system

Abstract

We provide the system. [Solution] Means of using flying or ground equipment to collect environmental data in real time, A means of using an artificial intelligence algorithm for analyzing the aforementioned collected environmental data, A means for predicting environmental changes and disaster risks based on the aforementioned analysis results, A means for issuing real-time warnings for the aforementioned predicted risks, A means of automatically generating environmental reports on a weekly or monthly basis, A means of displaying analysis results on a mobile device and encouraging users to adjust their behavior based on environmental information, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, environmental problems on a global scale have been intensifying. To aim for a sustainable future, rapid and accurate monitoring of environmental data and appropriate measures are necessary. However, in conventional environmental monitoring systems, the immediacy of data collection and the accuracy of analysis processing are insufficient, and it is difficult to appropriately predict environmental changes and disaster risks. As a result, there is a problem that it is impossible to take feasible measures even in situations where rapid response is required.

Means for Solving the Problems

[0005] This invention solves the problem by providing means for collecting environmental data in real time using aircraft or ground equipment, and means for analyzing the collected data with artificial intelligence algorithms to predict environmental changes and disaster risks. Furthermore, by providing means for issuing real-time warnings for risks predicted based on the analysis results, rapid response is possible. In addition, by automatically generating environmental reports on a weekly or monthly basis, it supports the promotion of sustainable activities. This realizes a comprehensive and effective monitoring and countermeasure system for environmental problems.

[0006] An "aircraft" is a device designed to fly through the air, and includes drones and manned aircraft.

[0007] "Ground equipment" refers to devices installed on the ground to directly collect environmental data, and includes sensor network systems.

[0008] "Environmental data" refers to information that indicates the state of the global environment, and includes measured values ​​such as temperature, humidity, CO2 concentration, and soil moisture.

[0009] An "artificial intelligence algorithm" is a set of procedures modeled by a computer program that can analyze data and perform pattern recognition and prediction.

[0010] "Analysis" is the process of thoroughly examining collected data and extracting information, using statistical methods and machine learning.

[0011] "Environmental change" refers to the phenomenon in which the environment changes over time due to natural or human factors.

[0012] "Disaster risk" refers to the likelihood of natural disasters or environmental hazards occurring and the degree of impact they may have.

[0013] "Real-time warning" means detecting an abnormal situation and immediately notifying the relevant parties.

[0014] An "environmental report" is a document that summarizes analysis results and prediction information and is generated periodically.

[0015] "Sustainable activities" refer to actions and measures that are environmentally friendly and contribute to society and the economy from a long-term perspective.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 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 Example 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.

Embodiments for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the 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, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one 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), etc.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) 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 numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[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 provides a system and method for collecting environmental data in real time and analyzing that data. One embodiment involves efficiently collecting data using aircraft or ground equipment, making predictions and warnings based on that data, and finally generating a report.

[0038] The server controls the drone based on pre-configured parameters, measuring temperature, humidity, CO2 concentration, and other parameters from above the target area. A large network of sensors is deployed on the ground to collect detailed environmental data. This collected data is then transmitted to the server.

[0039] The terminal receives data from the server and performs initial filtering. This removes inaccurate or unnecessary data, ensuring that analysis can be performed effectively.

[0040] The server analyzes the received data using artificial intelligence algorithms. The analysis detects anomalous patterns by comparing them with past data, and identifies trends in environmental changes. It also uses machine learning models to predict future weather conditions and disaster risks.

[0041] If the forecast indicates an increased risk of extreme weather or disaster, the server will promptly issue a real-time warning. The warning will include specific countermeasures to encourage users to take swift action.

[0042] Based on the warnings received, users can adjust their crop management methods or decide to suspend activities in specific areas. The server then provides users with regularly generated weekly and monthly environmental reports, allowing them to review past conditions and plan future actions.

[0043] This system enables effective management of environmental conservation activities and supports rapid decision-making. For example, it allows for early detection of high-temperature stress in agricultural areas and the implementation of improved water management.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server sends data collection instructions to aircraft and ground equipment based on a pre-configured schedule. The aircraft follows the designated flight path, and ground equipment measures various environmental parameters, collecting data in real time.

[0047] Step 2:

[0048] The server receives the collected raw data and performs initial processing. This initial processing involves detecting outliers and filtering out data that does not need to be processed.

[0049] Step 3:

[0050] The server passes the initially processed data to an artificial intelligence algorithm for advanced data analysis. This analysis specifically detects unusual fluctuations and patterns, generating data to visualize environmental conditions.

[0051] Step 4:

[0052] Based on the analysis results, the server uses machine learning models to predict future environmental conditions and disaster risks. These predictions include short-term weather forecasts and long-term environmental change forecasts.

[0053] Step 5:

[0054] Based on predictions, the server generates and sends real-time warning messages about potential risks to the terminal. The warnings include details of the risk and suggested countermeasures.

[0055] Step 6:

[0056] The terminal notifies the user of any received warning messages. The user can then check the details of the warning via the terminal and take necessary action promptly.

[0057] Step 7:

[0058] The server periodically compiles analysis results and warning information, automatically generating weekly or monthly environment reports. These reports are provided to users in digital format to support decision-making based on historical data.

[0059] (Example 1)

[0060] 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."

[0061] To provide a system that enables accurate and efficient data filtering and prediction in real-time collection and analysis of environmental information, allowing for the rapid identification of abnormal environmental changes and disaster risks, and the implementation of appropriate countermeasures.

[0062] 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.

[0063] In this invention, the server includes means for using an aircraft or ground device to collect environmental information in real time, means for initial filtering of the received environmental information to remove inaccurate information, and means for predicting environmental changes and disaster risks based on the analysis results. This enables early detection of abnormal environments and rapid response.

[0064] "Real-time" refers to a format where data collection and processing occur immediately, with minimal time delay.

[0065] "Environmental information" refers to information that includes data about the surrounding environment, such as temperature, humidity, and CO2 concentration.

[0066] "Aircraft or ground equipment" refers to equipment and devices used to collect environmental data in the air and on the ground.

[0067] An "artificial intelligence algorithm" refers to a computational method used to analyze data and recognize patterns and trends.

[0068] A "machine learning model" refers to a data analysis model used to learn from past data and predict future outcomes.

[0069] "Filtering" refers to the process of removing inaccurate or unnecessary information from collected data.

[0070] An "environmental report" refers to a report that compiles collected environmental information and the results of its analysis.

[0071] An "abnormal pattern" refers to data fluctuations or trends that fall outside the normal range.

[0072] A "warning" refers to a notification that informs the user in advance of potential risks or problems.

[0073] This invention provides a system for collecting and analyzing environmental information in real time. The central role of the entire system is played by a "server," which handles everything from collecting and analyzing environmental information to issuing warnings and generating reports.

[0074] The server collects environmental information using aircraft or ground equipment. Drones are used for aerial collection, while a diverse sensor network is employed on the ground. Drones accurately collect various environmental data, such as temperature, humidity, and CO2 concentration, and transmit it to the server via wireless communication. Technologies used include GPS location information and various weather sensors.

[0075] The collected data undergoes initial filtering by the terminal. This process removes inaccurate information and unnecessary data, improving the accuracy of the analysis. An artificial intelligence algorithm on the server then analyzes this filtered data. Analysis libraries such as Sci-kit Learn and TENSORFLOW®, written in Python, are used to detect anomalous patterns by comparing the collected historical database with the current data.

[0076] Next, the server uses a machine learning model to predict future environmental changes and disaster risks. Based on the prediction results, if an anomaly risk is detected, the server sends a real-time warning to the user. This warning includes specific countermeasures, allowing the user to take immediate action.

[0077] When users receive a warning, they can take practical actions, such as reviewing crop management methods or temporarily suspending work in specific areas. The server automatically generates and provides users with weekly and monthly environmental reports. Reports based on historical data make it easier for users to plan for the future.

[0078] As a concrete example, this system makes it possible to detect plant stress caused by high temperatures early in agricultural areas and adjust irrigation systems accordingly. An example of a prompt sentence to be input to the generated AI model is, "Based on weather data from the past month, please suggest the optimal irrigation schedule for crops." Implementing this system will enable rapid responses to environmental changes and efficient planning based on those responses.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The server controls aircraft and ground equipment to collect environmental information in real time. Pre-configured parameters for the target area are used as input, and data on temperature, humidity, and CO2 concentration are obtained as output. Sensors mounted on the drone measure this data and transmit it to the server wirelessly. During this process, location information is also transmitted using GPS data.

[0082] Step 2:

[0083] The terminal filters the raw environmental information received from the server. It receives various environmental data transmitted from drones and ground equipment as input, removing inaccurate information and other errors. As output, clean data suitable for analysis is generated. This filtering process is performed using a specific algorithm to improve data accuracy.

[0084] Step 3:

[0085] The server analyzes filtered data using artificial intelligence algorithms. The input is clean, filtered data. This data is compared with historical databases to detect anomalous patterns. The output provides analysis results regarding trends in environmental changes and potential problems. Advanced data analysis is achieved by using Python machine learning libraries.

[0086] Step 4:

[0087] The server uses a machine learning model to predict future environmental conditions based on the analysis results. It uses the analysis results obtained in step 3 as input to estimate future weather conditions and disaster risks. The output is a dataset containing details and probabilities of the predicted risks. This includes a prediction algorithm utilizing a generative AI model.

[0088] Step 5:

[0089] The server issues real-time warnings to users based on predicted risks. The predicted data from step 4 is used as input, and based on that data, it generates warnings with specific countermeasures. The output is a notification message sent to the user. This process is carried out via the warning system.

[0090] Step 6:

[0091] Users receive warnings from the server and take necessary actions. As input, users read the details of the warning and take actions such as changing agricultural management methods or temporarily suspending activities in the area. The output is the impact of the user's actions on the real environment. This ensures responsiveness to environmental changes.

[0092] Step 7:

[0093] The server automatically generates and provides users with periodic environment reports based on historical data and analysis results. The input uses all data and results from past processing, resulting in reports that provide users with information to support future planning. The output consists of detailed weekly and monthly reports. Tools are used to visualize the analysis data.

[0094] (Application Example 1)

[0095] 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."

[0096] Conventional environmental data collection systems have been insufficient in supporting the real-time use of collected data and rapid decision-making. Furthermore, they lacked the information necessary for users to intuitively understand and act upon countermeasures against environmental changes and risks. In particular, in agricultural regions, where rapid and appropriate action in response to environmental changes is crucial, current systems have been inadequate in providing this support.

[0097] 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.

[0098] In this invention, the server includes means for using an aerial or ground device to collect environmental data in real time, means for using an artificial intelligence algorithm to analyze the collected environmental data, and means for displaying the analysis results on a mobile terminal and prompting the user to adjust their actions based on the environmental information. This enables rapid understanding of environmental conditions and appropriate decision-making.

[0099] "Real-time" refers to the time frame in which information is compiled and made available.

[0100] "Environmental data" refers to information related to the surrounding natural environment, such as temperature, humidity, and CO2 concentration.

[0101] "Flight equipment" refers to machines such as drones and aircraft that collect data using the air.

[0102] "Ground equipment" refers to devices that collect data using sensors and devices installed on the ground.

[0103] An "artificial intelligence algorithm" refers to a computational method used to analyze received data and recognize trends and anomalies.

[0104] "Mobile devices" refer to portable computing devices such as smartphones and tablets.

[0105] "Analysis results" refer to conclusions and predictions derived by artificial intelligence algorithms based on collected data.

[0106] A "user" refers to anyone who uses this system and makes decisions based on environmental information.

[0107] "Adjusting actions" refers to optimizing work content and countermeasures based on the environmental information provided.

[0108] To realize this invention, a system will be constructed that collects environmental data in real time using flying devices and ground-based devices. Drones will be used as flying devices, and a sensor network will be installed as ground-based devices. The data collected by these devices will be transmitted to a server and analyzed by artificial intelligence algorithms. The server will use machine learning libraries such as Python and TensorFlow to analyze the received data and recognize trends and anomalies.

[0109] The analysis results are transmitted to mobile devices via the internet. These mobile devices include smartphones and tablets. These devices display the analysis results to the user through applications built using frameworks such as React Native. Based on the analysis results, the user understands the environmental information and adjusts their actions accordingly.

[0110] As a concrete example, it is possible to detect high-temperature stress in agricultural areas early and improve water management. The server analyzes temperature and humidity data and issues a warning to the user when it detects an abnormal pattern. The warning includes specific countermeasures, for example, sending a notification such as, "The temperature is expected to exceed 35 degrees Celsius next Wednesday. We recommend water management."

[0111] When using a generative AI model, the following are examples of prompt statements:

[0112] "I'd like to know about the environmental risks to agricultural work next week. Please provide the weather forecast for next week and appropriate countermeasures."

[0113] These prompts allow AI models to provide specific temperature predictions and work guidelines.

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The server receives environmental data such as temperature, humidity, and CO2 concentration from flying and ground-based devices. This includes the process of drones and ground sensors collecting various measurement data and transmitting it to the server wirelessly. The input is environmental data, and the output is raw data stored on the server.

[0117] Step 2:

[0118] The server applies an initial filter to the received environmental data. It detects and removes missing and outlier values ​​while maintaining data accuracy. The input is the raw data obtained in step 1, and the output is the refined data.

[0119] Step 3:

[0120] The server uses artificial intelligence algorithms to analyze the filtered data. Here, a machine learning model (e.g., TensorFlow) is used to compare the current data with historical data and recognize anomalous patterns and trends. The input is the refined data, and the output is the recognition of anomalous patterns and trends in environmental changes as a result of the analysis.

[0121] Step 4:

[0122] The server predicts future weather conditions and disaster risks based on the analysis results. Using a prediction algorithm, it calculates the most likely risks in a specific region. The input is the analysis results from step 3, and the output is risk prediction data.

[0123] Step 5:

[0124] The server generates and sends real-time warnings to terminals based on the generated risk predictions. These warnings include specific action plans, such as recommendations like "We recommend hydration management." The input is risk prediction data, and the output is a warning message.

[0125] Step 6:

[0126] The device displays received warning messages to the user via the application. These messages are displayed in a user interface built using React Native. The input is the warning message, and the output is the notification to the user.

[0127] Step 7:

[0128] Based on the information provided by the device, the user takes actions such as adjusting crop management methods. This requires the user to understand the presented information and make appropriate decisions. The input, as an intervention, is the notification content, and the output is the user's specific actions.

[0129] 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.

[0130] This invention provides a system that integrates a user emotion engine with real-time environmental data collection, analysis, prediction, warning, and report generation functions. This system enhances the accuracy of environmental monitoring and user responsiveness, enabling more personalized information delivery.

[0131] The server first collects environmental data from aircraft and ground equipment, filters it, and then analyzes the data using artificial intelligence algorithms. Based on the analysis results, predictions of environmental changes and disaster risks are made. If the predicted risk increases, the server generates a warning message in real time.

[0132] The emotion engine evaluates the user's psychological state based on sensor data and past response history. The device adjusts how warnings are presented according to the user's emotions identified by the emotion engine. This adjustment includes the intensity of the warning, the wording, and the level of detail in the recommended actions.

[0133] For example, if a risk of drought due to high temperatures is predicted, and the emotional engine determines that the user is stressed, the device will deliver a warning message in a softer, more subdued tone and suggest ways to alleviate anxiety. On the other hand, if the user is calm, the device will provide a warning with specific figures and offer detailed countermeasures.

[0134] Users receive alerts from their devices and can devise and implement countermeasures in a way that best suits their emotional state. The server automatically generates weekly or monthly environmental reports based on all the data and provides them to the user. These reports include insights based on environmental changes and user responses, supporting informed decision-making.

[0135] Thus, the present invention extends environmental monitoring technology and enables information transmission that takes user emotions into consideration, thereby supporting more effective environmental conservation activities.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The server collects environmental data in real time from aircraft and ground equipment. This includes measurements of temperature, humidity, and CO2 concentration. The data is transmitted to the server immediately.

[0139] Step 2:

[0140] The server performs initial processing on the collected data, filtering out unnecessary data and outliers. This ensures the accuracy and reliability of the data.

[0141] Step 3:

[0142] The server feeds the filtered data into an artificial intelligence algorithm for deep analysis. This analysis aims to understand the current state of the environment and identify the causes of any specific changes detected.

[0143] Step 4:

[0144] Based on the analysis results, the server predicts future environmental changes and potential disaster risks. If a high risk is identified, preparations for issuing a warning are made.

[0145] Step 5:

[0146] The emotion engine activates, reading the user's emotional state from sensor data and past history via the device. This allows for an evaluation of the user's psychological and emotional responses.

[0147] Step 6:

[0148] The device customizes warning messages based on the evaluation results of the emotion engine. The content and wording are adjusted according to the user's emotional state.

[0149] Step 7:

[0150] Users receive customized warnings through their devices and select appropriate actions based on the content of those warnings. This process takes into account the user's emotional state.

[0151] Step 8:

[0152] The server collects user response data and uses it to improve the accuracy of future warnings and suggestions. It also generates weekly or monthly reports based on the analysis results and provides them to the user.

[0153] Through this process, the system provides appropriate warnings based on environmental data and information that takes user emotions into consideration.

[0154] (Example 2)

[0155] 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 will be referred to as the "terminal."

[0156] Current environmental monitoring systems collect and analyze environmental data, but generally do not consider the individual psychological state of users when notifying them of this data. This can potentially cause stress and anxiety for users. Furthermore, the lack of specific, user-appropriate countermeasures makes it difficult to respond appropriately to the risks they face.

[0157] 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.

[0158] In this invention, the server includes means for using an aircraft or ground equipment to collect environmental information in real time, means for using a machine learning algorithm to analyze the collected environmental information, and means for using an emotion recognition engine to evaluate the user's psychological state and adjust warnings based on the evaluation. This makes it possible to provide information and propose specific countermeasures that are appropriate to the user's psychological state.

[0159] "Collecting environmental information in real time" means using aircraft or ground equipment to acquire current environmental conditions as data without any time delay.

[0160] "Using machine learning algorithms" means utilizing mathematical methods in data analysis so that computers can learn on their own and detect patterns and anomalies.

[0161] "Predicting environmental changes and disaster risks" means estimating the likelihood of future environmental changes and natural disasters based on collected data.

[0162] "Issuing real-time warnings" means immediately alerting users before predicted risks materialize.

[0163] "Automatically generating environmental reports" means automatically creating periodic reports based on environmental data collected and analyzed over a certain period.

[0164] "Using an emotion recognition engine" means utilizing technology that analyzes the user's psychological state and changes the way information is presented accordingly.

[0165] "Proposing specific countermeasures tailored to the user's psychological state" means considering the user's current emotions and mental state, and presenting specific action guidelines and coping strategies that are optimized for those conditions.

[0166] "Visualizing on a geographic information system" means visually representing collected environmental information on a map so that its location and situation can be understood intuitively.

[0167] The embodiments for carrying out the present invention will now be described. In this invention, a system is constructed that collects environmental information in real time and provides information according to the user's psychological state.

[0168] Server roles and operations:

[0169] The server collects environmental data from aircraft and ground equipment through various sensors. This environmental data includes information such as temperature, humidity, wind speed, and precipitation. The collected data is filtered using Python scripts to remove incomplete or noisy data. Subsequently, data analysis is performed using machine learning frameworks such as TensorFlow and PyTorch. Specifically, LSTM (Long Short-Term Memory Network) and CNN (Convolutional Neural Network) are used to predict environmental changes and disaster risks.

[0170] Terminal functions and operation:

[0171] The device is equipped with an emotion recognition engine that analyzes the user's psychological state based on their biometric data and past response history. Devices used in this process may include a heart rate sensor and a camera. The device utilizes a generative AI model to generate warning messages appropriate to the user's psychological state. For example, if the user is feeling stressed, a message in a softer tone will be presented.

[0172] User roles and operations:

[0173] Users receive personalized warning messages from their devices, enabling them to consider and implement appropriate countermeasures based on risk information. For example, in response to the effects of predicted high temperatures, specific advice such as, "Today's temperature is higher than expected, so we recommend that you stay hydrated," is provided.

[0174] Specific examples and prompt statements:

[0175] For example, if a drought due to high temperatures is predicted, the server will notify the user with a warning based on that prediction. If the emotion recognition engine determines that the user's psychological state is stressed, the device will provide a gentle message such as, "Please remain calm. Please consider the following measures."

[0176] An example of a prompt to input into the generative AI model would be an instruction such as, "Generate a message that warns residents in areas affected by high temperatures without causing them stress." Based on this prompt, the generative AI will create the optimal message and provide it to the user.

[0177] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0178] Step 1:

[0179] The server collects environmental data in real time from aircraft and ground facilities. This collected data includes environmental information such as temperature, humidity, wind speed, and precipitation. The environmental information obtained as input data is filtered using a Python script. Specifically, missing values ​​are removed and noisy data is smoothed to make it analyzable. A clean environmental dataset is obtained as output.

[0180] Step 2:

[0181] The server analyzes the filtered data using machine learning frameworks such as TensorFlow and PyTorch. In this step, models such as LSTM and CNN are used to extract features from environmental data and predict risks. Specifically, data is input into the analysis model, and predictions of environmental changes and disaster risks are output. The output is a predicted risk value for a specific point in the future.

[0182] Step 3:

[0183] The server generates warning messages in real time based on the analyzed data. If the predicted risk increases, it issues an appropriate warning to the user. The output includes a warning message and specific recommended countermeasures. This information is sent to the terminal.

[0184] Step 4:

[0185] The device uses an emotion recognition engine to analyze the user's psychological state based on the received warning message. Input includes the user's biometric data and past response history. Based on this data, the device analyzes the user's emotions and adjusts the message tone and content accordingly as the output.

[0186] Step 5:

[0187] The user receives a tailored warning message from the device. This message contains information appropriate to the user's psychological state and presents specific actionable countermeasures. Based on the information presented, the user can select and take appropriate action to address the risk.

[0188] Step 6:

[0189] The server automatically generates weekly or monthly reports based on data collected and generated throughout the entire processing process. Inputs include environmental data, user response data, and analysis results. Outputs are comprehensive reports summarizing environmental changes and user responses, offering long-term insights and recommendations for users.

[0190] (Application Example 2)

[0191] 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".

[0192] In modern society, climate change and environmental disasters occur frequently, and in urban areas in particular, there is a need for real-time monitoring of environmental data and rapid response based on that data. However, conventional systems have difficulty issuing personalized warnings that take into account the user's psychological state, making it difficult to take appropriate action. Furthermore, simply visualizing environmental data is insufficient to provide information tailored to individual users. Therefore, there is a need for the development of a new system that combines real-time environmental change prediction with means of providing information based on individual emotions.

[0193] 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.

[0194] In this invention, the server includes means for using an aircraft or ground device to collect area data in real time, means for using an artificial intelligence algorithm to analyze the collected area data, means for predicting area fluctuations and crisis risks based on the analysis results, and means integrating an emotion engine that evaluates the user's emotions and adjusts the method of presenting warnings based on their psychological state. This enables real-time prediction of environmental risks and the presentation of appropriate warnings and countermeasures according to the emotional state of each individual user.

[0195] "Real-time" refers to processing information and data immediately and providing results almost instantly.

[0196] "Regional data" refers to diverse environmental information such as weather, topography, and temperature related to a specific region.

[0197] An "artificial intelligence algorithm" refers to a set of logical steps that a computer uses to analyze data, learn, and make predictions.

[0198] "Crisis risk" refers to the potential danger associated with natural disasters and environmental changes.

[0199] An "emotion engine" refers to technology that analyzes a user's psychological state and evaluates their emotions.

[0200] "Integrated means" refers to methods of combining multiple functions or technologies to operate as a single system.

[0201] "Adjusting how warnings are presented" refers to changing the content and intensity of notifications according to the user's psychological state.

[0202] "Individual emotional state" refers to the mental and emotional state of each individual user.

[0203] This invention is based on a system that provides personalized environmental warnings to users in smart cities. A server collects real-time area data using aircraft and ground equipment and analyzes it using artificial intelligence algorithms. This analysis process utilizes platforms such as Azure® Machine Learning and TensorFlow to filter the data and predict environmental changes and crisis risks.

[0204] Furthermore, the server integrates an emotion engine to assess the user's emotional state. To do this, it utilizes the smartphone's camera and microphone, employing emotion recognition technology to determine the user's psychological state. Based on this information, it adjusts the intensity and content of warnings. For example, if a user is feeling anxious, it delivers information in a gentle tone and suggests reassuring measures. Conversely, for a calm user, it provides specific numerical data and detailed countermeasures.

[0205] Users can receive personalized alerts based on emotion recognition through a smartphone app. These alerts enable real-time awareness of local environmental risks and support appropriate action decisions. For example, if there is a possibility of flooding, and the emotion engine determines that the user is in an unstable state, it will offer a gentle suggestion such as, "There is a risk of flooding, so we recommend checking evacuation locations."

[0206] An example of an input prompt for a generative AI model is, "Suggest how to deliver a flood warning softly when the target user is deemed to be feeling anxious." This prompt serves as a reference for the generative AI in creating the most appropriate warning based on the user's emotions.

[0207] In this way, a system in which servers, terminals, and users work together as a unified whole provides appropriate environmental information and enables effective responses that are tailored to the user's emotions.

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] The server collects environmental data in real time via aircraft and ground equipment. This data includes abnormal weather information such as temperature, humidity, and precipitation. The input is raw data from sensors, and the output is clean environmental data that has been initially filtered. Filtering is performed to remove noise from the data and convert it into a format suitable for analysis.

[0211] Step 2:

[0212] The server analyzes collected environmental data using artificial intelligence algorithms. Specifically, it uses Azure Machine Learning and TensorFlow for pattern recognition and anomaly detection. The input is filtered environmental data, and the output is predictions of regional changes and crisis risks. An AI model is applied to analyze future risks based on the data.

[0213] Step 3:

[0214] The server uses an emotion engine to evaluate the user's psychological state. It analyzes input data from the smartphone's camera and microphone to identify the user's emotions. The input consists of the user's facial expressions and voice tone, while the output is data indicating the user's emotional state. The emotion analysis module converts the raw data into features and matches them against a learned model to identify emotions.

[0215] Step 4:

[0216] The device generates warning messages by combining the user's emotional state, obtained from the emotion engine, with environmental prediction results from an AI algorithm. An AI model for generating prompts selects a tone and expression appropriate to the user's emotions, customizing the message. Input is emotional state and risk data, while output is a specific warning message. It can generate warnings in a softer tone or information with adjusted levels of detail.

[0217] Step 5:

[0218] The user reviews the warning message received through the device and takes the recommended action as needed. The prompt-generated warnings are tailored to the user's emotional state, encouraging quick and appropriate action. The output is the warning information the user receives, influencing their final decision.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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".

[0235] This invention provides a system and method for collecting environmental data in real time and analyzing that data. One embodiment involves efficiently collecting data using aircraft or ground equipment, making predictions and warnings based on that data, and finally generating a report.

[0236] The server controls the drone based on pre-configured parameters, measuring temperature, humidity, CO2 concentration, and other parameters from above the target area. A large network of sensors is deployed on the ground to collect detailed environmental data. This collected data is then transmitted to the server.

[0237] The terminal receives data from the server and performs initial filtering. This removes inaccurate or unnecessary data, ensuring that analysis can be performed effectively.

[0238] The server analyzes the received data using artificial intelligence algorithms. The analysis detects anomalous patterns by comparing them with past data, and identifies trends in environmental changes. It also uses machine learning models to predict future weather conditions and disaster risks.

[0239] If the forecast indicates an increased risk of extreme weather or disaster, the server will promptly issue a real-time warning. The warning will include specific countermeasures to encourage users to take swift action.

[0240] Based on the warnings received, users can adjust their crop management methods or decide to suspend activities in specific areas. The server then provides users with regularly generated weekly and monthly environmental reports, allowing them to review past conditions and plan future actions.

[0241] This system enables effective management of environmental conservation activities and supports rapid decision-making. For example, it allows for early detection of high-temperature stress in agricultural areas and the implementation of improved water management.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The server sends data collection instructions to aircraft and ground equipment based on a pre-configured schedule. The aircraft follows the designated flight path, and ground equipment measures various environmental parameters, collecting data in real time.

[0245] Step 2:

[0246] The server receives the collected raw data and performs initial processing. This initial processing involves detecting outliers and filtering out data that does not need to be processed.

[0247] Step 3:

[0248] The server passes the initially processed data to an artificial intelligence algorithm for advanced data analysis. This analysis specifically detects unusual fluctuations and patterns, generating data to visualize environmental conditions.

[0249] Step 4:

[0250] Based on the analysis results, the server uses machine learning models to predict future environmental conditions and disaster risks. These predictions include short-term weather forecasts and long-term environmental change forecasts.

[0251] Step 5:

[0252] Based on predictions, the server generates and sends real-time warning messages about potential risks to the terminal. The warnings include details of the risk and suggested countermeasures.

[0253] Step 6:

[0254] The terminal notifies the user of any received warning messages. The user can then check the details of the warning via the terminal and take necessary action promptly.

[0255] Step 7:

[0256] The server periodically compiles analysis results and warning information, automatically generating weekly or monthly environment reports. These reports are provided to users in digital format to support decision-making based on historical data.

[0257] (Example 1)

[0258] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0259] To provide a system that enables accurate and efficient data filtering and prediction in real-time collection and analysis of environmental information, allowing for the rapid identification of abnormal environmental changes and disaster risks, and the implementation of appropriate countermeasures.

[0260] 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.

[0261] In this invention, the server includes means for using an aircraft or ground device to collect environmental information in real time, means for initial filtering of the received environmental information to remove inaccurate information, and means for predicting environmental changes and disaster risks based on the analysis results. This enables early detection of abnormal environments and rapid response.

[0262] "Real-time" refers to a format where data collection and processing occur immediately, with minimal time delay.

[0263] "Environmental information" refers to information that includes data about the surrounding environment, such as temperature, humidity, and CO2 concentration.

[0264] "Aircraft or ground equipment" refers to equipment and devices used to collect environmental data in the air and on the ground.

[0265] An "artificial intelligence algorithm" refers to a computational method used to analyze data and recognize patterns and trends.

[0266] A "machine learning model" refers to a data analysis model used to learn from past data and predict future outcomes.

[0267] "Filtering" refers to the process of removing inaccurate or unnecessary information from collected data.

[0268] An "environmental report" refers to a report that compiles collected environmental information and the results of its analysis.

[0269] An "abnormal pattern" refers to data fluctuations or trends that fall outside the normal range.

[0270] A "warning" refers to a notification that informs the user in advance of potential risks or problems.

[0271] This invention provides a system for collecting and analyzing environmental information in real time. The central role of the entire system is played by a "server," which handles everything from collecting and analyzing environmental information to issuing warnings and generating reports.

[0272] The server collects environmental information using aircraft or ground equipment. Drones are used for aerial collection, while a diverse sensor network is employed on the ground. Drones accurately collect various environmental data, such as temperature, humidity, and CO2 concentration, and transmit it to the server via wireless communication. Technologies used include GPS location information and various weather sensors.

[0273] The collected data undergoes initial filtering by the terminal. This process removes inaccurate information and unnecessary data, improving the accuracy of the analysis. An artificial intelligence algorithm on the server then analyzes this filtered data. Analysis libraries such as Sci-kit Learn and TensorFlow, written in Python, are used to detect anomalous patterns by comparing the collected historical database with the current data.

[0274] Next, the server uses a machine learning model to predict future environmental changes and disaster risks. Based on the prediction results, if an anomaly risk is detected, the server sends a real-time warning to the user. This warning includes specific countermeasures, allowing the user to take immediate action.

[0275] When users receive a warning, they can take practical actions, such as reviewing crop management methods or temporarily suspending work in specific areas. The server automatically generates and provides users with weekly and monthly environmental reports. Reports based on historical data make it easier for users to plan for the future.

[0276] As a concrete example, this system makes it possible to detect plant stress caused by high temperatures early in agricultural areas and adjust irrigation systems accordingly. An example of a prompt sentence to be input to the generated AI model is, "Based on weather data from the past month, please suggest the optimal irrigation schedule for crops." Implementing this system will enable rapid responses to environmental changes and efficient planning based on those responses.

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] The server controls aircraft and ground equipment to collect environmental information in real time. Pre-configured parameters for the target area are used as input, and data on temperature, humidity, and CO2 concentration are obtained as output. Sensors mounted on the drone measure this data and transmit it to the server wirelessly. During this process, location information is also transmitted using GPS data.

[0280] Step 2:

[0281] The terminal filters the raw environmental information received from the server. It receives various environmental data transmitted from drones and ground equipment as input, removing inaccurate information and other errors. As output, clean data suitable for analysis is generated. This filtering process is performed using a specific algorithm to improve data accuracy.

[0282] Step 3:

[0283] The server analyzes the filtered data using artificial intelligence algorithms. As input, the clean data after filtering is used. This data is compared with the past database to detect abnormal patterns. As output, analysis results regarding the trends of environmental changes and potential problems are obtained. By using the machine learning library of Python, advanced data analysis is realized.

[0284] Step 4:

[0285] The server uses a machine learning model based on the analysis results to predict future environmental situations. As input, the analysis results obtained in Step 3 are utilized to estimate future weather conditions and disaster risks. The output is a dataset including the details of the predicted risks and their probabilities. This includes prediction algorithms using generative AI models.

[0286] Step 5:

[0287] Based on the predicted risks, the server issues real-time warnings to the user. As input, the prediction data from Step 4 is used, and according to its content, warning messages with specific countermeasures are generated. The output is the notification message sent to the user. This process is carried out via the warning system.

[0288] Step 6:

[0289] Upon receiving the warning from the server, the user implements the necessary responses. The user reads the details of the warning as input and takes actions such as changing agricultural management methods or temporarily suspending activities in the area. The output is the impact on the actual environment due to the user's actions. This ensures responsiveness to environmental changes.

[0290] Step 7:

[0291] The server automatically generates and provides users with periodic environment reports based on historical data and analysis results. The input uses all data and results from past processing, resulting in reports that provide users with information to support future planning. The output consists of detailed weekly and monthly reports. Tools are used to visualize the analysis data.

[0292] (Application Example 1)

[0293] 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."

[0294] Conventional environmental data collection systems have been insufficient in supporting the real-time use of collected data and rapid decision-making. Furthermore, they lacked the information necessary for users to intuitively understand and act upon countermeasures against environmental changes and risks. In particular, in agricultural regions, where rapid and appropriate action in response to environmental changes is crucial, current systems have been inadequate in providing this support.

[0295] 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.

[0296] In this invention, the server includes means for using an aerial or ground device to collect environmental data in real time, means for using an artificial intelligence algorithm to analyze the collected environmental data, and means for displaying the analysis results on a mobile terminal and prompting the user to adjust their actions based on the environmental information. This enables rapid understanding of environmental conditions and appropriate decision-making.

[0297] "Real-time" refers to the time frame in which information is compiled and made available.

[0298] "Environmental data" refers to information related to the surrounding natural environment, such as temperature, humidity, and CO2 concentration.

[0299] "Flight equipment" refers to machines such as drones and aircraft that collect data using the air.

[0300] "Ground equipment" refers to devices that collect data using sensors and devices installed on the ground.

[0301] An "artificial intelligence algorithm" refers to a computational method used to analyze received data and recognize trends and anomalies.

[0302] "Mobile devices" refer to portable computing devices such as smartphones and tablets.

[0303] "Analysis results" refer to conclusions and predictions derived by artificial intelligence algorithms based on collected data.

[0304] A "user" refers to anyone who uses this system and makes decisions based on environmental information.

[0305] "Adjusting actions" refers to optimizing work content and countermeasures based on the environmental information provided.

[0306] To realize this invention, a system will be constructed that collects environmental data in real time using flying devices and ground-based devices. Drones will be used as flying devices, and a sensor network will be installed as ground-based devices. The data collected by these devices will be transmitted to a server and analyzed by artificial intelligence algorithms. The server will use machine learning libraries such as Python and TensorFlow to analyze the received data and recognize trends and anomalies.

[0307] The analysis results are transmitted to mobile devices via the internet. These mobile devices include smartphones and tablets. These devices display the analysis results to the user through applications built using frameworks such as React Native. Based on the analysis results, the user understands the environmental information and adjusts their actions accordingly.

[0308] As a specific example, it is possible to detect high-temperature stress in agricultural areas at an early stage and improve water management. When the server analyzes temperature and humidity data and detects an abnormal pattern, it issues a warning to the user. The warning includes specific countermeasures. For example, it sends a notification such as "It is expected that the temperature will exceed 35 degrees on Wednesday next week. Water management is recommended."

[0309] When using a generative AI model, examples of prompt texts are as follows:

[0310] "I want to know the environmental risks in next week's agricultural work. Please tell me the weather forecast for next week and appropriate countermeasures."

[0311] With such prompts, the AI model can provide specific temperature predictions and work guidelines.

[0312] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0313] Step 1:

[0314] The server receives environmental data such as temperature, humidity, and CO2 concentration from aircraft or ground devices. This includes the process in which drones of aircraft and ground sensors collect various measurement data and transmit it to the server using wireless communication. The input is environmental data, and the output is raw data stored on the server.

[0315] Step 2:

[0316] The server applies initial filtering to the received environmental data. While maintaining the accuracy of the data, it detects and removes missing values and outliers. The input is the raw data obtained in Step 1, and the output is purified data.

[0317] Step 3:

[0318] The server uses artificial intelligence algorithms to analyze the filtered data. Here, a machine learning model (e.g., TensorFlow) is used to compare the current data with historical data and recognize anomalous patterns and trends. The input is the refined data, and the output is the recognition of anomalous patterns and trends in environmental changes as a result of the analysis.

[0319] Step 4:

[0320] The server predicts future weather conditions and disaster risks based on the analysis results. Using a prediction algorithm, it calculates the most likely risks in a specific region. The input is the analysis results from step 3, and the output is risk prediction data.

[0321] Step 5:

[0322] The server generates and sends real-time warnings to terminals based on the generated risk predictions. These warnings include specific action plans, such as recommendations like "We recommend hydration management." The input is risk prediction data, and the output is a warning message.

[0323] Step 6:

[0324] The device displays received warning messages to the user via the application. These messages are displayed in a user interface built using React Native. The input is the warning message, and the output is the notification to the user.

[0325] Step 7:

[0326] Based on the information provided by the device, the user takes actions such as adjusting crop management methods. This requires the user to understand the presented information and make appropriate decisions. The input, as an intervention, is the notification content, and the output is the user's specific actions.

[0327] 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.

[0328] This invention provides a system that integrates a user emotion engine with real-time environmental data collection, analysis, prediction, warning, and report generation functions. This system enhances the accuracy of environmental monitoring and user responsiveness, enabling more personalized information delivery.

[0329] The server first collects environmental data from aircraft and ground equipment, filters it, and then analyzes the data using artificial intelligence algorithms. Based on the analysis results, predictions of environmental changes and disaster risks are made. If the predicted risk increases, the server generates a warning message in real time.

[0330] The emotion engine evaluates the user's psychological state based on sensor data and past response history. The device adjusts how warnings are presented according to the user's emotions identified by the emotion engine. This adjustment includes the intensity of the warning, the wording, and the level of detail in the recommended actions.

[0331] For example, if a risk of drought due to high temperatures is predicted, and the emotional engine determines that the user is stressed, the device will deliver a warning message in a softer, more subdued tone and suggest ways to alleviate anxiety. On the other hand, if the user is calm, the device will provide a warning with specific figures and offer detailed countermeasures.

[0332] Users receive alerts from their devices and can devise and implement countermeasures in a way that best suits their emotional state. The server automatically generates weekly or monthly environmental reports based on all the data and provides them to the user. These reports include insights based on environmental changes and user responses, supporting informed decision-making.

[0333] Thus, the present invention extends environmental monitoring technology and enables information transmission that takes user emotions into consideration, thereby supporting more effective environmental conservation activities.

[0334] The following describes the processing flow.

[0335] Step 1:

[0336] The server collects environmental data in real time from aircraft and ground equipment. This includes measurements of temperature, humidity, and CO2 concentration. The data is transmitted to the server immediately.

[0337] Step 2:

[0338] The server performs initial processing on the collected data, filtering out unnecessary data and outliers. This ensures the accuracy and reliability of the data.

[0339] Step 3:

[0340] The server feeds the filtered data into an artificial intelligence algorithm for deep analysis. This analysis aims to understand the current state of the environment and identify the causes of any specific changes detected.

[0341] Step 4:

[0342] Based on the analysis results, the server predicts future environmental changes and potential disaster risks. If a high risk is identified, preparations for issuing a warning are made.

[0343] Step 5:

[0344] The emotion engine activates, reading the user's emotional state from sensor data and past history via the device. This allows for an evaluation of the user's psychological and emotional responses.

[0345] Step 6:

[0346] The device customizes warning messages based on the evaluation results of the emotion engine. The content and wording are adjusted according to the user's emotional state.

[0347] Step 7:

[0348] Users receive customized warnings through their devices and select appropriate actions based on the content of those warnings. This process takes into account the user's emotional state.

[0349] Step 8:

[0350] The server collects user response data and uses it to improve the accuracy of future warnings and suggestions. It also generates weekly or monthly reports based on the analysis results and provides them to the user.

[0351] Through this process, the system provides appropriate warnings based on environmental data and information that takes user emotions into consideration.

[0352] (Example 2)

[0353] 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".

[0354] Current environmental monitoring systems collect and analyze environmental data, but generally do not consider the individual psychological state of users when notifying them of this data. This can potentially cause stress and anxiety for users. Furthermore, the lack of specific, user-appropriate countermeasures makes it difficult to respond appropriately to the risks they face.

[0355] 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.

[0356] In this invention, the server includes means for using an aircraft or ground equipment to collect environmental information in real time, means for using a machine learning algorithm to analyze the collected environmental information, and means for using an emotion recognition engine to evaluate the user's psychological state and adjust warnings based on the evaluation. This makes it possible to provide information and propose specific countermeasures that are appropriate to the user's psychological state.

[0357] "Collecting environmental information in real time" means using aircraft or ground equipment to acquire current environmental conditions as data without any time delay.

[0358] "Using machine learning algorithms" means utilizing mathematical methods in data analysis so that computers can learn on their own and detect patterns and anomalies.

[0359] "Predicting environmental changes and disaster risks" means estimating the likelihood of future environmental changes and natural disasters based on collected data.

[0360] "Issuing real-time warnings" means immediately alerting users before predicted risks materialize.

[0361] "Automatically generating environmental reports" means automatically creating periodic reports based on environmental data collected and analyzed over a certain period.

[0362] "Using an emotion recognition engine" means utilizing technology that analyzes the user's psychological state and changes the way information is presented accordingly.

[0363] "Proposing specific countermeasures tailored to the user's psychological state" means considering the user's current emotions and mental state, and presenting specific action guidelines and coping strategies that are optimized for those conditions.

[0364] "Visualizing on a geographic information system" means visually representing collected environmental information on a map so that its location and situation can be understood intuitively.

[0365] The embodiments for carrying out the present invention will now be described. In this invention, a system is constructed that collects environmental information in real time and provides information according to the user's psychological state.

[0366] Server roles and operations:

[0367] The server collects environmental data from aircraft and ground equipment through various sensors. This environmental data includes information such as temperature, humidity, wind speed, and precipitation. The collected data is filtered using Python scripts to remove incomplete or noisy data. Subsequently, data analysis is performed using machine learning frameworks such as TensorFlow and PyTorch. Specifically, LSTM (Long Short-Term Memory Network) and CNN (Convolutional Neural Network) are used to predict environmental changes and disaster risks.

[0368] Terminal functions and operation:

[0369] The device is equipped with an emotion recognition engine that analyzes the user's psychological state based on their biometric data and past response history. Devices used in this process may include a heart rate sensor and a camera. The device utilizes a generative AI model to generate warning messages appropriate to the user's psychological state. For example, if the user is feeling stressed, a message in a softer tone will be presented.

[0370] User roles and operations:

[0371] Users receive personalized warning messages from their devices, enabling them to consider and implement appropriate countermeasures based on risk information. For example, in response to the effects of predicted high temperatures, specific advice such as, "Today's temperature is higher than expected, so we recommend that you stay hydrated," is provided.

[0372] Specific examples and prompt statements:

[0373] For example, if a drought due to high temperatures is predicted, the server will notify the user with a warning based on that prediction. If the emotion recognition engine determines that the user's psychological state is stressed, the device will provide a gentle message such as, "Please remain calm. Please consider the following measures."

[0374] An example of a prompt to input into the generative AI model would be an instruction such as, "Generate a message that warns residents in areas affected by high temperatures without causing them stress." Based on this prompt, the generative AI will create the optimal message and provide it to the user.

[0375] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0376] Step 1:

[0377] The server collects environmental data in real time from aircraft and ground facilities. This collected data includes environmental information such as temperature, humidity, wind speed, and precipitation. The environmental information obtained as input data is filtered using a Python script. Specifically, missing values ​​are removed and noisy data is smoothed to make it analyzable. A clean environmental dataset is obtained as output.

[0378] Step 2:

[0379] The server analyzes the filtered data using machine learning frameworks such as TensorFlow and PyTorch. In this step, models such as LSTM and CNN are used to extract features from environmental data and predict risks. Specifically, data is input into the analysis model, and predictions of environmental changes and disaster risks are output. The output is a predicted risk value for a specific point in the future.

[0380] Step 3:

[0381] The server generates warning messages in real time based on the analyzed data. If the predicted risk increases, it issues an appropriate warning to the user. The output includes a warning message and specific recommended countermeasures. This information is sent to the terminal.

[0382] Step 4:

[0383] The device uses an emotion recognition engine to analyze the user's psychological state based on the received warning message. Input includes the user's biometric data and past response history. Based on this data, the device analyzes the user's emotions and adjusts the message tone and content accordingly as the output.

[0384] Step 5:

[0385] The user receives a tailored warning message from the device. This message contains information appropriate to the user's psychological state and presents specific actionable countermeasures. Based on the information presented, the user can select and take appropriate action to address the risk.

[0386] Step 6:

[0387] The server automatically generates weekly or monthly reports based on data collected and generated throughout the entire processing process. Inputs include environmental data, user response data, and analysis results. Outputs are comprehensive reports summarizing environmental changes and user responses, offering long-term insights and recommendations for users.

[0388] (Application Example 2)

[0389] 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."

[0390] In modern society, climate change and environmental disasters occur frequently, and in urban areas in particular, there is a need for real-time monitoring of environmental data and rapid response based on that data. However, conventional systems have difficulty issuing personalized warnings that take into account the user's psychological state, making it difficult to take appropriate action. Furthermore, simply visualizing environmental data is insufficient to provide information tailored to individual users. Therefore, there is a need for the development of a new system that combines real-time environmental change prediction with means of providing information based on individual emotions.

[0391] 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.

[0392] In this invention, the server includes means for using an aircraft or ground device to collect area data in real time, means for using an artificial intelligence algorithm to analyze the collected area data, means for predicting area fluctuations and crisis risks based on the analysis results, and means integrating an emotion engine that evaluates the user's emotions and adjusts the method of presenting warnings based on their psychological state. This enables real-time prediction of environmental risks and the presentation of appropriate warnings and countermeasures according to the emotional state of each individual user.

[0393] "Real-time" refers to processing information and data immediately and providing results almost instantly.

[0394] "Regional data" refers to diverse environmental information such as weather, topography, and temperature related to a specific region.

[0395] An "artificial intelligence algorithm" refers to a set of logical steps that a computer uses to analyze data, learn, and make predictions.

[0396] "Crisis risk" refers to the potential danger associated with natural disasters and environmental changes.

[0397] An "emotion engine" refers to technology that analyzes a user's psychological state and evaluates their emotions.

[0398] "Integrated means" refers to methods of combining multiple functions or technologies to operate as a single system.

[0399] "Adjusting how warnings are presented" refers to changing the content and intensity of notifications according to the user's psychological state.

[0400] "Individual emotional state" refers to the mental and emotional state of each individual user.

[0401] This invention is based on a system that provides personalized environmental warnings to users in smart cities. A server collects real-time regional data using aircraft and ground equipment and analyzes it using artificial intelligence algorithms. This analysis process utilizes platforms such as Azure Machine Learning and TensorFlow to filter the data and predict environmental changes and crisis risks.

[0402] Furthermore, the server integrates an emotion engine to assess the user's emotional state. To do this, it utilizes the smartphone's camera and microphone, employing emotion recognition technology to determine the user's psychological state. Based on this information, it adjusts the intensity and content of warnings. For example, if a user is feeling anxious, it delivers information in a gentle tone and suggests reassuring measures. Conversely, for a calm user, it provides specific numerical data and detailed countermeasures.

[0403] Users can receive personalized alerts based on emotion recognition through a smartphone app. These alerts enable real-time awareness of local environmental risks and support appropriate action decisions. For example, if there is a possibility of flooding, and the emotion engine determines that the user is in an unstable state, it will offer a gentle suggestion such as, "There is a risk of flooding, so we recommend checking evacuation locations."

[0404] An example of an input prompt for a generative AI model is, "Suggest how to deliver a flood warning softly when the target user is deemed to be feeling anxious." This prompt serves as a reference for the generative AI in creating the most appropriate warning based on the user's emotions.

[0405] In this way, a system in which servers, terminals, and users work together as a unified whole provides appropriate environmental information and enables effective responses that are tailored to the user's emotions.

[0406] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0407] Step 1:

[0408] The server collects environmental data in real time via aircraft and ground equipment. This data includes abnormal weather information such as temperature, humidity, and precipitation. The input is raw data from sensors, and the output is clean environmental data that has been initially filtered. Filtering is performed to remove noise from the data and convert it into a format suitable for analysis.

[0409] Step 2:

[0410] The server analyzes collected environmental data using artificial intelligence algorithms. Specifically, it uses Azure Machine Learning and TensorFlow for pattern recognition and anomaly detection. The input is filtered environmental data, and the output is predictions of regional changes and crisis risks. An AI model is applied to analyze future risks based on the data.

[0411] Step 3:

[0412] The server uses an emotion engine to evaluate the user's psychological state. It analyzes input data from the smartphone's camera and microphone to identify the user's emotions. The input consists of the user's facial expressions and voice tone, while the output is data indicating the user's emotional state. The emotion analysis module converts the raw data into features and matches them against a learned model to identify emotions.

[0413] Step 4:

[0414] The device generates warning messages by combining the user's emotional state, obtained from the emotion engine, with environmental prediction results from an AI algorithm. An AI model for generating prompts selects a tone and expression appropriate to the user's emotions, customizing the message. Input is emotional state and risk data, while output is a specific warning message. It can generate warnings in a softer tone or information with adjusted levels of detail.

[0415] Step 5:

[0416] The user reviews the warning message received through the device and takes the recommended action as needed. The prompt-generated warnings are tailored to the user's emotional state, encouraging quick and appropriate action. The output is the warning information the user receives, influencing their final decision.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] [Third Embodiment]

[0421] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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".

[0433] This invention provides a system and method for collecting environmental data in real time and analyzing that data. One embodiment involves efficiently collecting data using aircraft or ground equipment, making predictions and warnings based on that data, and finally generating a report.

[0434] The server controls the drone based on pre-configured parameters, measuring temperature, humidity, CO2 concentration, and other parameters from above the target area. A large network of sensors is deployed on the ground to collect detailed environmental data. This collected data is then transmitted to the server.

[0435] The terminal receives data from the server and performs initial filtering. This removes inaccurate or unnecessary data, ensuring that analysis can be performed effectively.

[0436] The server analyzes the received data using artificial intelligence algorithms. The analysis detects anomalous patterns by comparing them with past data, and identifies trends in environmental changes. It also uses machine learning models to predict future weather conditions and disaster risks.

[0437] If the forecast indicates an increased risk of extreme weather or disaster, the server will promptly issue a real-time warning. The warning will include specific countermeasures to encourage users to take swift action.

[0438] Based on the warnings received, users can adjust their crop management methods or decide to suspend activities in specific areas. The server then provides users with regularly generated weekly and monthly environmental reports, allowing them to review past conditions and plan future actions.

[0439] This system enables effective management of environmental conservation activities and supports rapid decision-making. For example, it allows for early detection of high-temperature stress in agricultural areas and the implementation of improved water management.

[0440] The following describes the processing flow.

[0441] Step 1:

[0442] The server sends data collection instructions to aircraft and ground equipment based on a pre-configured schedule. The aircraft follows the designated flight path, and ground equipment measures various environmental parameters, collecting data in real time.

[0443] Step 2:

[0444] The server receives the collected raw data and performs initial processing. This initial processing involves detecting outliers and filtering out data that does not need to be processed.

[0445] Step 3:

[0446] The server passes the initially processed data to an artificial intelligence algorithm for advanced data analysis. This analysis specifically detects unusual fluctuations and patterns, generating data to visualize environmental conditions.

[0447] Step 4:

[0448] Based on the analysis results, the server uses machine learning models to predict future environmental conditions and disaster risks. These predictions include short-term weather forecasts and long-term environmental change forecasts.

[0449] Step 5:

[0450] Based on predictions, the server generates and sends real-time warning messages about potential risks to the terminal. The warnings include details of the risk and suggested countermeasures.

[0451] Step 6:

[0452] The terminal notifies the user of any received warning messages. The user can then check the details of the warning via the terminal and take necessary action promptly.

[0453] Step 7:

[0454] The server periodically compiles analysis results and warning information, automatically generating weekly or monthly environment reports. These reports are provided to users in digital format to support decision-making based on historical data.

[0455] (Example 1)

[0456] 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."

[0457] To provide a system that enables accurate and efficient data filtering and prediction in real-time collection and analysis of environmental information, allowing for the rapid identification of abnormal environmental changes and disaster risks, and the implementation of appropriate countermeasures.

[0458] 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.

[0459] In this invention, the server includes means for using an aircraft or ground device to collect environmental information in real time, means for initial filtering of the received environmental information to remove inaccurate information, and means for predicting environmental changes and disaster risks based on the analysis results. This enables early detection of abnormal environments and rapid response.

[0460] "Real-time" refers to a format where data collection and processing occur immediately, with minimal time delay.

[0461] "Environmental information" refers to information that includes data about the surrounding environment, such as temperature, humidity, and CO2 concentration.

[0462] "Aircraft or ground equipment" refers to equipment and devices used to collect environmental data in the air and on the ground.

[0463] An "artificial intelligence algorithm" refers to a computational method used to analyze data and recognize patterns and trends.

[0464] A "machine learning model" refers to a data analysis model used to learn from past data and predict future outcomes.

[0465] "Filtering" refers to the process of removing inaccurate or unnecessary information from collected data.

[0466] An "environmental report" refers to a report that compiles collected environmental information and the results of its analysis.

[0467] An "abnormal pattern" refers to data fluctuations or trends that fall outside the normal range.

[0468] A "warning" refers to a notification that informs the user in advance of potential risks or problems.

[0469] This invention provides a system for collecting and analyzing environmental information in real time. The central role of the entire system is played by a "server," which handles everything from collecting and analyzing environmental information to issuing warnings and generating reports.

[0470] The server collects environmental information using aircraft or ground equipment. Drones are used for aerial collection, while a diverse sensor network is employed on the ground. Drones accurately collect various environmental data, such as temperature, humidity, and CO2 concentration, and transmit it to the server via wireless communication. Technologies used include GPS location information and various weather sensors.

[0471] The collected data undergoes initial filtering by the terminal. This process removes inaccurate information and unnecessary data, improving the accuracy of the analysis. An artificial intelligence algorithm on the server then analyzes this filtered data. Analysis libraries such as Sci-kit Learn and TensorFlow, written in Python, are used to detect anomalous patterns by comparing the collected historical database with the current data.

[0472] Next, the server uses a machine learning model to predict future environmental changes and disaster risks. Based on the prediction results, if an anomaly risk is detected, the server sends a real-time warning to the user. This warning includes specific countermeasures, allowing the user to take immediate action.

[0473] When users receive a warning, they can take practical actions, such as reviewing crop management methods or temporarily suspending work in specific areas. The server automatically generates and provides users with weekly and monthly environmental reports. Reports based on historical data make it easier for users to plan for the future.

[0474] As a concrete example, this system makes it possible to detect plant stress caused by high temperatures early in agricultural areas and adjust irrigation systems accordingly. An example of a prompt sentence to be input to the generated AI model is, "Based on weather data from the past month, please suggest the optimal irrigation schedule for crops." Implementing this system will enable rapid responses to environmental changes and efficient planning based on those responses.

[0475] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0476] Step 1:

[0477] The server controls aircraft and ground equipment to collect environmental information in real time. Pre-configured parameters for the target area are used as input, and data on temperature, humidity, and CO2 concentration are obtained as output. Sensors mounted on the drone measure this data and transmit it to the server wirelessly. During this process, location information is also transmitted using GPS data.

[0478] Step 2:

[0479] The terminal filters the raw environmental information received from the server. It receives various environmental data transmitted from drones and ground equipment as input, removing inaccurate information and other errors. As output, clean data suitable for analysis is generated. This filtering process is performed using a specific algorithm to improve data accuracy.

[0480] Step 3:

[0481] The server analyzes filtered data using artificial intelligence algorithms. The input is clean, filtered data. This data is compared with historical databases to detect anomalous patterns. The output provides analysis results regarding trends in environmental changes and potential problems. Advanced data analysis is achieved by using Python machine learning libraries.

[0482] Step 4:

[0483] The server uses a machine learning model to predict future environmental conditions based on the analysis results. It uses the analysis results obtained in step 3 as input to estimate future weather conditions and disaster risks. The output is a dataset containing details and probabilities of the predicted risks. This includes a prediction algorithm utilizing a generative AI model.

[0484] Step 5:

[0485] The server issues real-time warnings to users based on predicted risks. The predicted data from step 4 is used as input, and based on that data, it generates warnings with specific countermeasures. The output is a notification message sent to the user. This process is carried out via the warning system.

[0486] Step 6:

[0487] Users receive warnings from the server and take necessary actions. As input, users read the details of the warning and take actions such as changing agricultural management methods or temporarily suspending activities in the area. The output is the impact of the user's actions on the real environment. This ensures responsiveness to environmental changes.

[0488] Step 7:

[0489] The server automatically generates and provides users with periodic environment reports based on historical data and analysis results. The input uses all data and results from past processing, resulting in reports that provide users with information to support future planning. The output consists of detailed weekly and monthly reports. Tools are used to visualize the analysis data.

[0490] (Application Example 1)

[0491] 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."

[0492] Conventional environmental data collection systems have been insufficient in supporting the real-time use of collected data and rapid decision-making. Furthermore, they lacked the information necessary for users to intuitively understand and act upon countermeasures against environmental changes and risks. In particular, in agricultural regions, where rapid and appropriate action in response to environmental changes is crucial, current systems have been inadequate in providing this support.

[0493] 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.

[0494] In this invention, the server includes means for using an aerial or ground device to collect environmental data in real time, means for using an artificial intelligence algorithm to analyze the collected environmental data, and means for displaying the analysis results on a mobile terminal and prompting the user to adjust their actions based on the environmental information. This enables rapid understanding of environmental conditions and appropriate decision-making.

[0495] "Real-time" refers to the time frame in which information is compiled and made available.

[0496] "Environmental data" refers to information related to the surrounding natural environment, such as temperature, humidity, and CO2 concentration.

[0497] "Flight equipment" refers to machines such as drones and aircraft that collect data using the air.

[0498] "Ground equipment" refers to devices that collect data using sensors and devices installed on the ground.

[0499] An "artificial intelligence algorithm" refers to a computational method used to analyze received data and recognize trends and anomalies.

[0500] "Mobile devices" refer to portable computing devices such as smartphones and tablets.

[0501] "Analysis results" refer to conclusions and predictions derived by artificial intelligence algorithms based on collected data.

[0502] A "user" refers to anyone who uses this system and makes decisions based on environmental information.

[0503] "Adjusting actions" refers to optimizing work content and countermeasures based on the environmental information provided.

[0504] To realize this invention, a system will be constructed that collects environmental data in real time using flying devices and ground-based devices. Drones will be used as flying devices, and a sensor network will be installed as ground-based devices. The data collected by these devices will be transmitted to a server and analyzed by artificial intelligence algorithms. The server will use machine learning libraries such as Python and TensorFlow to analyze the received data and recognize trends and anomalies.

[0505] The analysis results are transmitted to mobile devices via the internet. These mobile devices include smartphones and tablets. These devices display the analysis results to the user through applications built using frameworks such as React Native. Based on the analysis results, the user understands the environmental information and adjusts their actions accordingly.

[0506] As a concrete example, it is possible to detect high-temperature stress in agricultural areas early and improve water management. The server analyzes temperature and humidity data and issues a warning to the user when it detects an abnormal pattern. The warning includes specific countermeasures, for example, sending a notification such as, "The temperature is expected to exceed 35 degrees Celsius next Wednesday. We recommend water management."

[0507] When using a generative AI model, the following are examples of prompt statements:

[0508] "I'd like to know about the environmental risks to agricultural work next week. Please provide the weather forecast for next week and appropriate countermeasures."

[0509] These prompts allow AI models to provide specific temperature predictions and work guidelines.

[0510] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0511] Step 1:

[0512] The server receives environmental data such as temperature, humidity, and CO2 concentration from flying and ground-based devices. This includes the process of drones and ground sensors collecting various measurement data and transmitting it to the server wirelessly. The input is environmental data, and the output is raw data stored on the server.

[0513] Step 2:

[0514] The server applies an initial filter to the received environmental data. It detects and removes missing and outlier values ​​while maintaining data accuracy. The input is the raw data obtained in step 1, and the output is the refined data.

[0515] Step 3:

[0516] The server uses artificial intelligence algorithms to analyze the filtered data. Here, a machine learning model (e.g., TensorFlow) is used to compare the current data with historical data and recognize anomalous patterns and trends. The input is the refined data, and the output is the recognition of anomalous patterns and trends in environmental changes as a result of the analysis.

[0517] Step 4:

[0518] The server predicts future weather conditions and disaster risks based on the analysis results. Using a prediction algorithm, it calculates the most likely risks in a specific region. The input is the analysis results from step 3, and the output is risk prediction data.

[0519] Step 5:

[0520] The server generates and sends real-time warnings to terminals based on the generated risk predictions. These warnings include specific action plans, such as recommendations like "We recommend hydration management." The input is risk prediction data, and the output is a warning message.

[0521] Step 6:

[0522] The device displays received warning messages to the user via the application. These messages are displayed in a user interface built using React Native. The input is the warning message, and the output is the notification to the user.

[0523] Step 7:

[0524] Based on the information provided by the device, the user takes actions such as adjusting crop management methods. This requires the user to understand the presented information and make appropriate decisions. The input, as an intervention, is the notification content, and the output is the user's specific actions.

[0525] 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.

[0526] This invention provides a system that integrates a user emotion engine with real-time environmental data collection, analysis, prediction, warning, and report generation functions. This system enhances the accuracy of environmental monitoring and user responsiveness, enabling more personalized information delivery.

[0527] The server first collects environmental data from aircraft and ground equipment, filters it, and then analyzes the data using artificial intelligence algorithms. Based on the analysis results, predictions of environmental changes and disaster risks are made. If the predicted risk increases, the server generates a warning message in real time.

[0528] The emotion engine evaluates the user's psychological state based on sensor data and past response history. The device adjusts how warnings are presented according to the user's emotions identified by the emotion engine. This adjustment includes the intensity of the warning, the wording, and the level of detail in the recommended actions.

[0529] For example, if a risk of drought due to high temperatures is predicted, and the emotional engine determines that the user is stressed, the device will deliver a warning message in a softer, more subdued tone and suggest ways to alleviate anxiety. On the other hand, if the user is calm, the device will provide a warning with specific figures and offer detailed countermeasures.

[0530] Users receive alerts from their devices and can devise and implement countermeasures in a way that best suits their emotional state. The server automatically generates weekly or monthly environmental reports based on all the data and provides them to the user. These reports include insights based on environmental changes and user responses, supporting informed decision-making.

[0531] Thus, the present invention extends environmental monitoring technology and enables information transmission that takes user emotions into consideration, thereby supporting more effective environmental conservation activities.

[0532] The following describes the processing flow.

[0533] Step 1:

[0534] The server collects environmental data in real time from aircraft and ground equipment. This includes measurements of temperature, humidity, and CO2 concentration. The data is transmitted to the server immediately.

[0535] Step 2:

[0536] The server performs initial processing on the collected data, filtering out unnecessary data and outliers. This ensures the accuracy and reliability of the data.

[0537] Step 3:

[0538] The server feeds the filtered data into an artificial intelligence algorithm for deep analysis. This analysis aims to understand the current state of the environment and identify the causes of any specific changes detected.

[0539] Step 4:

[0540] Based on the analysis results, the server predicts future environmental changes and potential disaster risks. If a high risk is identified, preparations for issuing a warning are made.

[0541] Step 5:

[0542] The emotion engine activates, reading the user's emotional state from sensor data and past history via the device. This allows for an evaluation of the user's psychological and emotional responses.

[0543] Step 6:

[0544] The device customizes warning messages based on the evaluation results of the emotion engine. The content and wording are adjusted according to the user's emotional state.

[0545] Step 7:

[0546] Users receive customized warnings through their devices and select appropriate actions based on the content of those warnings. This process takes into account the user's emotional state.

[0547] Step 8:

[0548] The server collects user response data and uses it to improve the accuracy of future warnings and suggestions. It also generates weekly or monthly reports based on the analysis results and provides them to the user.

[0549] Through this process, the system provides appropriate warnings based on environmental data and information that takes user emotions into consideration.

[0550] (Example 2)

[0551] 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."

[0552] Current environmental monitoring systems collect and analyze environmental data, but generally do not consider the individual psychological state of users when notifying them of this data. This can potentially cause stress and anxiety for users. Furthermore, the lack of specific, user-appropriate countermeasures makes it difficult to respond appropriately to the risks they face.

[0553] 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.

[0554] In this invention, the server includes means for using an aircraft or ground equipment to collect environmental information in real time, means for using a machine learning algorithm to analyze the collected environmental information, and means for using an emotion recognition engine to evaluate the user's psychological state and adjust warnings based on the evaluation. This makes it possible to provide information and propose specific countermeasures that are appropriate to the user's psychological state.

[0555] "Collecting environmental information in real time" means using aircraft or ground equipment to acquire current environmental conditions as data without any time delay.

[0556] "Using machine learning algorithms" means utilizing mathematical methods in data analysis so that computers can learn on their own and detect patterns and anomalies.

[0557] "Predicting environmental changes and disaster risks" means estimating the likelihood of future environmental changes and natural disasters based on collected data.

[0558] "Issuing real-time warnings" means immediately alerting users before predicted risks materialize.

[0559] "Automatically generating environmental reports" means automatically creating periodic reports based on environmental data collected and analyzed over a certain period.

[0560] "Using an emotion recognition engine" means utilizing technology that analyzes the user's psychological state and changes the way information is presented accordingly.

[0561] "Proposing specific countermeasures tailored to the user's psychological state" means considering the user's current emotions and mental state, and presenting specific action guidelines and coping strategies that are optimized for those conditions.

[0562] "Visualizing on a geographic information system" means visually representing collected environmental information on a map so that its location and situation can be understood intuitively.

[0563] The embodiments for carrying out the present invention will now be described. In this invention, a system is constructed that collects environmental information in real time and provides information according to the user's psychological state.

[0564] Server roles and operations:

[0565] The server collects environmental data from aircraft and ground equipment through various sensors. This environmental data includes information such as temperature, humidity, wind speed, and precipitation. The collected data is filtered using Python scripts to remove incomplete or noisy data. Subsequently, data analysis is performed using machine learning frameworks such as TensorFlow and PyTorch. Specifically, LSTM (Long Short-Term Memory Network) and CNN (Convolutional Neural Network) are used to predict environmental changes and disaster risks.

[0566] Terminal functions and operation:

[0567] The device is equipped with an emotion recognition engine that analyzes the user's psychological state based on their biometric data and past response history. Devices used in this process may include a heart rate sensor and a camera. The device utilizes a generative AI model to generate warning messages appropriate to the user's psychological state. For example, if the user is feeling stressed, a message in a softer tone will be presented.

[0568] User roles and operations:

[0569] Users receive personalized warning messages from their devices, enabling them to consider and implement appropriate countermeasures based on risk information. For example, in response to the effects of predicted high temperatures, specific advice such as, "Today's temperature is higher than expected, so we recommend that you stay hydrated," is provided.

[0570] Specific examples and prompt statements:

[0571] For example, if a drought due to high temperatures is predicted, the server will notify the user with a warning based on that prediction. If the emotion recognition engine determines that the user's psychological state is stressed, the device will provide a gentle message such as, "Please remain calm. Please consider the following measures."

[0572] An example of a prompt to input into the generative AI model would be an instruction such as, "Generate a message that warns residents in areas affected by high temperatures without causing them stress." Based on this prompt, the generative AI will create the optimal message and provide it to the user.

[0573] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0574] Step 1:

[0575] The server collects environmental data in real time from aircraft and ground facilities. This collected data includes environmental information such as temperature, humidity, wind speed, and precipitation. The environmental information obtained as input data is filtered using a Python script. Specifically, missing values ​​are removed and noisy data is smoothed to make it analyzable. A clean environmental dataset is obtained as output.

[0576] Step 2:

[0577] The server analyzes the filtered data using machine learning frameworks such as TensorFlow and PyTorch. In this step, models such as LSTM and CNN are used to extract features from environmental data and predict risks. Specifically, data is input into the analysis model, and predictions of environmental changes and disaster risks are output. The output is a predicted risk value for a specific point in the future.

[0578] Step 3:

[0579] The server generates warning messages in real time based on the analyzed data. If the predicted risk increases, it issues an appropriate warning to the user. The output includes a warning message and specific recommended countermeasures. This information is sent to the terminal.

[0580] Step 4:

[0581] The device uses an emotion recognition engine to analyze the user's psychological state based on the received warning message. Input includes the user's biometric data and past response history. Based on this data, the device analyzes the user's emotions and adjusts the message tone and content accordingly as the output.

[0582] Step 5:

[0583] The user receives a tailored warning message from the device. This message contains information appropriate to the user's psychological state and presents specific actionable countermeasures. Based on the information presented, the user can select and take appropriate action to address the risk.

[0584] Step 6:

[0585] The server automatically generates weekly or monthly reports based on data collected and generated throughout the entire processing process. Inputs include environmental data, user response data, and analysis results. Outputs are comprehensive reports summarizing environmental changes and user responses, offering long-term insights and recommendations for users.

[0586] (Application Example 2)

[0587] 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."

[0588] In modern society, climate change and environmental disasters occur frequently, and in urban areas in particular, there is a need for real-time monitoring of environmental data and rapid response based on that data. However, conventional systems have difficulty issuing personalized warnings that take into account the user's psychological state, making it difficult to take appropriate action. Furthermore, simply visualizing environmental data is insufficient to provide information tailored to individual users. Therefore, there is a need for the development of a new system that combines real-time environmental change prediction with means of providing information based on individual emotions.

[0589] 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.

[0590] In this invention, the server includes means for using an aircraft or ground device to collect area data in real time, means for using an artificial intelligence algorithm to analyze the collected area data, means for predicting area fluctuations and crisis risks based on the analysis results, and means integrating an emotion engine that evaluates the user's emotions and adjusts the method of presenting warnings based on their psychological state. This enables real-time prediction of environmental risks and the presentation of appropriate warnings and countermeasures according to the emotional state of each individual user.

[0591] "Real-time" refers to processing information and data immediately and providing results almost instantly.

[0592] "Regional data" refers to diverse environmental information such as weather, topography, and temperature related to a specific region.

[0593] An "artificial intelligence algorithm" refers to a set of logical steps that a computer uses to analyze data, learn, and make predictions.

[0594] "Crisis risk" refers to the potential danger associated with natural disasters and environmental changes.

[0595] An "emotion engine" refers to technology that analyzes a user's psychological state and evaluates their emotions.

[0596] "Integrated means" refers to methods of combining multiple functions or technologies to operate as a single system.

[0597] "Adjusting how warnings are presented" refers to changing the content and intensity of notifications according to the user's psychological state.

[0598] "Individual emotional state" refers to the mental and emotional state of each individual user.

[0599] This invention is based on a system that provides personalized environmental warnings to users in smart cities. A server collects real-time regional data using aircraft and ground equipment and analyzes it using artificial intelligence algorithms. This analysis process utilizes platforms such as Azure Machine Learning and TensorFlow to filter the data and predict environmental changes and crisis risks.

[0600] Furthermore, the server integrates an emotion engine to assess the user's emotional state. To do this, it utilizes the smartphone's camera and microphone, employing emotion recognition technology to determine the user's psychological state. Based on this information, it adjusts the intensity and content of warnings. For example, if a user is feeling anxious, it delivers information in a gentle tone and suggests reassuring measures. Conversely, for a calm user, it provides specific numerical data and detailed countermeasures.

[0601] Users can receive personalized alerts based on emotion recognition through a smartphone app. These alerts enable real-time awareness of local environmental risks and support appropriate action decisions. For example, if there is a possibility of flooding, and the emotion engine determines that the user is in an unstable state, it will offer a gentle suggestion such as, "There is a risk of flooding, so we recommend checking evacuation locations."

[0602] An example of an input prompt for a generative AI model is, "Suggest how to deliver a flood warning softly when the target user is deemed to be feeling anxious." This prompt serves as a reference for the generative AI in creating the most appropriate warning based on the user's emotions.

[0603] In this way, a system in which servers, terminals, and users work together as a unified whole provides appropriate environmental information and enables effective responses that are tailored to the user's emotions.

[0604] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0605] Step 1:

[0606] The server collects environmental data in real time via aircraft and ground equipment. This data includes abnormal weather information such as temperature, humidity, and precipitation. The input is raw data from sensors, and the output is clean environmental data that has been initially filtered. Filtering is performed to remove noise from the data and convert it into a format suitable for analysis.

[0607] Step 2:

[0608] The server analyzes collected environmental data using artificial intelligence algorithms. Specifically, it uses Azure Machine Learning and TensorFlow for pattern recognition and anomaly detection. The input is filtered environmental data, and the output is predictions of regional changes and crisis risks. An AI model is applied to analyze future risks based on the data.

[0609] Step 3:

[0610] The server uses an emotion engine to evaluate the user's psychological state. It analyzes input data from the smartphone's camera and microphone to identify the user's emotions. The input consists of the user's facial expressions and voice tone, while the output is data indicating the user's emotional state. The emotion analysis module converts the raw data into features and matches them against a learned model to identify emotions.

[0611] Step 4:

[0612] The device generates warning messages by combining the user's emotional state, obtained from the emotion engine, with environmental prediction results from an AI algorithm. An AI model for generating prompts selects a tone and expression appropriate to the user's emotions, customizing the message. Input is emotional state and risk data, while output is a specific warning message. It can generate warnings in a softer tone or information with adjusted levels of detail.

[0613] Step 5:

[0614] The user reviews the warning message received through the device and takes the recommended action as needed. The prompt-generated warnings are tailored to the user's emotional state, encouraging quick and appropriate action. The output is the warning information the user receives, influencing their final decision.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] [Fourth Embodiment]

[0619] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0620] 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.

[0621] 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).

[0622] 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.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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".

[0632] This invention provides a system and method for collecting environmental data in real time and analyzing that data. One embodiment involves efficiently collecting data using aircraft or ground equipment, making predictions and warnings based on that data, and finally generating a report.

[0633] The server controls the drone based on pre-configured parameters, measuring temperature, humidity, CO2 concentration, and other parameters from above the target area. A large network of sensors is deployed on the ground to collect detailed environmental data. This collected data is then transmitted to the server.

[0634] The terminal receives data from the server and performs initial filtering. This removes inaccurate or unnecessary data, ensuring that analysis can be performed effectively.

[0635] The server analyzes the received data using artificial intelligence algorithms. The analysis detects anomalous patterns by comparing them with past data, and identifies trends in environmental changes. It also uses machine learning models to predict future weather conditions and disaster risks.

[0636] If the forecast indicates an increased risk of extreme weather or disaster, the server will promptly issue a real-time warning. The warning will include specific countermeasures to encourage users to take swift action.

[0637] Based on the warnings received, users can adjust their crop management methods or decide to suspend activities in specific areas. The server then provides users with regularly generated weekly and monthly environmental reports, allowing them to review past conditions and plan future actions.

[0638] This system enables effective management of environmental conservation activities and supports rapid decision-making. For example, it allows for early detection of high-temperature stress in agricultural areas and the implementation of improved water management.

[0639] The following describes the processing flow.

[0640] Step 1:

[0641] The server sends data collection instructions to aircraft and ground equipment based on a pre-configured schedule. The aircraft follows the designated flight path, and ground equipment measures various environmental parameters, collecting data in real time.

[0642] Step 2:

[0643] The server receives the collected raw data and performs initial processing. This initial processing involves detecting outliers and filtering out data that does not need to be processed.

[0644] Step 3:

[0645] The server passes the initially processed data to an artificial intelligence algorithm for advanced data analysis. This analysis specifically detects unusual fluctuations and patterns, generating data to visualize environmental conditions.

[0646] Step 4:

[0647] Based on the analysis results, the server uses machine learning models to predict future environmental conditions and disaster risks. These predictions include short-term weather forecasts and long-term environmental change forecasts.

[0648] Step 5:

[0649] Based on predictions, the server generates and sends real-time warning messages about potential risks to the terminal. The warnings include details of the risk and suggested countermeasures.

[0650] Step 6:

[0651] The terminal notifies the user of any received warning messages. The user can then check the details of the warning via the terminal and take necessary action promptly.

[0652] Step 7:

[0653] The server periodically compiles analysis results and warning information, automatically generating weekly or monthly environment reports. These reports are provided to users in digital format to support decision-making based on historical data.

[0654] (Example 1)

[0655] 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".

[0656] To provide a system that enables accurate and efficient data filtering and prediction in real-time collection and analysis of environmental information, allowing for the rapid identification of abnormal environmental changes and disaster risks, and the implementation of appropriate countermeasures.

[0657] 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.

[0658] In this invention, the server includes means for using an aircraft or ground device to collect environmental information in real time, means for initial filtering of the received environmental information to remove inaccurate information, and means for predicting environmental changes and disaster risks based on the analysis results. This enables early detection of abnormal environments and rapid response.

[0659] "Real-time" refers to a format where data collection and processing occur immediately, with minimal time delay.

[0660] "Environmental information" refers to information that includes data about the surrounding environment, such as temperature, humidity, and CO2 concentration.

[0661] "Aircraft or ground equipment" refers to equipment and devices used to collect environmental data in the air and on the ground.

[0662] An "artificial intelligence algorithm" refers to a computational method used to analyze data and recognize patterns and trends.

[0663] A "machine learning model" refers to a data analysis model used to learn from past data and predict future outcomes.

[0664] "Filtering" refers to the process of removing inaccurate or unnecessary information from collected data.

[0665] An "environmental report" refers to a report that compiles collected environmental information and the results of its analysis.

[0666] An "abnormal pattern" refers to data fluctuations or trends that fall outside the normal range.

[0667] A "warning" refers to a notification that informs the user in advance of potential risks or problems.

[0668] This invention provides a system for collecting and analyzing environmental information in real time. The central role of the entire system is played by a "server," which handles everything from collecting and analyzing environmental information to issuing warnings and generating reports.

[0669] The server collects environmental information using aircraft or ground equipment. Drones are used for aerial collection, while a diverse sensor network is employed on the ground. Drones accurately collect various environmental data, such as temperature, humidity, and CO2 concentration, and transmit it to the server via wireless communication. Technologies used include GPS location information and various weather sensors.

[0670] The collected data undergoes initial filtering by the terminal. This process removes inaccurate information and unnecessary data, improving the accuracy of the analysis. An artificial intelligence algorithm on the server then analyzes this filtered data. Analysis libraries such as Sci-kit Learn and TensorFlow, written in Python, are used to detect anomalous patterns by comparing the collected historical database with the current data.

[0671] Next, the server uses a machine learning model to predict future environmental changes and disaster risks. Based on the prediction results, if an anomaly risk is detected, the server sends a real-time warning to the user. This warning includes specific countermeasures, allowing the user to take immediate action.

[0672] When users receive a warning, they can take practical actions, such as reviewing crop management methods or temporarily suspending work in specific areas. The server automatically generates and provides users with weekly and monthly environmental reports. Reports based on historical data make it easier for users to plan for the future.

[0673] As a concrete example, this system makes it possible to detect plant stress caused by high temperatures early in agricultural areas and adjust irrigation systems accordingly. An example of a prompt sentence to be input to the generated AI model is, "Based on weather data from the past month, please suggest the optimal irrigation schedule for crops." Implementing this system will enable rapid responses to environmental changes and efficient planning based on those responses.

[0674] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0675] Step 1:

[0676] The server controls aircraft and ground equipment to collect environmental information in real time. Pre-configured parameters for the target area are used as input, and data on temperature, humidity, and CO2 concentration are obtained as output. Sensors mounted on the drone measure this data and transmit it to the server wirelessly. During this process, location information is also transmitted using GPS data.

[0677] Step 2:

[0678] The terminal filters the raw environmental information received from the server. It receives various environmental data transmitted from drones and ground equipment as input, removing inaccurate information and other errors. As output, clean data suitable for analysis is generated. This filtering process is performed using a specific algorithm to improve data accuracy.

[0679] Step 3:

[0680] The server analyzes filtered data using artificial intelligence algorithms. The input is clean, filtered data. This data is compared with historical databases to detect anomalous patterns. The output provides analysis results regarding trends in environmental changes and potential problems. Advanced data analysis is achieved by using Python machine learning libraries.

[0681] Step 4:

[0682] The server uses a machine learning model to predict future environmental conditions based on the analysis results. It uses the analysis results obtained in step 3 as input to estimate future weather conditions and disaster risks. The output is a dataset containing details and probabilities of the predicted risks. This includes a prediction algorithm utilizing a generative AI model.

[0683] Step 5:

[0684] The server issues real-time warnings to users based on predicted risks. The predicted data from step 4 is used as input, and based on that data, it generates warnings with specific countermeasures. The output is a notification message sent to the user. This process is carried out via the warning system.

[0685] Step 6:

[0686] Users receive warnings from the server and take necessary actions. As input, users read the details of the warning and take actions such as changing agricultural management methods or temporarily suspending activities in the area. The output is the impact of the user's actions on the real environment. This ensures responsiveness to environmental changes.

[0687] Step 7:

[0688] The server automatically generates and provides users with periodic environment reports based on historical data and analysis results. The input uses all data and results from past processing, resulting in reports that provide users with information to support future planning. The output consists of detailed weekly and monthly reports. Tools are used to visualize the analysis data.

[0689] (Application Example 1)

[0690] 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".

[0691] Conventional environmental data collection systems have been insufficient in supporting the real-time use of collected data and rapid decision-making. Furthermore, they lacked the information necessary for users to intuitively understand and act upon countermeasures against environmental changes and risks. In particular, in agricultural regions, where rapid and appropriate action in response to environmental changes is crucial, current systems have been inadequate in providing this support.

[0692] 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.

[0693] In this invention, the server includes means for using an aerial or ground device to collect environmental data in real time, means for using an artificial intelligence algorithm to analyze the collected environmental data, and means for displaying the analysis results on a mobile terminal and prompting the user to adjust their actions based on the environmental information. This enables rapid understanding of environmental conditions and appropriate decision-making.

[0694] "Real-time" refers to the time frame in which information is compiled and made available.

[0695] "Environmental data" refers to information related to the surrounding natural environment, such as temperature, humidity, and CO2 concentration.

[0696] "Flight equipment" refers to machines such as drones and aircraft that collect data using the air.

[0697] "Ground equipment" refers to devices that collect data using sensors and devices installed on the ground.

[0698] An "artificial intelligence algorithm" refers to a computational method used to analyze received data and recognize trends and anomalies.

[0699] "Mobile devices" refer to portable computing devices such as smartphones and tablets.

[0700] "Analysis results" refer to conclusions and predictions derived by artificial intelligence algorithms based on collected data.

[0701] A "user" refers to anyone who uses this system and makes decisions based on environmental information.

[0702] "Adjusting actions" refers to optimizing work content and countermeasures based on the environmental information provided.

[0703] To realize this invention, a system will be constructed that collects environmental data in real time using flying devices and ground-based devices. Drones will be used as flying devices, and a sensor network will be installed as ground-based devices. The data collected by these devices will be transmitted to a server and analyzed by artificial intelligence algorithms. The server will use machine learning libraries such as Python and TensorFlow to analyze the received data and recognize trends and anomalies.

[0704] The analysis results are transmitted to mobile devices via the internet. These mobile devices include smartphones and tablets. These devices display the analysis results to the user through applications built using frameworks such as React Native. Based on the analysis results, the user understands the environmental information and adjusts their actions accordingly.

[0705] As a concrete example, it is possible to detect high-temperature stress in agricultural areas early and improve water management. The server analyzes temperature and humidity data and issues a warning to the user when it detects an abnormal pattern. The warning includes specific countermeasures, for example, sending a notification such as, "The temperature is expected to exceed 35 degrees Celsius next Wednesday. We recommend water management."

[0706] When using a generative AI model, the following are examples of prompt statements:

[0707] "I'd like to know about the environmental risks to agricultural work next week. Please provide the weather forecast for next week and appropriate countermeasures."

[0708] These prompts allow AI models to provide specific temperature predictions and work guidelines.

[0709] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0710] Step 1:

[0711] The server receives environmental data such as temperature, humidity, and CO2 concentration from flying and ground-based devices. This includes the process of drones and ground sensors collecting various measurement data and transmitting it to the server wirelessly. The input is environmental data, and the output is raw data stored on the server.

[0712] Step 2:

[0713] The server applies an initial filter to the received environmental data. It detects and removes missing and outlier values ​​while maintaining data accuracy. The input is the raw data obtained in step 1, and the output is the refined data.

[0714] Step 3:

[0715] The server uses artificial intelligence algorithms to analyze the filtered data. Here, a machine learning model (e.g., TensorFlow) is used to compare the current data with historical data and recognize anomalous patterns and trends. The input is the refined data, and the output is the recognition of anomalous patterns and trends in environmental changes as a result of the analysis.

[0716] Step 4:

[0717] The server predicts future weather conditions and disaster risks based on the analysis results. Using a prediction algorithm, it calculates the most likely risks in a specific region. The input is the analysis results from step 3, and the output is risk prediction data.

[0718] Step 5:

[0719] The server generates and sends real-time warnings to terminals based on the generated risk predictions. These warnings include specific action plans, such as recommendations like "We recommend hydration management." The input is risk prediction data, and the output is a warning message.

[0720] Step 6:

[0721] The device displays received warning messages to the user via the application. These messages are displayed in a user interface built using React Native. The input is the warning message, and the output is the notification to the user.

[0722] Step 7:

[0723] Based on the information provided by the device, the user takes actions such as adjusting crop management methods. This requires the user to understand the presented information and make appropriate decisions. The input, as an intervention, is the notification content, and the output is the user's specific actions.

[0724] 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.

[0725] This invention provides a system that integrates a user emotion engine with real-time environmental data collection, analysis, prediction, warning, and report generation functions. This system enhances the accuracy of environmental monitoring and user responsiveness, enabling more personalized information delivery.

[0726] The server first collects environmental data from aircraft and ground equipment, filters it, and then analyzes the data using artificial intelligence algorithms. Based on the analysis results, predictions of environmental changes and disaster risks are made. If the predicted risk increases, the server generates a warning message in real time.

[0727] The emotion engine evaluates the user's psychological state based on sensor data and past response history. The device adjusts how warnings are presented according to the user's emotions identified by the emotion engine. This adjustment includes the intensity of the warning, the wording, and the level of detail in the recommended actions.

[0728] For example, if a risk of drought due to high temperatures is predicted, and the emotional engine determines that the user is stressed, the device will deliver a warning message in a softer, more subdued tone and suggest ways to alleviate anxiety. On the other hand, if the user is calm, the device will provide a warning with specific figures and offer detailed countermeasures.

[0729] Users receive alerts from their devices and can devise and implement countermeasures in a way that best suits their emotional state. The server automatically generates weekly or monthly environmental reports based on all the data and provides them to the user. These reports include insights based on environmental changes and user responses, supporting informed decision-making.

[0730] Thus, the present invention extends environmental monitoring technology and enables information transmission that takes user emotions into consideration, thereby supporting more effective environmental conservation activities.

[0731] The following describes the processing flow.

[0732] Step 1:

[0733] The server collects environmental data in real time from aircraft and ground equipment. This includes measurements of temperature, humidity, and CO2 concentration. The data is transmitted to the server immediately.

[0734] Step 2:

[0735] The server performs initial processing on the collected data, filtering out unnecessary data and outliers. This ensures the accuracy and reliability of the data.

[0736] Step 3:

[0737] The server feeds the filtered data into an artificial intelligence algorithm for deep analysis. This analysis aims to understand the current state of the environment and identify the causes of any specific changes detected.

[0738] Step 4:

[0739] Based on the analysis results, the server predicts future environmental changes and potential disaster risks. If a high risk is identified, preparations for issuing a warning are made.

[0740] Step 5:

[0741] The emotion engine activates, reading the user's emotional state from sensor data and past history via the device. This allows for an evaluation of the user's psychological and emotional responses.

[0742] Step 6:

[0743] The device customizes warning messages based on the evaluation results of the emotion engine. The content and wording are adjusted according to the user's emotional state.

[0744] Step 7:

[0745] Users receive customized warnings through their devices and select appropriate actions based on the content of those warnings. This process takes into account the user's emotional state.

[0746] Step 8:

[0747] The server collects user response data and uses it to improve the accuracy of future warnings and suggestions. It also generates weekly or monthly reports based on the analysis results and provides them to the user.

[0748] Through this process, the system provides appropriate warnings based on environmental data and information that takes user emotions into consideration.

[0749] (Example 2)

[0750] 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".

[0751] Current environmental monitoring systems collect and analyze environmental data, but generally do not consider the individual psychological state of users when notifying them of this data. This can potentially cause stress and anxiety for users. Furthermore, the lack of specific, user-appropriate countermeasures makes it difficult to respond appropriately to the risks they face.

[0752] 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.

[0753] In this invention, the server includes means for using an aircraft or ground equipment to collect environmental information in real time, means for using a machine learning algorithm to analyze the collected environmental information, and means for using an emotion recognition engine to evaluate the user's psychological state and adjust warnings based on the evaluation. This makes it possible to provide information and propose specific countermeasures that are appropriate to the user's psychological state.

[0754] "Collecting environmental information in real time" means using aircraft or ground equipment to acquire current environmental conditions as data without any time delay.

[0755] "Using machine learning algorithms" means utilizing mathematical methods in data analysis so that computers can learn on their own and detect patterns and anomalies.

[0756] "Predicting environmental changes and disaster risks" means estimating the likelihood of future environmental changes and natural disasters based on collected data.

[0757] "Issuing real-time warnings" means immediately alerting users before predicted risks materialize.

[0758] "Automatically generating environmental reports" means automatically creating periodic reports based on environmental data collected and analyzed over a certain period.

[0759] "Using an emotion recognition engine" means utilizing technology that analyzes the user's psychological state and changes the way information is presented accordingly.

[0760] "Proposing specific countermeasures tailored to the user's psychological state" means considering the user's current emotions and mental state, and presenting specific action guidelines and coping strategies that are optimized for those conditions.

[0761] "Visualizing on a geographic information system" means visually representing collected environmental information on a map so that its location and situation can be understood intuitively.

[0762] The embodiments for carrying out the present invention will now be described. In this invention, a system is constructed that collects environmental information in real time and provides information according to the user's psychological state.

[0763] Server roles and operations:

[0764] The server collects environmental data from aircraft and ground equipment through various sensors. This environmental data includes information such as temperature, humidity, wind speed, and precipitation. The collected data is filtered using Python scripts to remove incomplete or noisy data. Subsequently, data analysis is performed using machine learning frameworks such as TensorFlow and PyTorch. Specifically, LSTM (Long Short-Term Memory Network) and CNN (Convolutional Neural Network) are used to predict environmental changes and disaster risks.

[0765] Terminal functions and operation:

[0766] The device is equipped with an emotion recognition engine that analyzes the user's psychological state based on their biometric data and past response history. Devices used in this process may include a heart rate sensor and a camera. The device utilizes a generative AI model to generate warning messages appropriate to the user's psychological state. For example, if the user is feeling stressed, a message in a softer tone will be presented.

[0767] User roles and operations:

[0768] Users receive personalized warning messages from their devices, enabling them to consider and implement appropriate countermeasures based on risk information. For example, in response to the effects of predicted high temperatures, specific advice such as, "Today's temperature is higher than expected, so we recommend that you stay hydrated," is provided.

[0769] Specific examples and prompt statements:

[0770] For example, if a drought due to high temperatures is predicted, the server will notify the user with a warning based on that prediction. If the emotion recognition engine determines that the user's psychological state is stressed, the device will provide a gentle message such as, "Please remain calm. Please consider the following measures."

[0771] An example of a prompt to input into the generative AI model would be an instruction such as, "Generate a message that warns residents in areas affected by high temperatures without causing them stress." Based on this prompt, the generative AI will create the optimal message and provide it to the user.

[0772] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0773] Step 1:

[0774] The server collects environmental data in real time from aircraft and ground facilities. This collected data includes environmental information such as temperature, humidity, wind speed, and precipitation. The environmental information obtained as input data is filtered using a Python script. Specifically, missing values ​​are removed and noisy data is smoothed to make it analyzable. A clean environmental dataset is obtained as output.

[0775] Step 2:

[0776] The server analyzes the filtered data using machine learning frameworks such as TensorFlow and PyTorch. In this step, models such as LSTM and CNN are used to extract features from environmental data and predict risks. Specifically, data is input into the analysis model, and predictions of environmental changes and disaster risks are output. The output is a predicted risk value for a specific point in the future.

[0777] Step 3:

[0778] The server generates warning messages in real time based on the analyzed data. If the predicted risk increases, it issues an appropriate warning to the user. The output includes a warning message and specific recommended countermeasures. This information is sent to the terminal.

[0779] Step 4:

[0780] The device uses an emotion recognition engine to analyze the user's psychological state based on the received warning message. Input includes the user's biometric data and past response history. Based on this data, the device analyzes the user's emotions and adjusts the message tone and content accordingly as the output.

[0781] Step 5:

[0782] The user receives a tailored warning message from the device. This message contains information appropriate to the user's psychological state and presents specific actionable countermeasures. Based on the information presented, the user can select and take appropriate action to address the risk.

[0783] Step 6:

[0784] The server automatically generates weekly or monthly reports based on data collected and generated throughout the entire processing process. Inputs include environmental data, user response data, and analysis results. Outputs are comprehensive reports summarizing environmental changes and user responses, offering long-term insights and recommendations for users.

[0785] (Application Example 2)

[0786] 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".

[0787] In modern society, climate change and environmental disasters occur frequently, and in urban areas in particular, there is a need for real-time monitoring of environmental data and rapid response based on that data. However, conventional systems have difficulty issuing personalized warnings that take into account the user's psychological state, making it difficult to take appropriate action. Furthermore, simply visualizing environmental data is insufficient to provide information tailored to individual users. Therefore, there is a need for the development of a new system that combines real-time environmental change prediction with means of providing information based on individual emotions.

[0788] 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.

[0789] In this invention, the server includes means for using an aircraft or ground device to collect area data in real time, means for using an artificial intelligence algorithm to analyze the collected area data, means for predicting area fluctuations and crisis risks based on the analysis results, and means integrating an emotion engine that evaluates the user's emotions and adjusts the method of presenting warnings based on their psychological state. This enables real-time prediction of environmental risks and the presentation of appropriate warnings and countermeasures according to the emotional state of each individual user.

[0790] "Real-time" refers to processing information and data immediately and providing results almost instantly.

[0791] "Regional data" refers to diverse environmental information such as weather, topography, and temperature related to a specific region.

[0792] An "artificial intelligence algorithm" refers to a set of logical steps that a computer uses to analyze data, learn, and make predictions.

[0793] "Crisis risk" refers to the potential danger associated with natural disasters and environmental changes.

[0794] An "emotion engine" refers to technology that analyzes a user's psychological state and evaluates their emotions.

[0795] "Integrated means" refers to methods of combining multiple functions or technologies to operate as a single system.

[0796] "Adjusting how warnings are presented" refers to changing the content and intensity of notifications according to the user's psychological state.

[0797] "Individual emotional state" refers to the mental and emotional state of each individual user.

[0798] This invention is based on a system that provides personalized environmental warnings to users in smart cities. A server collects real-time regional data using aircraft and ground equipment and analyzes it using artificial intelligence algorithms. This analysis process utilizes platforms such as Azure Machine Learning and TensorFlow to filter the data and predict environmental changes and crisis risks.

[0799] Furthermore, the server integrates an emotion engine to assess the user's emotional state. To do this, it utilizes the smartphone's camera and microphone, employing emotion recognition technology to determine the user's psychological state. Based on this information, it adjusts the intensity and content of warnings. For example, if a user is feeling anxious, it delivers information in a gentle tone and suggests reassuring measures. Conversely, for a calm user, it provides specific numerical data and detailed countermeasures.

[0800] Users can receive personalized alerts based on emotion recognition through a smartphone app. These alerts enable real-time awareness of local environmental risks and support appropriate action decisions. For example, if there is a possibility of flooding, and the emotion engine determines that the user is in an unstable state, it will offer a gentle suggestion such as, "There is a risk of flooding, so we recommend checking evacuation locations."

[0801] An example of an input prompt for a generative AI model is, "Suggest how to deliver a flood warning softly when the target user is deemed to be feeling anxious." This prompt serves as a reference for the generative AI in creating the most appropriate warning based on the user's emotions.

[0802] In this way, a system in which servers, terminals, and users work together as a unified whole provides appropriate environmental information and enables effective responses that are tailored to the user's emotions.

[0803] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0804] Step 1:

[0805] The server collects environmental data in real time via aircraft and ground equipment. This data includes abnormal weather information such as temperature, humidity, and precipitation. The input is raw data from sensors, and the output is clean environmental data that has been initially filtered. Filtering is performed to remove noise from the data and convert it into a format suitable for analysis.

[0806] Step 2:

[0807] The server analyzes collected environmental data using artificial intelligence algorithms. Specifically, it uses Azure Machine Learning and TensorFlow for pattern recognition and anomaly detection. The input is filtered environmental data, and the output is predictions of regional changes and crisis risks. An AI model is applied to analyze future risks based on the data.

[0808] Step 3:

[0809] The server uses an emotion engine to evaluate the user's psychological state. It analyzes input data from the smartphone's camera and microphone to identify the user's emotions. The input consists of the user's facial expressions and voice tone, while the output is data indicating the user's emotional state. The emotion analysis module converts the raw data into features and matches them against a learned model to identify emotions.

[0810] Step 4:

[0811] The device generates warning messages by combining the user's emotional state, obtained from the emotion engine, with environmental prediction results from an AI algorithm. An AI model for generating prompts selects a tone and expression appropriate to the user's emotions, customizing the message. Input is emotional state and risk data, while output is a specific warning message. It can generate warnings in a softer tone or information with adjusted levels of detail.

[0812] Step 5:

[0813] The user reviews the warning message received through the device and takes the recommended action as needed. The prompt-generated warnings are tailored to the user's emotional state, encouraging quick and appropriate action. The output is the warning information the user receives, influencing their final decision.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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."

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] The following is further disclosed regarding the embodiments described above.

[0836] (Claim 1)

[0837] Means of using aircraft or ground equipment to collect environmental data in real time,

[0838] A means of using an artificial intelligence algorithm for analyzing the aforementioned collected environmental data,

[0839] A means for predicting environmental changes and disaster risks based on the aforementioned analysis results,

[0840] A means for issuing real-time warnings for the aforementioned predicted risks,

[0841] A means of automatically generating environmental reports on a weekly or monthly basis,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, further comprising means for proposing specific countermeasures when the warning means detects an abnormal situation.

[0845] (Claim 3)

[0846] The system according to claim 1, further comprising means for visualizing the aforementioned environmental data on a geographic information system.

[0847] "Example 1"

[0848] (Claim 1)

[0849] Means of using aircraft or ground equipment to collect environmental information in real time,

[0850] A means of using an artificial intelligence algorithm for analyzing the collected environmental information,

[0851] A means for predicting environmental changes and disaster risks based on the aforementioned analysis results,

[0852] A means for issuing real-time warnings for the aforementioned predicted risks,

[0853] A means of automatically generating environmental reports on a weekly or monthly basis,

[0854] A means of initially filtering the received environmental information to remove inaccurate information,

[0855] A means of predicting future environmental conditions using machine learning models,

[0856] A means of comparing with past information to detect abnormal patterns,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, further comprising means for proposing specific countermeasures when the warning means detects an abnormal situation.

[0860] (Claim 3)

[0861] The system according to claim 1, further comprising means for visualizing the aforementioned environmental information on a geographic information system.

[0862] "Application Example 1"

[0863] (Claim 1)

[0864] Means of using flying or ground equipment to collect environmental data in real time,

[0865] A means of using an artificial intelligence algorithm for analyzing the aforementioned collected environmental data,

[0866] A means for predicting environmental changes and disaster risks based on the aforementioned analysis results,

[0867] A means for issuing real-time warnings for the aforementioned predicted risks,

[0868] A means of automatically generating environmental reports on a weekly or monthly basis,

[0869] A means of displaying analysis results on a mobile device and encouraging users to adjust their behavior based on environmental information,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, further comprising means for proposing specific countermeasures when the warning means detects an abnormal situation.

[0873] (Claim 3)

[0874] The system according to claim 1, further comprising means for visualizing the environmental data on a geographic information platform.

[0875] "Example 2 of combining an emotion engine"

[0876] (Claim 1)

[0877] Means of using aircraft or ground equipment to collect environmental information in real time,

[0878] A means of using a machine learning algorithm to analyze the collected environmental information,

[0879] A means for predicting environmental changes and disaster risks based on the aforementioned analysis results,

[0880] A means for issuing real-time warnings for the aforementioned predicted risks,

[0881] A means of automatically generating environmental reports on a weekly or monthly basis,

[0882] A means of using an emotion recognition engine that evaluates the user's psychological state and adjusts warnings based on the evaluation,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, further comprising means for proposing specific countermeasures in accordance with the user's psychological state when the warning means detects an abnormal situation.

[0886] (Claim 3)

[0887] The system according to claim 1, further comprising means for visualizing the aforementioned environmental information on a geographic information system.

[0888] "Application example 2 when combining with an emotional engine"

[0889] (Claim 1)

[0890] Means of using aircraft or ground equipment to collect region data in real time,

[0891] A means of using an artificial intelligence algorithm for analyzing the collected region data,

[0892] A means for predicting regional fluctuations and crisis risks based on the aforementioned analysis results,

[0893] A means for issuing real-time warnings for the aforementioned predicted risks,

[0894] A means of automatically generating area reports on a weekly or monthly basis,

[0895] A means that integrates an emotion engine that evaluates the user's emotions and adjusts the way warnings are presented based on their psychological state,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, further comprising means for proposing specific measures when the warning means detects an abnormal situation, and means for generating a warning message corresponding to an individual emotional state.

[0899] (Claim 3)

[0900] The system according to claim 1, further comprising means for visualizing the aforementioned region data on a geographic information system, and means for displaying warning content adjusted based on individual emotional states. [Explanation of Symbols]

[0901] 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 of using flying or ground equipment to collect environmental data in real time, A means of using an artificial intelligence algorithm for analyzing the aforementioned collected environmental data, A means for predicting environmental changes and disaster risks based on the aforementioned analysis results, A means for issuing real-time warnings for the aforementioned predicted risks, A means of automatically generating environmental reports on a weekly or monthly basis, A means of displaying analysis results on a mobile device and encouraging users to adjust their behavior based on environmental information, A system that includes this.

2. The system according to claim 1, further comprising means for proposing specific countermeasures when the warning means detects an abnormal situation.

3. The system according to claim 1, further comprising means for visualizing the environmental data on a geographic information platform.

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