

[0726] "Observation equipment" refers to devices used to monitor natural phenomena and collect data on them. Examples include satellites, seismometers, and weather sensors.

[0727] "Preprocessing" refers to the process of preparing raw data for analysis by removing noise and imputing missing information.

[0728] An "anomalous pattern" refers to a portion of the data being analyzed that exhibits unusual movements or changes that deviate from the normal range.

[0729] "Risk assessment" involves analyzing the likelihood that detected abnormal patterns could lead to disasters and quantifying that likelihood.

[0730] An "information repository" refers to a database used to collect and store data within a system.

[0731] An "information network" refers to the entire network of communications used to share data with users and related organizations.

[0732] A "mobile communication terminal" is a device that receives and allows users to view mobile information, and includes smartphones and tablets.

[0733] A "support plan" refers to a plan generated based on analytical data to efficiently carry out actions such as the distribution of supplies and the dispatch of personnel in the event of a disaster.

[0734] "Feedback" refers to information that reports on reactions and areas for improvement, such as the situation on the ground and the results of support activities.

[0735] "Geospatial technology" refers to technologies for processing and analyzing geographical information, including mapmaking and spatial analysis.

[0736] This system invention has a complex configuration including observation equipment, servers, terminals, and users, in order to realize rapid and accurate information gathering and support activities in response to natural disasters.

[0737] The server functions as a central integrator, receiving and efficiently processing data from observation instruments. These instruments include satellites, seismometers, weather sensors, and river level gauges, from which data on natural phenomena such as crustal deformation, weather patterns, and water levels are acquired. The received data is preprocessed using dedicated analysis software. This software utilizes noise reduction algorithms and missing data imputation techniques to improve data accuracy. Furthermore, by integrating AI analysis algorithms, it detects anomalous patterns and assesses disaster risk. The analysis results are aggregated in a database and then distributed in real time to relevant organizations and users via a cloud-based information network.

[0738] The terminal functions as a means of providing users with alert information. Users can receive alerts via smartphones or computers, enabling them to respond quickly. Along with alert notifications, the system automatically generates support plans based on analysis results and sends them to relevant organizations, thereby streamlining support activities such as supply distribution and evacuation guidance.

[0739] Users are responsible for transmitting feedback from the field to the server via their devices. This includes detailed reports on support activities carried out on-site and up-to-date information on the extent of the damage, which improves the accuracy of the server's analysis.

[0740] As a concrete example, when a typhoon occurs, the server analyzes data such as wind speed, precipitation, and trajectory obtained from observation equipment to predict areas that may be affected. Based on this information, swift evacuation orders are sent to residents via terminals. Furthermore, the generated support plan is used for distributing relief supplies and determining where to dispatch personnel.

[0741] Examples of prompt messages include specific instructions such as, "Analyze the data from the following sensors, identify anomaly patterns, and assess the risk level. Use the AI ​​model to generate the optimal support plan."

[0742] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0743] Step 1:

[0744] The server receives data such as crustal deformation, weather patterns, and water level information from observation instruments. This data is raw observational data and requires preprocessing to improve accuracy. The input is raw data, and the output is data suitable for preprocessing.

[0745] Step 2:

[0746] The server preprocesses the received data. Specifically, it removes noise from the data using a denoising algorithm and estimates and fills in missing data using missing data imputation techniques. This process outputs clean data suitable for analysis.

[0747] Step 3:

[0748] The server inputs clean data into an AI analysis algorithm to detect anomalous patterns. If unusual movements are observed in the crustal deformation data, these movements are identified as anomalies, and the disaster risk is assessed. The output after analysis is the anomalous pattern and its risk level.

[0749] Step 4:

[0750] The server aggregates the analysis results into a database and transmits them over the information network in real time. This information effectively serves as a risk dashboard, making it immediately accessible to users and relevant organizations. The output exists as accessible real-time data.

[0751] Step 5:

[0752] The terminal receives alarm information sent from the server and notifies the user. For example, in the event of a potential earthquake, it immediately warns users living in a specific area. The input is alarm data, and the output is an alert notification to the user.

[0753] Step 6:

[0754] The server generates a support plan based on the analysis data and automatically transmits it to the relevant organizations. This support plan includes actionable guidelines regarding the distribution routes of supplies and the optimal allocation of resources. The output is a concrete and actionable support plan.

[0755] Step 7:

[0756] Users provide feedback to the server via their terminals, based on information gathered on-site. They send detailed reports to the server, including the local situation, the results of the support provided, and areas for improvement. This feedback is used for re-evaluation, promoting continuous improvement in analysis accuracy. The input is on-site feedback information, and the output is the improved analysis results.

[0757] (Application Example 1)

[0758] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0759] During natural disasters, rapid and accurate information gathering and support activities are crucial. However, conventional systems struggle to efficiently process large amounts of data, provide real-time information, and optimize support plans. In particular, there is a lack of safe and optimal route calculations for moving objects during disasters, posing a risk of delays in support activities. A system is needed to solve these problems and enable more effective disaster response.

[0760] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0761] In this invention, the server includes means for receiving data from satellite observation devices and sensor devices; means for preprocessing the received data to fill in gaps and remove noise; means for analyzing the preprocessed data and detecting abnormal patterns; means for aggregating the analysis results in a database and updating the information infrastructure in real time; means for notifying terminal devices of alarm information; means for automatically generating support plans based on the analysis data and transmitting them to relevant organizations; means for receiving return information obtained from on-site responses and improving analysis accuracy; and means for optimizing the trajectory of moving objects and automatically calculating safe routes based on obstacle information. This enables accurate information provision in real time and safe delivery of support supplies.

[0762] A "satellite observation device" is a device installed on an artificial satellite orbiting the Earth that observes data on the Earth's surface and weather, and transmits it to the ground as a signal.

[0763] A "sensor device" is a device used to measure physical phenomena and environmental conditions and acquire corresponding data. For example, it is used to detect crustal movements and changes in weather.

[0764] "Preprocessing" refers to the process of preparing raw observational data into a format suitable for analysis, and includes tasks such as imputing missing data and removing noise.

[0765] "Analysis" refers to data processing performed on received and pre-processed data to detect specific patterns or anomalies.

[0766] A "database" is a system that centrally manages structured information and allows for rapid access and updating.

[0767] "Information infrastructure" refers to the fundamental systems and networks for processing and utilizing data safely and quickly.

[0768] "Warning information" refers to important information regarding disasters or anomalies that is sent to devices to encourage a quick response.

[0769] A "support plan" is a plan that automatically generates action guidelines and resource allocations for appropriate disaster response.

[0770] "Related organizations" refers to various groups and institutions involved in disaster response and support activities, with the aim of ensuring effective coordination among them.

[0771] "On-site response" refers to the specific actions taken at actual disaster sites, and the related feedback information is used to improve overall accuracy.

[0772] "Return information" refers to performance data and feedback obtained from the field, and this information is used to improve the accuracy of the system.

[0773] A "mobile vehicle" refers to an unmanned or manned vehicle used for transporting supplies or gathering information during a disaster.

[0774] A "safe route" is the safest and most efficient path that a moving object should take, taking into account various environments and circumstances.

[0775] "Track optimization" refers to the process of optimizing the route plan so that a moving object can reach its destination efficiently and safely.

[0776] The system of this invention consists of multiple devices and programs designed to quickly and accurately collect information during natural disasters and to streamline support activities. At the heart of the system is a server that processes data received from satellite observation and sensor devices. The server uses specialized software to preprocess large amounts of raw data, filling in gaps and removing noise. Next, the preprocessed data is analyzed using AI analysis algorithms to detect anomalous patterns. Machine learning platforms such as TensorFlow are used in this process.

[0777] The analyzed data is aggregated in real time in a database on the information infrastructure, and alert information is automatically sent to the relevant organizations and institutions. Terminals receive this alert information and notify the user. Users send feedback to the server as information upon their return from the field, contributing to improving the accuracy of the system's analysis.

[0778] Furthermore, in the operation of the mobile vehicles, the server utilizes algorithms that incorporate obstacle and geographical information to optimize safe and efficient routes. This enables autonomous vehicles to safely deliver supplies during disasters.

[0779] As a concrete example, in the delivery of relief supplies during a disaster, the system calculates the optimal route considering road closure information and weather conditions. Autonomous vehicles then efficiently deliver supplies based on this route, accelerating relief efforts. An example of a prompt using a generative AI model is: "Calculate the latest route to the disaster area and optimize safe and efficient supply delivery. Consider current location and traffic information, and suggest a route that avoids obstacles."

[0780] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0781] Step 1:

[0782] The server receives raw data, including crustal deformation, weather, and water level information, from satellite observation and sensor devices. The input is the raw data, and the output is the pre-processed result of the data. In this step, the server performs noise reduction and missing data imputation to prepare the data for analysis. Specifically, noise is removed using a filtering algorithm, and missing values ​​are filled in by an imputation algorithm.

[0783] Step 2:

[0784] The server processes pre-processed data through an AI analysis algorithm to detect anomalous patterns. The input is pre-processed data, and the output is the analysis result of the anomalous patterns. The server uses machine learning models to analyze anomalous trends within the data and assess potential disaster risks. In particular, it utilizes TensorFlow to predict earthquake and flood precursors.

[0785] Step 3:

[0786] The server aggregates the analysis results into a database on the information infrastructure and updates them in real time via a cloud service. The input is the analysis results, and the output is notification information for relevant organizations. The analysis results are immediately uploaded to the cloud and made available for relevant organizations to access at any time. Specifically, the update work is performed using a database update API.

[0787] Step 4:

[0788] The terminal checks information in the cloud and notifies the user of important alerts. The input is alert information retrieved from the cloud, and the output is an alert notification to the user. The terminal uses a notification mechanism to send warnings to smartphones and dedicated devices. At this time, it is provided with a user-friendly interface so that the user can immediately understand the situation.

[0789] Step 5:

[0790] The server calculates the optimal route for the moving object based on the analysis data and obstacle information. The input for this step is the analysis data, and the output is the optimal route. The route calculation incorporates real-time map data through integration with a geographic information system, and also takes traffic conditions and weather into consideration. The calculated route is then transmitted to the autonomous vehicle.

[0791] Step 6:

[0792] The autonomous vehicle receives the optimal route transmitted from the server and safely delivers the relief supplies. The input is the optimal route information from the server, and the output is the result of the supplies delivery. Specifically, the automated control system accurately guides the vehicle to the target location, and sensors are used to avoid obstacles.

[0793] Step 7:

[0794] Users send feedback to the server regarding the local situation and any additional requests after the delivery of supplies. The input is information collected on-site, and the output is feedback information. Users submit feedback using a smart device with simple operations. This feedback is used to improve the accuracy of the system's analysis and is utilized in subsequent support activities.

[0795] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0796] This invention relates to a system for facilitating rapid information gathering and support activities during natural disasters, and further provides more personalized support by incorporating an emotion engine that recognizes the user's emotions.

[0797] The system is primarily composed of three elements: "server," "terminal," and "user."

[0798] Server roles and processing

[0799] The server receives crustal deformation data, climate data, water level information, etc., from satellite observation and sensor devices, and preprocesses this data. This processing includes denoising and imputing missing data. Subsequently, the preprocessed data is analyzed using an AI analysis algorithm to detect anomaly patterns.

[0800] The server aggregates the analysis results into a database and updates them to the cloud in real time. This data becomes immediately available to users through relevant organizations and devices, enabling the automated generation of efficient support plans. Crucially, this involves the use of an emotion engine. The server receives feedback from users and analyzes the text and audio data within that feedback using the emotion engine to understand the user's emotional state.

[0801] Based on the results of the emotion analysis, the server adjusts the support plan and provides the user with the most appropriate information. For example, if the user is experiencing high levels of stress, it can provide a more reassuring message.

[0802] Terminal role

[0803] The device receives alert information and optimized support plans transmitted from the server and notifies the user. Furthermore, it can provide content to promote relaxation based on the results of the emotion engine. This reduces the user's psychological burden and encourages a quick and appropriate response.

[0804] User roles and processes

[0805] Users use their devices to check the situation on-site and send feedback to the server as needed. This feedback can include written text or voice messages, which are analyzed in detail by an emotion engine. This allows for personalized support and enables rapid decision-making.

[0806] In this way, by incorporating an emotion engine, the system provides flexible and effective support that takes into account the user's emotional state.

[0807] The following describes the processing flow.

[0808] Step 1:

[0809] The server receives data in real time from satellite observation and sensor devices. This data includes information on crustal deformation, climate change, and sea level.

[0810] Step 2:

[0811] The server preprocesses the received data. Specifically, it fills in missing parts, removes noise, and prepares the data for analysis.

[0812] Step 3:

[0813] The server processes pre-processed data using an AI analysis algorithm to detect anomalous patterns. This analysis allows for the early identification of natural disaster risks.

[0814] Step 4:

[0815] The server aggregates the analysis results into a cloud-based database and updates it in real time. This makes the data accessible to relevant organizations and users.

[0816] Step 5:

[0817] The server receives feedback from users. This feedback can be sent in text or audio format and is analyzed by an emotion engine.

[0818] Step 6:

[0819] The server uses an emotion engine to recognize emotions from user feedback. The analysis results are used to understand the user's psychological state.

[0820] Step 7:

[0821] The server adjusts the support plan based on the emotion analysis results. For users experiencing high stress levels, messages and information designed to provide reassurance are generated.

[0822] Step 8:

[0823] The device notifies the user of alert information and optimized support plans sent from the server. Furthermore, it provides content that promotes relaxation based on the user's emotions.

[0824] Step 9:

[0825] Users review the information provided on their devices and take action as needed. User feedback is sent to the server in the next cycle to help the system make decisions.

[0826] (Example 2)

[0827] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0828] While there is a need for rapid and appropriate information gathering and smooth support activities during natural disasters, conventional systems have challenges in real-time data processing and flexible support based on user sentiment, resulting in a lack of measures tailored to individual needs.

[0829] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0830] In this invention, the server includes means for receiving data from observation and detection devices, means for preprocessing the received data to fill in missing data and remove noise, and means for analyzing the preprocessed data to detect abnormal patterns. This enables real-time, high-precision data analysis, and by utilizing an emotion analysis engine that analyzes user emotions, it becomes possible to provide flexible and effective support tailored to individual needs.

[0831] An "observation device" is a device installed on satellites or on the ground to quantitatively capture changes in the environment or the Earth's surface.

[0832] A "detection device" is a device that senses specific phenomena, physical quantities, or environmental changes and acquires data related to them.

[0833] "Preprocessing" refers to the process of preparing data for analysis by performing tasks such as noise reduction, data interpolation, and format conversion.

[0834] "Noise" refers to unwanted information contained in data that negatively impacts the analysis results.

[0835] An "anomalous pattern" is a data trend that deviates from normal data and exhibits unusual behavior or phenomena.

[0836] An "information management device" is a device or system for aggregating data and storing and organizing analysis results.

[0837] An "information processing infrastructure" is a platform for instantly managing, processing, and sharing data on the cloud or network.

[0838] A "computing device" is a device that electronically processes information and provides that information to the user.

[0839] A "support plan" is an action plan created to efficiently provide the necessary support for a specific situation.

[0840] An "emotion analysis engine" is a system that extracts emotional information from text and audio to analyze the user's emotional state.

[0841] "User emotional state" refers to the mental reactions and sensitivities that a user is experiencing.

[0842] The embodiment of the invention is a system that enables rapid and effective information gathering and support during natural disasters. This system is constructed using observation devices, detection devices, servers, terminals, and generative AI models.

[0843] The server acquires data in real time from observation and detection devices. The received data is preprocessed to remove noise and impute missing data. This preprocessing uses statistical methods and filtering techniques. After preprocessing is complete, the server analyzes the data using AI analysis algorithms to detect anomalous patterns. The AI ​​analysis algorithms used typically include machine learning or deep learning techniques.

[0844] The analysis results are aggregated in an information management device and updated in real time on the information processing infrastructure. The terminal functions as a computing device and notifies the user of alarms and optimized support plans transmitted from the server. Furthermore, it utilizes an emotion analysis engine to analyze user feedback and adjust the support plan based on the user's emotional state.

[0845] Users check the local situation through their devices and send feedback to the server in voice or text format. This feedback is further analyzed by an emotion analysis engine to provide optimal information tailored to the user's individual needs.

[0846] A concrete example is the rapid analysis of crustal deformation and climate data in specific areas during disasters, and the provision of evacuation orders based on that analysis. An example of a prompt for the generating AI model is, "Please show the data processing flow for creating an appropriate support plan during a disaster."

[0847] This system enables a rapid and appropriate response in specific disaster situations, improving user safety and peace of mind.

[0848] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0849] Step 1:

[0850] The server receives crustal deformation data, meteorological data, and water level information in real time from observation and detection devices. Input is raw data from each device, and output is a collection of raw data. The data is converted into a structured format and prepared for further processing.

[0851] Step 2:

[0852] The server preprocesses the received raw data. It applies noise reduction filtering to remove outliers. Furthermore, it uses statistical methods to impute missing data. The preprocessed data is generated as output and prepared for analysis. As a specific example, data lost due to sensor communication failures is supplemented with average values ​​from a surrounding database.

[0853] Step 3:

[0854] The server inputs pre-processed data into an AI analysis algorithm to detect anomalous patterns. The input is a clean dataset, and the output is a list of detected anomalous patterns. The detection process uses machine learning models and employs pattern recognition techniques. For example, it can automatically identify waveform patterns that indicate an impending earthquake.

[0855] Step 4:

[0856] The server aggregates the analysis results in the information management device and updates the data in the information processing infrastructure in real time. The input is a list of abnormal patterns, and the output is an updated database. This allows relevant organizations and terminals to access the new information immediately.

[0857] Step 5:

[0858] The terminal receives analysis results and support plans from the server. The input is optimization information sent from the server, and the output is alarms and support plans notified to the user. The terminal immediately notifies the user and, if necessary, alerts them with an alarm sound or vibration.

[0859] Step 6:

[0860] Users check the on-site situation and send feedback to the server via their terminal. Input is text or audio describing the on-site situation, and output is user feedback data. The feedback is transcribed into text using speech recognition technology and used for sentiment analysis.

[0861] Step 7:

[0862] The server analyzes user feedback using an emotion analysis engine to assess the user's emotional state. The input is text data of the feedback, and the output is an evaluation result indicating the emotional state. Based on these results, the support plan is adjusted to match the user's psychological state. For example, information that provides reassurance is added for users who are feeling anxious.

[0863] (Application Example 2)

[0864] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0865] In today's information society, the rapid and accurate provision of information during natural disasters is essential for social stability. However, conventional systems provide uniform information without considering the individual emotional state of users, making it difficult to provide support tailored to individual circumstances. Therefore, there is a need to develop systems that reduce the psychological burden on users during disasters and provide more accurate information.

[0866] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0867] In this invention, the server includes means for receiving data from satellite observation devices and sensor devices; means for preprocessing the received data to fill in missing data and remove noise; means for analyzing the preprocessed data and detecting abnormal patterns; means for aggregating the analysis results in a database and updating it in real time on the cloud; means for notifying terminal devices of alarm information; means for automatically generating support plans based on the analysis data and transmitting them to relevant organizations; means for receiving feedback obtained from on-site responses and improving analysis accuracy; and means for recognizing emotions using a personal display device and providing personalized services based on the analysis results. This enables the provision of rapid and personalized information and support in accordance with the user's emotional state.

[0868] A "satellite observation device" is a device installed on an artificial satellite orbiting the Earth to observe the conditions of the Earth's surface and atmosphere.

[0869] A "sensor device" is a device that detects physical phenomena or states and outputs them as digital data.

[0870] "Preprocessing" refers to steps such as data imputation and noise reduction performed to improve the accuracy of data analysis.

[0871] An "anomalous pattern" is a data pattern that shows unusual movements or states that differ from normal observation data.

[0872] A "database" is a structured collection of data used to efficiently store, search, and manage information.

[0873] "Real-time updating" is an update method in which information is instantly reflected in the database or cloud the moment it is generated.

[0874] A "terminal device" is a device used by users to receive and manipulate digital information.

[0875] "Warning information" refers to information that is sent out to quickly draw attention when danger or abnormality occurs.

[0876] A "support plan" is a plan for providing support and assistance in a specific situation efficiently and effectively.

[0877] "Feedback" is the act of returning a response or evaluation of the information or service received to the sender.

[0878] "Personalized service" refers to the provision of services that are customized according to each individual's characteristics and circumstances.

[0879] "Emotional recognition" means analyzing and interpreting the user's emotional state from their facial expressions and voice.

[0880] To implement this system, it is necessary to build a network environment that handles information between servers, terminals, and users.

[0881] The server receives surface and weather data acquired from satellite observation and sensor devices. This data is often raw and therefore unsuitable for direct analysis. First, preprocessing is performed to remove noise and fill in missing data. This improves the quality of the data and increases the accuracy of the analysis. The preprocessed data is then analyzed by AI analysis algorithms to attempt to detect anomaly patterns. If an anomaly is detected, the information is aggregated in a database in real time and immediately updated on the cloud.

[0882] The terminal receives alert information and support plans from the server and notifies the user. Personalized display devices used here include smart glasses. The terminal recognizes the user's emotions in real time and provides personalized services accordingly. For example, if the emotional state indicates the user is experiencing high stress, it can display content with a relaxing effect.

[0883] Users use information obtained through their devices to check the situation and send their opinions and impressions to the server through feedback. This feedback is analyzed by an emotion engine and used to improve the service and optimize further support plans. User feedback becomes a new data source, contributing to improved analysis accuracy.

[0884] As a concrete example, a server might send an instruction to a terminal saying, "Provide this customer with music to help them relax." An example of a prompt using a generative AI model would be, "Analyze the customer's facial expression data to determine if they are relaxed. If they are tense, tell me what you should suggest to them."

[0885] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0886] Step 1:

[0887] The server receives crustal deformation data, climate data, and sea level information from satellite observation and sensor devices. Raw data is acquired as input and preprocessed. By removing noise and imputing missing parts of the data, a clean dataset is output.

[0888] Step 2:

[0889] The server feeds pre-processed data into an AI analysis algorithm to detect anomaly patterns. The input is clean, pre-processed data, and the AI ​​model analyzes this data to output the anomaly detection results. A generative AI model is utilized in this process.

[0890] Step 3:

[0891] The server aggregates detected anomaly patterns into a database and updates it in real time on the cloud. The input is the result of anomaly detection, and aggregation into the database and cloud updates are performed to efficiently manage this data.

[0892] Step 4:

[0893] The terminal receives alarm information and support plans sent from the server and notifies the user. The input is alarm information and support plans from the server, and the output is in the form of a notification to the user. This allows the user to immediately obtain guidance on what to do.

[0894] Step 5:

[0895] The device recognizes the user's emotions and provides personalized services. Input consists of the user's facial expressions and voice data, which are analyzed by an emotion engine. Output includes relaxing music and messages. Prompts generated using a generative AI model select the most appropriate content based on the user's situation.

[0896] Step 6:

[0897] Users check the current status based on information obtained through their devices and send feedback to the server. The input consists of services and information from the device, which are returned to the server as feedback. This feedback is then analyzed by the server and contributes to improving the support plan.

[0898] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0899] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0900] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0901] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0902] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0903] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0904] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0905] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0906] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0907] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0908] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0909] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0910] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0911] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0912] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0913] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0914] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0915] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0916] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0917] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0918] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0919] The following is further disclosed regarding the embodiments described above.

[0920] (Claim 1)

[0921] Means for receiving data from satellite observation equipment and sensor equipment,

[0922] A means of preprocessing the received data to fill in missing data and remove noise,

[0923] A means for analyzing pre-processed data and detecting abnormal patterns,

[0924] A method for aggregating analysis results in a database and updating it in real time on the cloud,

[0925] A means of notifying a terminal device of alarm information,

[0926] A means of automatically generating support plans based on analysis data and sending them to relevant organizations,

[0927] A means of receiving feedback obtained from on-site operations and improving the accuracy of the analysis,

[0928] A system that includes this.

[0929] (Claim 2)

[0930] The system according to claim 1, which includes means for creating distribution routes for supplies and layout maps of evacuation shelters in cooperation with a geographic information system using data received from satellite observation devices and sensor devices.

[0931] (Claim 3)

[0932] The system according to claim 1, characterized in that the data received includes crustal deformation data, climate data, and water level information.

[0933] "Example 1"

[0934] (Claim 1)

[0935] A means of receiving data from observation equipment,

[0936] A means of preprocessing the received data to fill in missing data and remove noise,

[0937] A means for analyzing pre-processed data and detecting abnormal patterns,

[0938] A means of assessing the risk associated with disasters based on abnormal patterns,

[0939] A means of aggregating analysis results in an information repository and updating them in real time on an information network,

[0940] A means of notifying a mobile communication terminal of an alarm,

[0941] A means of automatically generating a support plan based on analysis data and sending it to relevant organizations,

[0942] A means of receiving feedback obtained from on-site operations and improving the accuracy of the analysis,

[0943] A system that includes this.

[0944] (Claim 2)

[0945] The system according to claim 1, which includes means for creating distribution routes for supplies and layout maps of evacuation shelters in conjunction with geospatial technology based on received data.

[0946] (Claim 3)

[0947] The system according to claim 1, characterized in that the data received includes crustal deformation data, meteorological data, and water level information.

[0948] "Application Example 1"

[0949] (Claim 1)

[0950] Means for receiving data from satellite observation equipment and sensor equipment,

[0951] A means of preprocessing the received data to fill in missing data and remove noise,

[0952] A means for analyzing pre-processed data and detecting abnormal patterns,

[0953] A means of aggregating analysis results into a database and updating the information infrastructure in real time,

[0954] A means of notifying a terminal device of alarm information,

[0955] A means of automatically generating support plans based on analysis data and sending them to relevant organizations,

[0956] A means to improve the accuracy of analysis by receiving return information obtained from on-site response,

[0957] A method for optimizing the trajectory of a moving object and automatically calculating a safe route based on obstacle information,

[0958] A system that includes this.

[0959] (Claim 2)

[0960] The system according to claim 1, which includes means for creating distribution routes for supplies and location maps of evacuation shelters in cooperation with a geographic information system using data received from satellite observation devices and sensor devices.

[0961] (Claim 3)

[0962] The system according to claim 1, characterized in that the data received includes crustal deformation data, meteorological data, and water level information.

[0963] "Example 2 of combining an emotion engine"

[0964] (Claim 1)

[0965] Means for receiving data from observation devices and detection devices,

[0966] A means of preprocessing the received data to fill in missing data and remove noise,

[0967] A means for analyzing pre-processed data and detecting abnormal patterns,

[0968] A means of aggregating analysis results in an information management device and immediately updating them on the information processing infrastructure,

[0969] A means for notifying a computing device of alarm information,

[0970] A means of automatically generating a support plan based on analysis information and sending it to the relevant organizations,

[0971] A means of receiving feedback obtained from on-site operations and improving the accuracy of the analysis,

[0972] A means of further analyzing feedback using an emotion analysis engine that analyzes user emotions, and adjusting the support plan based on the user's emotional state,

[0973] A system that includes this.

[0974] (Claim 2)

[0975] The system according to claim 1, comprising means for creating distribution routes for supplies and layout maps of evacuation shelters in cooperation with a geographic information system using data received from observation devices and detection devices.

[0976] (Claim 3)

[0977] The system according to claim 1, characterized in that the data received includes crustal deformation data, climate data, and water level information.

[0978] "Application example 2 when combining with an emotional engine"

[0979] (Claim 1)

[0980] Means for receiving data from satellite observation equipment and sensor equipment,

[0981] A means of preprocessing the received data to fill in missing data and remove noise,

[0982] A means for analyzing pre-processed data and detecting abnormal patterns,

[0983] A method for aggregating analysis results in a database and updating it in real time on the cloud,

[0984] A means of notifying a terminal device of alarm information,

[0985] A means of automatically generating support plans based on analysis data and sending them to relevant organizations,

[0986] A means of receiving feedback obtained from on-site operations and improving the accuracy of the analysis,

[0987] A means of recognizing emotions using a personal display device and providing personalized services based on the analysis results,

[0988] A system that includes this.

[0989] (Claim 2)

[0990] The system according to claim 1, comprising means for using data received from satellite observation devices and sensor devices to create a map of physical resource distribution routes and shelter layout plans in cooperation with a geographic information system.

[0991] (Claim 3)

[0992] The system according to claim 1, characterized in that the data received includes crustal deformation data, climate data, and water level information. [Explanation of symbols]

[0993] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for receiving data from satellite observation equipment and sensor equipment, A means of preprocessing the received data to fill in missing data and remove noise, A means for analyzing pre-processed data and detecting abnormal patterns, A method for aggregating analysis results in a database and updating it in real time on the cloud, A means of notifying a terminal device of alarm information, A means of automatically generating support plans based on analysis data and sending them to relevant organizations, A means of receiving feedback obtained from on-site operations and improving the accuracy of the analysis, A system that includes this.

2. The system according to claim 1, which includes means for creating distribution routes for supplies and layout maps of evacuation shelters in cooperation with a geographic information system using data received from satellite observation devices and sensor devices.

3. The system according to claim 1, characterized in that the data received includes crustal deformation data, climate data, and water level information.

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