system

Abstract

Provide a system. A reading means for collecting monitoring data; an analysis means for analyzing the collected data to detect anomalies; alert generation means for generating an alert when an abnormality is detected; reporting means for generating reports based on the generated alerts; display means for displaying the generated report to a user; A system including:

Description

[Technical Field]

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

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

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

[0004] To protect the future of the Earth and leave it beautiful for future generations, it is necessary to detect environmental problems such as illegal logging and pollution early. However, current methods make it difficult to monitor these problems in real time and respond quickly. There is also a lack of mechanisms for efficiently processing the vast amounts of data collected from multiple sensors and detecting anomalies. For this reason, there is a need for a system that enables early detection of environmental problems and rapid response. [Means for solving the problem]

[0005] This invention provides an environmental monitoring system that enables early detection and rapid response to environmental problems. Specifically, the system includes a reading means for collecting monitoring data, an analysis means for analyzing the collected data to detect anomalies, an alert generation means for generating an alert when an anomaly is detected, a reporting means for creating a report based on the generated alert, and a display means for displaying the created report to a user. The reading means is connected to multiple sensors via a network and acquires environmental data from each sensor. The analysis means preprocesses the acquired data and recognizes patterns, and applies an anomaly detection algorithm. The alert generation means generates appropriate alert messages based on the type and severity of the anomaly, and distributes the alerts to relevant parties via communication means. The display means provides a user interface, allowing users to view the generated reports and enter feedback. This enables real-time monitoring of environmental problems and rapid response, thereby protecting the future of the Earth.

[0006] "Monitoring data" is information about the environment obtained from monitoring devices such as sensors.

[0007] The "reading means" is a function or device for acquiring monitoring data from a monitoring device.

[0008] "Analysis means" refers to a function or device that processes collected monitoring data and detects abnormalities.

[0009] An "anomaly" is an unusual pattern or value detected in environmental data that indicates a potential problem.

[0010] An "alert generator" is a function or device that generates a warning or notification based on a detected anomaly.

[0011] A "reporting means" is a function or device that creates a report based on the generated alert.

[0012] "Display means" refers to a function or device that displays the created report or notification to the user.

[0013] A "sensor" is a measurement device for collecting environmental data.

[0014] A "network" is an infrastructure that allows multiple devices to communicate with each other.

[0015] "Data preprocessing" is the process of organizing data and removing unnecessary parts before analysis.

[0016] "Pattern recognition" is the art of identifying specific patterns or trends in data.

[0017] An "anomaly detection algorithm" is a computational method for identifying and detecting anomalies in data.

[0018] "Communication" means the method or technique used to send alerts or reports to users or administrators.

[0019] A "user interface" is a screen or input means through which a user interacts with a system.

[0020] "Feedback" is the opinion or information that a user provides to the system. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0023] First, the terms used in the following description will be explained.

[0024] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0025] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0026] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0027] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0034] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0035] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0039] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0040] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] This invention relates to a system for early detection of environmental problems based on monitoring data, enabling rapid response. This system integrates the reading, analysis, anomaly detection, alert generation, report creation, and display of monitoring data.

[0043] 1. Reading monitoring data

[0044] Processing Description:

[0045] The server automatically collects environmental data from multiple sensors connected via a network. The sensors include various devices that measure temperature, humidity, CO2 concentration, and wood movement. The server reads data from each sensor at regular intervals and temporarily stores this data. This process provides real-time environmental information.

[0046] Examples:

[0047] The server collects data on temperature, humidity, and wood movement every five minutes from multiple sensors installed in the forest, and stores this data in a database on the server.

[0048] 2. Data Analysis

[0049] Processing Description:

[0050] The server analyzes the collected data in real time. First, it preprocesses the data to filter out incomplete data and outliers. Then it applies pattern recognition algorithms to detect anomalies by comparing it with past data. This analysis process is used to identify sudden changes and unusual patterns.

[0051] Examples:

[0052] The server analyzes the collected timber movement data and detects sudden declines in timber volume in specific areas, which are recorded as anomalies.

[0053] 3. Anomaly detection and alert generation

[0054] Processing Description:

[0055] The server detects anomalies based on the analysis results. Detected anomalies are added to an anomaly list. Then, an appropriate alert message is generated depending on the type and severity of the anomaly. The generated alert is set to immediately notify relevant parties.

[0056] Examples:

[0057] The server generates an alert saying "A sudden decrease in timber has been detected in region X" and notifies the administrator by email.

[0058] 4. Create a report

[0059] Processing Description:

[0060] The server generates detailed reports based on detected anomalies and generated alerts, including the time and location of the anomaly, its type, predicted impact, and recommended actions. Reports are generated periodically and provided to relevant parties.

[0061] Examples:

[0062] A detailed report of "Sudden timber decline in area X" is generated, detailing when and where the problem occurred, the specific nature of the anomaly, and the remedial action taken.

[0063] 5. View the report

[0064] Processing Description:

[0065] The terminal displays the reports sent from the server to the user. The user can easily check these reports through the provided user interface. The user can also leave comments on the reports and request additional investigations.

[0066] Examples:

[0067] The user uses a laptop computer to view the "Rapid Timber Decline Report in Region X" through a web browser and instructs on necessary measures.

[0068] System benefits

[0069] This system makes it possible to detect environmental problems such as illegal logging and pollution early, allowing for swift action. Furthermore, detailed reports are provided, making it easier for relevant parties to share information and take effective measures. This will help protect the global environment and leave a beautiful planet for future generations.

[0070] The processing flow will be explained below.

[0071] Step 1:

[0072] The server connects to multiple sensors via the network. Specifically, it establishes a connection using the IP address and port information of the sensor. Once the connection is established, it is ready to periodically acquire data from the sensor.

[0073] Step 2:

[0074] The server reads environmental data from each sensor. For example, it calls the sensor.read() method to obtain data such as temperature, humidity, CO2 concentration, and the amount of wood movement. The obtained data is temporarily stored in memory or a database.

[0075] Step 3:

[0076] The server performs preprocessing on the collected data. Specifically, it filters out incomplete data and abnormal values. It also performs processing to fill in missing parts of the data. This improves the accuracy of the analysis.

[0077] Step 4:

[0078] The server performs pattern recognition on the pre-processed data, comparing historical data with current data and applying anomaly detection algorithms to identify unusual patterns or sudden changes, for example, using moving averages or sudden change detection algorithms.

[0079] Step 5:

[0080] The server detects anomalies from the analysis results. Detected anomalies are added to an anomaly list. If multiple anomalies are detected, each anomaly is recorded in the list.

[0081] Step 6:

[0082] The server generates alerts based on the anomaly list. Depending on the type and severity of the anomaly, it creates an appropriate alert message. For example, it generates a message such as "Sudden timber decline detected in region X."

[0083] Step 7:

[0084] The server then distributes the generated alerts to relevant parties via email, SMS, and a dedicated dashboard, allowing them to respond to any anomalies immediately.

[0085] Step 8:

[0086] The server generates a detailed report based on the anomalies detected and the alerts generated, including the time, location, type, predicted impact, and recommended remedial action of the anomaly.

[0087] Step 9:

[0088] The server then sends the generated report to the user, typically via email or a web server.

[0089] Step 10:

[0090] The terminal displays the report sent from the server to the user, who can easily check the report through the provided user interface, allowing the user to take any necessary measures immediately.

[0091] Step 11:

[0092] The terminal provides an interface for receiving feedback and additional information from users. Users can leave comments on reports or request further investigation. Feedback is sent to the server and used to improve the overall system, if necessary.

[0093] Example 1

[0094] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0095] Currently, there are many systems that require early detection and rapid response to environmental problems, but the functions of data collection, analysis, anomaly detection, alert generation, report creation, and display are all dispersed, making it difficult to integrate the entire system.In addition, because the functions are not seamlessly linked, it is difficult to process data in real time or respond quickly, and there is a risk that the accuracy and immediacy of information will be lost.

[0096] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0097] In this invention, the server includes an acquisition means for collecting monitoring data, an analysis means for analyzing the collected data in real time, filtering out abnormal values ​​and incomplete data, and detecting abnormalities by applying a pattern recognition algorithm, an alert generation means for generating an alert message in response to the detected abnormality and notifying relevant parties, a reporting means for creating a detailed report based on the generated alert and describing the date and time of the abnormality detection, location, type, predicted impact, and recommended response, and a display means for providing an interface that allows a user to easily view the created report and for comments or requests for additional investigation. This makes it possible to seamlessly perform the entire process from collecting environmental monitoring data to analyzing, detecting abnormalities, generating alerts, and creating and displaying reports.

[0098] The "acquisition means" is a function that periodically acquires environmental data from multiple sensor devices via a network in order to collect monitoring data.

[0099] The "analysis means" is a function that preprocesses the collected data, filters out incomplete data and outliers, and then applies pattern recognition algorithms to detect anomalies.

[0100] The "alert generation means" is a function that generates an alert message in response to a detected abnormality and notifies the relevant parties of the message.

[0101] "Reporting tools" refers to the ability to create detailed reports based on generated alerts, detailing the time, location, type, predicted impact, and recommended response of the anomaly.

[0102] The "display means" is a function that provides an interface that allows the user to easily check the created report, and to make comments or request additional investigations.

[0103] This invention relates to a method for seamlessly integrating a series of processes through an environmental monitoring system, from automatically collecting monitoring data, analyzing it, detecting abnormalities, generating alerts, creating reports, and displaying them to users. This system includes the following major hardware and software components:

[0104] Hardware Components

[0105] 1. Sensor Devices

[0106] This is a device for measuring various environmental data, such as temperature, humidity, CO2 concentration, and the amount of wood movement.

[0107] 2. Server

[0108] It performs the primary functions of data collection, analysis, alerting, and reporting.

[0109] 3. Terminal

[0110] The user uses the terminal to view the report sent from the server and, if necessary, make comments or request additional investigations.

[0111] Software Components

[0112] 1. Data Collection Software

[0113] The server periodically retrieves environmental data from the sensor devices, using HTTP or MQTT protocols for this process.

[0114] 2. Database Management System

[0115] The collected data is stored in a database on the server for later analysis, often using an in-memory database (e.g., Redis).

[0116] 3. Data Analysis Software

[0117] The server runs programs to analyze the collected data in real time, using Python scripts and machine learning models (e.g., LSTM architecture).

[0118] 4. Alert Generation Software

[0119] Based on the detected anomaly, an alert message is generated, and this message is sent to relevant parties via email or SMS via the SMTP server.

[0120] 5. Report Generation Software

[0121] The server creates reports based on the generated alerts and saves them in HTML or PDF format, using Python libraries (e.g. Jinja2) to insert data into templates.

[0122] 6. User Interface

[0123] The terminal provides a user interface via a web browser, allowing users to easily view the generated reports and also allows users to add comments through this interface.

[0124] Specific example explanation

[0125] Specific operational examples include the following seeds:

[0126] Monitoring Data Collection

[0127] The server collects data on temperature, humidity, and wood movement every five minutes from sensors installed in the forest, and stores this data in a database on the server.

[0128] Data analysis

[0129] The server analyzes the collected timber movement data and detects sudden declines in timber volume in specific areas, which are recorded as anomalies.

[0130] Anomaly detection and alerting

[0131] The server generates an alert saying "A sudden decrease in timber has been detected in region X" and notifies the administrator by email.

[0132] Creating a report

[0133] A detailed report of "Sudden timber decline in area X" is generated, detailing when and where the problem occurred, the specific nature of the anomaly, and the remedial action taken.

[0134] Viewing Reports

[0135] The user uses a laptop computer to view the "Rapid Timber Decline Report in Region X" through a web browser and instructs on necessary measures.

[0136] Examples of prompt statements

[0137] "Please give us an overview of the system that detects sudden changes in the amount of wood movement within a forest in real time and generates an alert if an abnormality is discovered."

[0138] The above procedures and methods make it possible to realize a system that seamlessly collects, analyzes, detects abnormalities, generates alerts, and creates and displays reports on environmental monitoring data.

[0139] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0140] Step 1:

[0141] Obtaining monitoring data

[0142] Specifically, the server acquires environmental data from multiple sensors connected via a network.

[0143] Input: Real-time environmental data sent from sensors (temperature, humidity, CO2 concentration, wood movement, etc.)

[0144] Output: Temporarily saved environmental data

[0145] Specific behavior:

[0146] The sensor sends the measured data to the server using HTTP or MQTT protocol.

[0147] The data received by the server is temporarily stored in an in-memory database (e.g. Redis) to enable fast access.

[0148] Step 2:

[0149] Data Preprocessing

[0150] Specifically: The server preprocesses the collected environmental data, filtering out incomplete data and outliers, and preparing the data for analysis.

[0151] Input: Temporarily saved environmental data

[0152] Output: Filtered data for analysis

[0153] Specific behavior:

[0154] The server runs a Python script to complete missing values ​​and remove irrational data from the collected data.

[0155] The filtered data is prepared as a dataset for analysis.

[0156] Step 3:

[0157] Applying pattern recognition algorithms and detecting anomalies

[0158] What it means: The server applies pattern recognition algorithms to the pre-processed data and compares it with historical data to detect anomalies.

[0159] Input: Filtered data for analysis

[0160] Output: Detected anomalies

[0161] Specific behavior:

[0162] The server uses a machine learning model (e.g., LSTM architecture) to analyze the data for analysis in real time.

[0163] Detects abnormal patterns and sudden changes and lists them as abnormal data.

[0164] Step 4:

[0165] Alert generation and notification

[0166] Specific Description: The server generates an alert message based on the detected anomaly and notifies relevant parties.

[0167] Input: Detected anomaly data

[0168] Output: Generated alert messages and notifications

[0169] Specific behavior:

[0170] The server generates an alert message using a template based on the type and severity of the abnormality.

[0171] The generated alerts are notified to the administrator via email or SMS via the SMTP server.

[0172] Step 5:

[0173] Creating a report

[0174] Specifically: The server generates detailed reports based on generated alerts, including the time, location, type, predicted impact, and recommended actions to detect the anomaly.

[0175] Input: The generated alert message

[0176] Output: Generated report (HTML or PDF format)

[0177] Specific behavior:

[0178] The server uses a Python library (e.g., Jinja2) to insert the alert data into the template.

[0179] Generate the completed report in PDF or HTML format and save it to your file system.

[0180] Step 6:

[0181] Viewing Reports

[0182] Specific explanation: The terminal displays the report sent from the server to the user. The user checks the report through the provided user interface and makes comments or requests for further investigation as necessary.

[0183] Input: Generated report (HTML or PDF format)

[0184] Output: The report displayed in the user interface and any user comments

[0185] Specific behavior:

[0186] The device issues an HTTP request to retrieve the latest report from the server.

[0187] The terminal displays the report via a web browser and provides an interface for the user to enter comments and requests for further investigation.

[0188] (Application example 1)

[0189] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0190] Existing monitoring systems have the problem of being unable to detect anomalies in environmental data in real time and promptly notify the appropriate parties. In particular, delayed response after an anomaly is detected can potentially exacerbate environmental problems. To solve this problem, a system is needed that integrates real-time monitoring of environmental data, anomaly detection, alert generation, and prompt notification to the relevant parties.

[0191] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0192] In this invention, the server includes a reading means for collecting monitoring data, an analyzing means for analyzing the collected data to detect abnormalities, an alert generating means for generating an alert when an abnormality is detected, a reporting means for creating a report based on the created alert, a display means for displaying the created report to a user, and a notifying means for sending the alert to relevant parties using various notifying means. This makes it possible to monitor environmental data in real time and to notify relevant parties promptly and appropriately when an abnormality occurs.

[0193] "Monitoring data" refers to data such as temperature, humidity, CO2 concentration, and amount of wood movement collected from environmental sensors and various measuring devices.

[0194] A "reader" is a mechanism for acquiring data from multiple sensors over a network.

[0195] "Analysis means" is a function for preprocessing collected data and detecting anomalies using pattern recognition algorithms.

[0196] The "alert generation means" is a mechanism for generating an alert message to notify relevant parties based on a detected abnormality.

[0197] The "reporting tool" is a function that creates a detailed report based on the generated alert, describing the problem occurrence and recommended countermeasures.

[0198] The "display means" is an interface that displays the created report to the user so that the user can easily check it.

[0199] "Notification means" refers to a mechanism for sending the generated alert message to relevant parties via push notification, email notification, etc.

[0200] This invention relates to a system for early detection of environmental problems based on monitoring data, enabling prompt responses. This system includes the following elements: reading means for collecting monitoring data, analysis means for analyzing the collected data to detect abnormalities, alert generation means for generating an alert when an abnormality is detected, reporting means for creating a report based on the generated alert, display means for displaying the created report to a user, and notification means for sending the alert to relevant parties using various notification means.

[0201] 1. Reading monitoring data

[0202] The server automatically collects environmental data from multiple sensors connected via a network. The sensors include various devices that measure temperature, humidity, CO2 concentration, and wood movement. The server reads data from each sensor at regular intervals and temporarily stores this data. This process provides real-time environmental information.

[0203] 2. Data Analysis

[0204] The server analyzes the collected data in real time. First, it preprocesses the data to filter out incomplete data and outliers. Then it applies pattern recognition algorithms to detect anomalies by comparing it with past data. This analysis process is used to identify sudden changes and unusual patterns.

[0205] 3. Anomaly detection and alert generation

[0206] The server detects anomalies based on the analysis results. Detected anomalies are added to an anomaly list. Then, an appropriate alert message is generated depending on the type and severity of the anomaly. The generated alert is set to immediately notify relevant parties.

[0207] 4. Create a report

[0208] The server generates detailed reports based on detected anomalies and generated alerts, including the time and location of the anomaly, its type, predicted impact, and recommended actions. Reports are generated periodically and provided to relevant parties.

[0209] 5. View the report

[0210] The terminal displays the reports sent from the server to the user. The user can easily check these reports through the provided user interface. The user can also leave comments on the reports and request additional investigations.

[0211] 6. Means of notification

[0212] The server is equipped with a notification means for sending the generated alert message to relevant parties. This notification is performed using push notifications, email notifications, etc. This allows relevant parties to respond quickly when an abnormality occurs.

[0213] The program of this system is implemented in the Python language and uses the following major libraries:

[0214] Requests library: Acquiring data from sensors

[0215] JSON Library: Data Analysis and Report Generation

[0216] SMTP Library: Email Notification

[0217] For example, the invention can be specifically implemented by inputting the following prompt sentences into a generative AI model:

[0218] text

[0219] "Design a program to analyze the data obtained from the sensors in real time, generate an alert and send a notification when the number of timber movements exceeds 100. Also create a detailed report of the generated alerts."

[0220] This invention makes it possible to monitor environmental data in real time and notify relevant parties promptly and appropriately if an abnormality occurs, thereby realizing early detection of environmental problems and rapid response.

[0221] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0222] Step 1:

[0223] The server acquires environmental data from various sensors via the network. The input is data sent from the sensors, and the output is environmental data stored in a temporary database on the server. Specifically, the server periodically sends API requests, receives JSON-formatted data from the sensors, parses it, and stores it in the database.

[0224] Step 2:

[0225] The server preprocesses the acquired data. The input is raw data acquired from the sensor, and the output is preprocessed data. Specifically, it filters out incomplete data and outliers, extracting only the necessary data. During this process, missing data is filled in and outliers are removed.

[0226] Step 3:

[0227] The server applies pattern recognition algorithms to the preprocessed data to detect anomalies. The input is the preprocessed data, and the output is the result of whether an anomaly was detected. Specifically, it analyzes patterns by comparing them with past data and using statistical models to determine whether an anomaly exists.

[0228] Step 4:

[0229] The server generates an alert when an anomaly is detected. The input is the anomaly detection result, and the output is an alert message. Specifically, it generates an alert message according to the type and severity of the anomaly, and adds that message to the anomaly list.

[0230] Step 5:

[0231] The server creates a detailed report based on the generated alert. The input is the alert message, and the output is a detailed report. Specifically, the server automatically generates a report that summarizes information such as the date and time the anomaly was detected, its location, the type of anomaly, its predicted impact, and recommended actions.

[0232] Step 6:

[0233] The server displays the generated report to the user. The input is a detailed report and the output is a report displayed through a user interface. Specifically, the server provides the report to the user through a web browser or a mobile application, allowing the user to view it.

[0234] Step 7:

[0235] The server generates an alert message and sends it to the relevant parties. The input is the alert message, and the output is the notification sent to the relevant parties. Specifically, the alert content is sent to the relevant parties using push notification, email notification, or SMS notification, allowing the relevant parties to respond to the anomaly quickly.

[0236] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0237] This invention relates to a system that uses monitoring data to detect environmental problems early and enable rapid response. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system dynamically adjusts display content and notifications based on the user's reactions.

[0238] 1. Reading monitoring data

[0239] Processing Description:

[0240] The server reads environmental data in real time from multiple sensors connected via a network, including temperature, humidity, CO2 concentration, and the amount of wood movement, and periodically acquires and stores data from each sensor.

[0241] Examples:

[0242] The server collects data every five minutes from temperature, humidity, CO2, and wood movement sensors installed in the forest, and stores the data in a database on the server for subsequent analysis.

[0243] 2. Data Analysis

[0244] Processing Description:

[0245] The server analyzes the collected environmental data. First, it preprocesses the data, filters out outliers, and fills incomplete data. Then, it uses pattern recognition algorithms to analyze the data and detect anomalies.

[0246] Examples:

[0247] The server preprocesses the acquired timber movement data and applies anomaly detection algorithms to detect sudden declines in timber volume in specific areas, thereby identifying areas with a high probability of illegal logging.

[0248] 3. Anomaly detection and alert generation

[0249] Processing Description:

[0250] The server detects anomalies from the analysis results and adds them to an anomaly list.Then, the alert generation means generates an appropriate alert message depending on the type and severity of the anomaly and notifies the relevant parties.

[0251] Examples:

[0252] After detecting a sudden decrease in timber in a specific area, the server creates an alert message such as "A sudden decrease in timber has been detected in area X" and notifies the administrator by email.

[0253] 4. Create a report

[0254] Processing Description:

[0255] The server generates a detailed report based on the detected anomalies and generated alerts, including the time and location of the anomaly, its type, predicted impact, and recommended remedial action, and sends the report to the relevant parties.

[0256] Examples:

[0257] A detailed report of the sudden timber loss is prepared and sent to the manager, including the date and time of detection, the location, specific details of the abnormality, and countermeasures.

[0258] 5. User Emotion Recognition and Response Adjustment

[0259] Processing Description:

[0260] The device uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, voice, typing speed, etc. to detect emotions such as pleasure, displeasure, and impatience. Based on this information, the device dynamically adjusts display content and notifications.

[0261] Examples:

[0262] When a user views a report, if the emotion engine detects that the user is anxious by analyzing their facial expressions, the server will provide more detailed information and an additional alert message, such as "Check out more information related to timber decline in region X."

[0263] 6. Report viewing and feedback

[0264] Processing Description:

[0265] The terminal displays the report sent from the server to the user. The user can easily check the report and take necessary measures through the provided user interface. The user can also leave comments on the report or request additional investigation. The user's feedback is sent to the server and used to improve the system.

[0266] Examples:

[0267] The user can view the report through a web browser on a laptop computer, issue instructions to immediately dispatch an investigation team to the anomalous area, add comments to the report, and send requests for future investigations to the server.

[0268] System benefits

[0269] This system makes it possible to detect environmental problems such as illegal logging and pollution early and respond quickly. Furthermore, by recognizing the user's emotions, it improves the user experience and supports more effective information provision and problem solving. As a result, it becomes easier to take concrete measures to protect the global environment and leave a beautiful planet for future generations.

[0270] The processing flow will be explained below.

[0271] Step 1:

[0272] The server connects to multiple sensors via the network. Specifically, it establishes a connection using the IP address and port number of the sensor. Once the connection is established, it is ready to periodically acquire data.

[0273] Step 2:

[0274] The server obtains environmental data from each sensor. For example, it calls the sensor.read() method to obtain data such as temperature, humidity, CO2 concentration, and the amount of wood movement. This data is collected in real time and stored in a database on the server.

[0275] Step 3:

[0276] The server performs preprocessing on the collected data, specifically filling in missing values, filtering out noise and outliers, and converting the data to a uniform format.

[0277] Step 4:

[0278] The server performs pattern recognition on the pre-processed data, comparing it with historical data and applying anomaly detection algorithms to detect unusual patterns or sudden changes, such as a sudden decrease in timber volume over a short period of time.

[0279] Step 5:

[0280] The server detects anomalies from the analysis results. Detected anomalies are added to an anomaly list. If multiple anomalies are detected, each anomaly is recorded in the list.

[0281] Step 6:

[0282] The server generates alerts based on the anomaly list. It creates appropriate alert messages depending on the type and severity of the anomaly. For example, it generates a message such as "Sudden timber decline detected in region X."

[0283] Step 7:

[0284] The server then distributes the generated alerts to relevant parties, specifically by sending notifications via email, SMS, and a dedicated dashboard, allowing relevant parties to respond to anomalies immediately.

[0285] Step 8:

[0286] The server generates a detailed report based on the detected anomalies and generated alerts, including when the anomaly was detected, its location, the type of anomaly, its predicted impact, and recommended remedial actions.

[0287] Step 9:

[0288] The server then sends the generated report to the user, who can then receive and review it, typically via email or a web server.

[0289] Step 10:

[0290] The terminal displays the report sent from the server to the user, who can easily check the report through the provided user interface, allowing the user to take any necessary measures immediately.

[0291] Step 11:

[0292] The device uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, voice, typing speed, etc. to detect the user's emotions. Based on the results, the device provides appropriate display content or additional input to respond to the user's reaction.

[0293] Step 12:

[0294] The device dynamically adjusts the display content based on the user's emotions detected by the emotion engine. For example, if the user is feeling anxious, the display content may include additional support information or encouraging messages.

[0295] Step 13:

[0296] The terminal provides an interface for receiving feedback and additional information from the user, who can leave comments on the report or request further investigation.

[0297] Step 14:

[0298] The server receives feedback from the devices and uses it to improve the overall system, adjusting system settings and alert generation rules accordingly.

[0299] Example 2

[0300] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0301] In modern society, early detection and rapid response to environmental problems are extremely important. However, existing monitoring systems often take time to analyze collected data and detect anomalies, resulting in delayed effective responses. Furthermore, they do not provide information tailored to the user's emotions, and the user experience is not sufficiently improved. This makes it difficult to quickly and effectively resolve problems. The purpose of this invention is to solve these problems by enabling early detection of environmental problems, taking appropriate measures, and improving the user experience.

[0302] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0303] In this invention, the server includes a reading means for collecting monitoring data, an analysis means for analyzing the collected data to detect abnormalities, an alert generation means for generating an alert when an abnormality is detected, a reporting means for creating a report based on the created alert, a display means for displaying the created report to the user, and an emotion recognition means for recognizing the user's emotions and dynamically adjusting the display content. This enables rapid collection and analysis of environmental data and early detection of abnormalities, and further realizes flexible information provision according to the user's emotions, thereby improving the user experience.

[0304] "Monitoring data" refers to numerical information obtained using sensors or other devices to measure environmental conditions and fluctuations.

[0305] A "reader" is a device or method for obtaining monitoring data from a sensor over a network.

[0306] The "analysis means" refers to an algorithm or method for processing the acquired monitoring data and determining whether or not there is an abnormality.

[0307] The "alert generation means" is a device or method that generates a message to notify relevant parties when an abnormality is detected by the analysis means.

[0308] A "reporting means" is a device or method that generates detailed reports based on generated alerts.

[0309] A "display means" is a device or method for visually presenting the generated report to a user.

[0310] The "emotion recognition means" is a device or method for analyzing a user's facial expression, voice, and typing speed to identify the user's emotion.

[0311] "Sensors" are measurement devices used to collect environmental data.

[0312] "Environmental data" refers to data that indicates the state of the natural environment, such as temperature, humidity, CO2 concentration, and the amount of wood movement.

[0313] "Preprocessing" is the process of shaping data in analytical means, removing outliers, and filling in missing values.

[0314] A "pattern recognition algorithm" is a mathematical technique or algorithm for detecting specific patterns or anomalies in data.

[0315] An "anomaly detection algorithm" is a specific algorithm for identifying anomalous data points in monitored data.

[0316] The present invention relates to an environmental monitoring system, and provides a method and system for monitoring specific environmental conditions and for early detection and notification of abnormalities. The system also has the ability to recognize a user's emotions and dynamically adjust the display content. Specific embodiments for implementing the invention are described below.

[0317] First, the server accesses multiple sensors via the network and periodically acquires environmental data from them. The main hardware used includes temperature sensors, humidity sensors, CO2 sensors, and wood movement sensors. The software used to acquire the data includes a library for executing HTTP requests and a database management system (e.g., MySQL (registered trademark)).

[0318] Specifically, the server sends an HTTP request to the IP address of each sensor every five minutes, parses the data returned from each sensor (e.g., temperature 30°C, humidity 60%, CO2 concentration 400 ppm, wood movement volume 10 cubic meters) in JSON format, and stores it in a database.

[0319] The server then analyzes the collected environmental data using software such as Python's pandas library and scikit-learn. The data is preprocessed by filtering outliers and imputing missing data. Pattern recognition algorithms are then applied to detect anomalies.

[0320] For example, the server executes the query "SELECT FROM environment_data WHERE timestamp >= NOW() - INTERVAL 5 MINUTE" and uses the z-score method to remove outliers from the retrieved data. It then applies the scikit-learn Isolation Forest algorithm to detect sudden changes in timber movement in a specific area. This process identifies areas suspected of illegal logging.

[0321] If an anomaly is detected, the server generates an alert message and notifies the relevant parties. At this time, the alert message is created using the Java® Spring Framework and sent via an email API (e.g., Azure® SendGrid) with the content, for example, "Sudden timber decline detected in region X."

[0322] The server also creates detailed reports based on anomaly and alert information. The reports are generated in PDF format using document generation tools such as LaTeX. The reports include the date and time the anomaly was detected, its location, type, predicted impact, and recommended countermeasures. The reports are then sent to the relevant parties via an SMTP server.

[0323] When a user checks a report, an emotion recognition engine analyzes the user's facial expressions and voice. For example, Google® Teachable Machine or Amazon Rekognition can be used as the emotion engine. The device acquires data from the webcam and microphone, and if it determines that the user is anxious, the server provides more detailed information. For example, an additional message such as "Please check the detailed information related to timber decline in area X" is displayed.

[0324] Users can view reports through a web browser on their laptop and take immediate action through the provided user interface. The system also has a feedback function, allowing users to leave comments and request additional investigations. This feedback is sent to the server and used to improve the system.

[0325] Examples of prompts include:

[0326] "Write a program that generates an email alert message to notify administrators when a sudden decline in timber is detected in a forest."

[0327] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0328] Step 1:

[0329] Monitoring Data Collection

[0330] The server obtains environmental data from multiple sensors via the network. Specifically, the server sends an HTTP GET request to a list of sensor IP addresses every five minutes and receives data from each sensor. The input is the response from the sensor, and the output is environmental data in JSON format. This data includes temperature, humidity, CO2 concentration, and wood movement.

[0331] Specific behavior:

[0332] The server uses the "requests" library to send HTTP requests to the sensors.

[0333] For example, the response might contain JSON data like "{temp: 30, humidity: 60, CO2: 400, wood_movement: 10}".

[0334] Parse the received data and insert it into a MySQL database.

[0335] Step 2:

[0336] Data preprocessing and analysis

[0337] The server first performs preprocessing to analyze the collected environmental data. The input for preprocessing is the environmental data obtained in step 1, and the output is data formatted in an analyzable format. Next, anomaly detection is performed using a pattern recognition algorithm. The input is the formatted data, and the output is a list of anomalous data.

[0338] Specific behavior:

[0339] The server uses the "pandas" library to read the data and filter outliers.

[0340] For example, a z-score is calculated for each data point and data exceeding 3 sigma are removed as anomalies.

[0341] Next, we use scikit-learn's "Isolation Forest" algorithm to detect anomalies.

[0342] Step 3:

[0343] Anomaly detection and alerting

[0344] The server detects anomalies from the analysis results and adds them to an anomaly list.Then, the alert generation means generates an appropriate alert message and notifies the relevant parties.The input is a list of abnormal data, and the output is the generated alert message.

[0345] Specific behavior:

[0346] Add the region or data point where the anomaly was detected to the anomaly list.

[0347] Automatically generate alert messages and send them to a mail server using the Spring Framework.

[0348] For example, a message such as "Sudden timber decline detected in region X" can be sent using Azure SendGrid.

[0349] Step 4:

[0350] Report creation and delivery

[0351] The server creates a detailed report based on the detected anomalies and generated alerts. The input of the report is the anomaly data and the alert message, and the output is a PDF report, which is then sent to the relevant parties.

[0352] Specific behavior:

[0353] "LaTeX" is used to generate reports based on abnormal data and alert messages.

[0354] For example, the PDF file may contain the date and time of detection, the location of the anomaly, the details of the anomaly, and recommended countermeasures.

[0355] The generated report is sent to the administrator's email address via the "SMTP server".

[0356] Step 5:

[0357] User emotion recognition and display adjustment

[0358] The device uses an emotion engine to recognize the user's emotions and adjust the display content accordingly. The input is the user's facial expressions and voice data, and the output is the optimal screen display according to the user's emotions.

[0359] Specific behavior:

[0360] A webcam and microphone are used to capture the user's facial and voice data.

[0361] Use "Google Teachable Machine" or "Amazon Rekognition" to perform sentiment analysis.

[0362] For example, if the user is concerned, display additional information such as "Check out more information related to timber decline in region X."

[0363] Step 6:

[0364] View reports and gather user feedback

[0365] The terminal displays the report sent from the server to the user and collects feedback from the user. The input is the PDF report and the user's comments, and the output is the feedback data to the server.

[0366] Specific behavior:

[0367] The terminal displays reports using front-end technologies such as "React.js".

[0368] Feedback is collected by users entering comments and pressing the "Submit" button.

[0369] The feedback data is sent to the server via a "POST request" and stored in a database.

[0370] (Application example 2)

[0371] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0372] While there is a need for early detection of environmental problems and swift countermeasures, current systems tend to be slow in collecting and analyzing data, making it difficult to provide timely alerts and countermeasures.Furthermore, there is a lack of functionality to adjust the information provided based on the user's emotions, which means that users' anxiety and tension cannot be alleviated.

[0373] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a reading means for collecting monitoring data, an analysis means for analyzing the collected data to detect abnormalities, an alert generation means for generating an alert when an abnormality is detected, a reporting means for creating a report based on the generated alert, a display means for displaying the created report to the user, and an emotion recognition means for recognizing the user's emotions and dynamically adjusting the notification content. This enables early detection and rapid response to environmental problems, and also allows appropriate information and alert notifications to be provided based on the user's emotions, thereby improving the user experience.

[0374] "Monitoring data" is a general term for various sensor data collected to detect changes and abnormalities in environmental conditions.

[0375] A "reader" is a device or system that collects monitoring data from a sensor.

[0376] "Analysis means" refers to algorithms and programs that process and analyze collected monitoring data and detect abnormalities.

[0377] An "alert generator" is a system or function that generates a warning message or notification based on an anomaly detected by the analysis means.

[0378] "Reporting tools" are systems or functions that create detailed reports based on generated alerts.

[0379] The "display means" refers to a device or interface for visually presenting the created report to the user.

[0380] An "emotion recognition means" is a system or algorithm that recognizes emotions from a user's facial expressions, voice, actions, etc., and analyzes that information.

[0381] A "sensor" is a measuring device that measures environmental data such as temperature, humidity, and CO2 concentration and provides that information.

[0382] An "anomaly detection algorithm" is a calculation method or program for detecting abnormal conditions based on monitoring data.

[0383] "Environmental data" refers to measurements of temperature, humidity, CO2 concentration, etc. related to the natural and artificial environments.

[0384] A "user interface" is the means by which a user interacts with a system or device.

[0385] "Notification content" is the details of the message or alert, including information about the abnormality or warning.

[0386] "Dynamic adjustment" means changing the content in real time according to the situation and the user's state.

[0387] This invention is a system that collects environmental data in real time, detects abnormalities, generates alerts, and dynamically adjusts notification content based on the user's emotions.

[0388] System configuration

[0389] Hardware Configuration

[0390] The system uses the following hardware:

[0391] 1. Sensor

[0392] Temperature sensors, humidity sensors, CO2 concentration sensors, wood movement sensors, etc.

[0393] These sensors are connected to a server via a network.

[0394] 2. Server

[0395] Reads data, analyzes it, generates alerts, and creates reports.

[0396] Store the data in a database and run analysis algorithms.

[0397] 3. User Device

[0398] Smart glasses (e.g., regular smart glasses), laptops, etc.

[0399] It allows users to receive emotion recognition, view reports, and provide feedback.

[0400] Software Configuration

[0401] The system uses the following software:

[0402] 1. Data Collection Module

[0403] Data is collected periodically from each sensor and sent to a server.

[0404] 2. Data Analysis Module

[0405] Run preprocessing and anomaly detection algorithms.

[0406] We use data preprocessing with Scikit-learn and anomaly detection models with Keras and TENSORFLOW (registered trademark).

[0407] 3. Emotion Recognition Module

[0408] The system analyzes the user's facial expressions captured by smart glasses and recognizes their emotions.

[0409] Extract facial features and infer emotions using OpenCV and dlib.

[0410] 4. Alert Generation Module

[0411] When an anomaly is detected, an appropriate alert message is generated.

[0412] 5. Report Generation Module

[0413] Create detailed reports based on generated alerts.

[0414] 6. Notification Module

[0415] The notification content is dynamically adjusted based on the user's emotions and sent to the user terminal.

[0416] System Operation

[0417] 1. Data Collection

[0418] Sensors periodically read environmental data (e.g., temperature, humidity, CO2 concentration, wood movement).

[0419] The data collection module sends these data to the server.

[0420] 2. Data Analysis

[0421] The server preprocesses the collected data and applies anomaly detection algorithms.

[0422] For example, detecting a sudden decrease in timber volume in a particular area.

[0423] 3. Emotion recognition

[0424] The smart glasses capture images of the user's face, which are then analyzed by an emotion recognition module.

[0425] Determine whether the user is feeling anxious.

[0426] 4. Alert Generation and Notification

[0427] Based on the detected anomalies, an alert generation module generates a warning message.

[0428] The notification module adjusts the notification content according to the user's emotions and displays it on the user terminal.

[0429] Specific examples

[0430] For example, if a sensor in a forest detects a sudden decrease in the amount of timber in a set area, the system operates as follows:

[0431] 1. The server collects data and applies an anomaly detection algorithm to detect sudden declines in timber volume.

[0432] 2. The alert generation module generates a warning message such as "Sudden timber decline detected in region X."

[0433] 3. The user views the report through the smart glasses, and the emotion recognition module detects the user's anxiety.

[0434] 4. The notification module provides additional information to allay concerns, displaying a message such as "Check out more information related to timber decline in region X."

[0435] Prompt Sentence Examples

[0436] We train a model to detect anomalies under each of the following conditions: a sudden rise in temperature, a sudden increase in CO2 concentration, and abnormally low humidity. We also provide the model with facial expression data showing when the user is feeling happy, uncomfortable, or anxious.

[0437] This enables the system to detect environmental problems in real time, respond quickly, and provide information based on user emotions.

[0438] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0439] Step 1:

[0440] The server collects environmental data in real time from multiple sensors via the network. Specifically, it acquires data every five minutes from temperature sensors, humidity sensors, CO2 concentration sensors, and wood movement sensors and stores it in a database. The input is data from each sensor, and the output is environmental data stored in the server's database.

[0441] Step 2:

[0442] The server preprocesses the collected environmental data, filtering out outliers and imputing incomplete data as appropriate. The input is the environmental data stored in the database, and the output is the preprocessed clean data. Specific operations include data cleaning techniques such as mean value imputation and outlier removal.

[0443] Step 3:

[0444] The server analyzes the preprocessed data and applies an anomaly detection algorithm. It uses an anomaly detection model (trained with Keras / TensorFlow) to recognize patterns in the data and detect anomalies. The input is the preprocessed clean data, and the output is a list of detected anomalies. Specifically, it detects abnormal wood movement and abnormal CO2 concentrations.

[0445] Step 4:

[0446] The server generates alerts based on the detected anomalies. The alert generation module creates an alert message depending on the type and severity of the anomaly and notifies the administrator. The input is a list of anomalies, and the output is the generated alert message. Specific operations include generating a warning message such as "Sudden timber movement detected in region X."

[0447] Step 5:

[0448] The server creates a detailed report based on the generated alert. The reporting means generates a report including the detection date and time of the anomaly, its location, type, impact, and countermeasures, and sends it to the user. The input is the generated alert message, and the output is a detailed report. Specifically, the report is generated in HTML or PDF format and sent to the user's email.

[0449] Step 6:

[0450] The device recognizes the user's emotions using an emotion recognition module. It detects emotions by analyzing the user's facial expressions, voice, typing speed, etc. The input is the user's facial expression data and voice data, and the output is the recognized emotion. Specifically, the smart glasses capture facial expressions and estimate the emotion using OpenCV and dlib.

[0451] Step 7:

[0452] The server dynamically adjusts the notification content based on the recognized emotion. If the emotion recognition module detects an emotion such as anxiety, it provides more information or an additional alert message. The input is the recognized emotion, and the output is the adjusted notification message. Specific behaviors include adding a supplemental message such as "Check for more information."

[0453] Step 8:

[0454] The terminal displays the report sent from the server to the user. Through the user interface, the user can check the report and take necessary measures. The input is the report sent from the server, and the output is the user's viewing information and feedback. Specifically, the terminal opens the report in a browser, enters comments, and sends it.

[0455] By executing the above processing steps in succession, the system realizes early detection of environmental problems, prompt response, and provision of information based on the user's emotions.

[0456] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0457] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0458] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0459] [Second embodiment]

[0460] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0461] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0462] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0463] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0464] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0465] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0466] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0467] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0468] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0469] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0470] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0471] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0472] This invention relates to a system for early detection of environmental problems based on monitoring data, enabling rapid response. This system integrates the reading, analysis, anomaly detection, alert generation, report creation, and display of monitoring data.

[0473] 1. Reading monitoring data

[0474] Processing Description:

[0475] The server automatically collects environmental data from multiple sensors connected via a network. The sensors include various devices that measure temperature, humidity, CO2 concentration, and wood movement. The server reads data from each sensor at regular intervals and temporarily stores this data. This process provides real-time environmental information.

[0476] Examples:

[0477] The server collects data on temperature, humidity, and wood movement every five minutes from multiple sensors installed in the forest, and stores this data in a database on the server.

[0478] 2. Data Analysis

[0479] Processing Description:

[0480] The server analyzes the collected data in real time. First, it preprocesses the data to filter out incomplete data and outliers. Then it applies pattern recognition algorithms to detect anomalies by comparing it with past data. This analysis process is used to identify sudden changes and unusual patterns.

[0481] Examples:

[0482] The server analyzes the collected timber movement data and detects sudden declines in timber volume in specific areas, which are recorded as anomalies.

[0483] 3. Anomaly detection and alert generation

[0484] Processing Description:

[0485] The server detects anomalies based on the analysis results. Detected anomalies are added to an anomaly list. Then, an appropriate alert message is generated depending on the type and severity of the anomaly. The generated alert is set to immediately notify relevant parties.

[0486] Examples:

[0487] The server generates an alert saying "A sudden decrease in timber has been detected in region X" and notifies the administrator by email.

[0488] 4. Create a report

[0489] Processing Description:

[0490] The server generates detailed reports based on detected anomalies and generated alerts, including the time and location of the anomaly, its type, predicted impact, and recommended actions. Reports are generated periodically and provided to relevant parties.

[0491] Examples:

[0492] A detailed report of "Sudden timber decline in area X" is generated, detailing when and where the problem occurred, the specific nature of the anomaly, and the remedial action taken.

[0493] 5. View the report

[0494] Processing Description:

[0495] The terminal displays the reports sent from the server to the user. The user can easily check these reports through the provided user interface. The user can also leave comments on the reports and request additional investigations.

[0496] Examples:

[0497] The user uses a laptop computer to view the "Rapid Timber Decline Report in Region X" through a web browser and instructs on necessary measures.

[0498] System benefits

[0499] This system makes it possible to detect environmental problems such as illegal logging and pollution early, allowing for swift action. Furthermore, detailed reports are provided, making it easier for relevant parties to share information and take effective measures. This will help protect the global environment and leave a beautiful planet for future generations.

[0500] The processing flow will be explained below.

[0501] Step 1:

[0502] The server connects to multiple sensors via the network. Specifically, it establishes a connection using the IP address and port information of the sensor. Once the connection is established, it is ready to periodically acquire data from the sensor.

[0503] Step 2:

[0504] The server reads environmental data from each sensor. For example, it calls the sensor.read() method to obtain data such as temperature, humidity, CO2 concentration, and the amount of wood movement. The obtained data is temporarily stored in memory or a database.

[0505] Step 3:

[0506] The server performs preprocessing on the collected data. Specifically, it filters out incomplete data and abnormal values. It also performs processing to fill in missing parts of the data. This improves the accuracy of the analysis.

[0507] Step 4:

[0508] The server performs pattern recognition on the pre-processed data, comparing historical data with current data and applying anomaly detection algorithms to identify unusual patterns or sudden changes, for example, using moving averages or sudden change detection algorithms.

[0509] Step 5:

[0510] The server detects anomalies from the analysis results. Detected anomalies are added to an anomaly list. If multiple anomalies are detected, each anomaly is recorded in the list.

[0511] Step 6:

[0512] The server generates alerts based on the anomaly list. Depending on the type and severity of the anomaly, it creates an appropriate alert message. For example, it generates a message such as "Sudden timber decline detected in region X."

[0513] Step 7:

[0514] The server then distributes the generated alerts to relevant parties via email, SMS, and a dedicated dashboard, allowing them to respond to any anomalies immediately.

[0515] Step 8:

[0516] The server generates a detailed report based on the anomalies detected and the alerts generated, including the time, location, type, predicted impact, and recommended remedial action of the anomaly.

[0517] Step 9:

[0518] The server then sends the generated report to the user, typically via email or a web server.

[0519] Step 10:

[0520] The terminal displays the report sent from the server to the user, who can easily check the report through the provided user interface, allowing the user to take any necessary measures immediately.

[0521] Step 11:

[0522] The terminal provides an interface for receiving feedback and additional information from users. Users can leave comments on reports or request further investigation. Feedback is sent to the server and used to improve the overall system, if necessary.

[0523] Example 1

[0524] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0525] Currently, there are many systems that require early detection and rapid response to environmental problems, but the functions of data collection, analysis, anomaly detection, alert generation, report creation, and display are all dispersed, making it difficult to integrate the entire system.In addition, because the functions are not seamlessly linked, it is difficult to process data in real time or respond quickly, and there is a risk that the accuracy and immediacy of information will be lost.

[0526] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0527] In this invention, the server includes an acquisition means for collecting monitoring data, an analysis means for analyzing the collected data in real time, filtering out abnormal values ​​and incomplete data, and detecting abnormalities by applying a pattern recognition algorithm, an alert generation means for generating an alert message in response to the detected abnormality and notifying relevant parties, a reporting means for creating a detailed report based on the generated alert and describing the date and time of the abnormality detection, location, type, predicted impact, and recommended response, and a display means for providing an interface that allows a user to easily view the created report and for comments or requests for additional investigation. This makes it possible to seamlessly perform the entire process from collecting environmental monitoring data to analyzing, detecting abnormalities, generating alerts, and creating and displaying reports.

[0528] The "acquisition means" is a function that periodically acquires environmental data from multiple sensor devices via a network in order to collect monitoring data.

[0529] The "analysis means" is a function that preprocesses the collected data, filters out incomplete data and outliers, and then applies pattern recognition algorithms to detect anomalies.

[0530] The "alert generation means" is a function that generates an alert message in response to a detected abnormality and notifies the relevant parties of the message.

[0531] "Reporting tools" refers to the ability to create detailed reports based on generated alerts, detailing the time, location, type, predicted impact, and recommended response of the anomaly.

[0532] The "display means" is a function that provides an interface that allows the user to easily check the created report, and to make comments or request additional investigations.

[0533] This invention relates to a method for seamlessly integrating a series of processes through an environmental monitoring system, from automatically collecting monitoring data, analyzing it, detecting abnormalities, generating alerts, creating reports, and displaying them to users. This system includes the following major hardware and software components:

[0534] Hardware Components

[0535] 1. Sensor Devices

[0536] This is a device for measuring various environmental data, such as temperature, humidity, CO2 concentration, and the amount of wood movement.

[0537] 2. Server

[0538] It performs the primary functions of data collection, analysis, alerting, and reporting.

[0539] 3. Terminal

[0540] The user uses the terminal to view the report sent from the server and, if necessary, make comments or request additional investigations.

[0541] Software Components

[0542] 1. Data Collection Software

[0543] The server periodically retrieves environmental data from the sensor devices, using HTTP or MQTT protocols for this process.

[0544] 2. Database Management System

[0545] The collected data is stored in a database on the server for later analysis, often using an in-memory database (e.g., Redis).

[0546] 3. Data Analysis Software

[0547] The server runs programs to analyze the collected data in real time, using Python scripts and machine learning models (e.g., LSTM architecture).

[0548] 4. Alert Generation Software

[0549] Based on the detected anomaly, an alert message is generated, and this message is sent to relevant parties via email or SMS via the SMTP server.

[0550] 5. Report Generation Software

[0551] The server creates reports based on the generated alerts and saves them in HTML or PDF format, using Python libraries (e.g. Jinja2) to insert data into templates.

[0552] 6. User Interface

[0553] The terminal provides a user interface via a web browser, allowing users to easily view the generated reports and also allows users to add comments through this interface.

[0554] Specific example explanation

[0555] Specific operational examples include the following seeds:

[0556] Monitoring Data Collection

[0557] The server collects data on temperature, humidity, and wood movement every five minutes from sensors installed in the forest, and stores this data in a database on the server.

[0558] Data analysis

[0559] The server analyzes the collected timber movement data and detects sudden declines in timber volume in specific areas, which are recorded as anomalies.

[0560] Anomaly detection and alerting

[0561] The server generates an alert saying "A sudden decrease in timber has been detected in region X" and notifies the administrator by email.

[0562] Creating a report

[0563] A detailed report of "Sudden timber decline in area X" is generated, detailing when and where the problem occurred, the specific nature of the anomaly, and the remedial action taken.

[0564] Viewing Reports

[0565] The user uses a laptop computer to view the "Rapid Timber Decline Report in Region X" through a web browser and instructs on necessary measures.

[0566] Examples of prompt statements

[0567] "Please give us an overview of the system that detects sudden changes in the amount of wood movement within a forest in real time and generates an alert if an abnormality is discovered."

[0568] The above procedures and methods make it possible to realize a system that seamlessly collects, analyzes, detects abnormalities, generates alerts, and creates and displays reports on environmental monitoring data.

[0569] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0570] Step 1:

[0571] Obtaining monitoring data

[0572] Specifically, the server acquires environmental data from multiple sensors connected via a network.

[0573] Input: Real-time environmental data sent from sensors (temperature, humidity, CO2 concentration, wood movement, etc.)

[0574] Output: Temporarily saved environmental data

[0575] Specific behavior:

[0576] The sensor sends the measured data to the server using HTTP or MQTT protocol.

[0577] The data received by the server is temporarily stored in an in-memory database (e.g. Redis) to enable fast access.

[0578] Step 2:

[0579] Data Preprocessing

[0580] Specifically: The server preprocesses the collected environmental data, filtering out incomplete data and outliers, and preparing the data for analysis.

[0581] Input: Temporarily saved environmental data

[0582] Output: Filtered data for analysis

[0583] Specific behavior:

[0584] The server runs a Python script to complete missing values ​​and remove irrational data from the collected data.

[0585] The filtered data is prepared as a dataset for analysis.

[0586] Step 3:

[0587] Applying pattern recognition algorithms and detecting anomalies

[0588] What it means: The server applies pattern recognition algorithms to the pre-processed data and compares it with historical data to detect anomalies.

[0589] Input: Filtered data for analysis

[0590] Output: Detected anomalies

[0591] Specific behavior:

[0592] The server uses a machine learning model (e.g., LSTM architecture) to analyze the data for analysis in real time.

[0593] Detects abnormal patterns and sudden changes and lists them as abnormal data.

[0594] Step 4:

[0595] Alert generation and notification

[0596] Specific Description: The server generates an alert message based on the detected anomaly and notifies relevant parties.

[0597] Input: Detected anomaly data

[0598] Output: Generated alert messages and notifications

[0599] Specific behavior:

[0600] The server generates an alert message using a template based on the type and severity of the abnormality.

[0601] The generated alerts are notified to the administrator via email or SMS via the SMTP server.

[0602] Step 5:

[0603] Creating a report

[0604] Specifically: The server generates detailed reports based on generated alerts, including the time, location, type, predicted impact, and recommended actions to detect the anomaly.

[0605] Input: The generated alert message

[0606] Output: Generated report (HTML or PDF format)

[0607] Specific behavior:

[0608] The server uses a Python library (e.g., Jinja2) to insert the alert data into the template.

[0609] Generate the completed report in PDF or HTML format and save it to your file system.

[0610] Step 6:

[0611] Viewing Reports

[0612] Specific explanation: The terminal displays the report sent from the server to the user. The user checks the report through the provided user interface and makes comments or requests for further investigation as necessary.

[0613] Input: Generated report (HTML or PDF format)

[0614] Output: The report displayed in the user interface and any user comments

[0615] Specific behavior:

[0616] The device issues an HTTP request to retrieve the latest report from the server.

[0617] The terminal displays the report via a web browser and provides an interface for the user to enter comments and requests for further investigation.

[0618] (Application example 1)

[0619] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0620] Existing monitoring systems have the problem of being unable to detect anomalies in environmental data in real time and promptly notify the appropriate parties. In particular, delayed response after an anomaly is detected can potentially exacerbate environmental problems. To solve this problem, a system is needed that integrates real-time monitoring of environmental data, anomaly detection, alert generation, and prompt notification to the relevant parties.

[0621] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0622] In this invention, the server includes a reading means for collecting monitoring data, an analyzing means for analyzing the collected data to detect abnormalities, an alert generating means for generating an alert when an abnormality is detected, a reporting means for creating a report based on the created alert, a display means for displaying the created report to a user, and a notifying means for sending the alert to relevant parties using various notifying means. This makes it possible to monitor environmental data in real time and to notify relevant parties promptly and appropriately when an abnormality occurs.

[0623] "Monitoring data" refers to data such as temperature, humidity, CO2 concentration, and amount of wood movement collected from environmental sensors and various measuring devices.

[0624] A "reader" is a mechanism for acquiring data from multiple sensors over a network.

[0625] "Analysis means" is a function for preprocessing collected data and detecting anomalies using pattern recognition algorithms.

[0626] The "alert generation means" is a mechanism for generating an alert message to notify relevant parties based on a detected abnormality.

[0627] The "reporting tool" is a function that creates a detailed report based on the generated alert, describing the problem occurrence and recommended countermeasures.

[0628] The "display means" is an interface that displays the created report to the user so that the user can easily check it.

[0629] "Notification means" refers to a mechanism for sending the generated alert message to relevant parties via push notification, email notification, etc.

[0630] This invention relates to a system for early detection of environmental problems based on monitoring data, enabling prompt responses. This system includes the following elements: reading means for collecting monitoring data, analysis means for analyzing the collected data to detect abnormalities, alert generation means for generating an alert when an abnormality is detected, reporting means for creating a report based on the generated alert, display means for displaying the created report to a user, and notification means for sending the alert to relevant parties using various notification means.

[0631] 1. Reading monitoring data

[0632] The server automatically collects environmental data from multiple sensors connected via a network. The sensors include various devices that measure temperature, humidity, CO2 concentration, and wood movement. The server reads data from each sensor at regular intervals and temporarily stores this data. This process provides real-time environmental information.

[0633] 2. Data Analysis

[0634] The server analyzes the collected data in real time. First, it preprocesses the data to filter out incomplete data and outliers. Then it applies pattern recognition algorithms to detect anomalies by comparing it with past data. This analysis process is used to identify sudden changes and unusual patterns.

[0635] 3. Anomaly detection and alert generation

[0636] The server detects anomalies based on the analysis results. Detected anomalies are added to an anomaly list. Then, an appropriate alert message is generated depending on the type and severity of the anomaly. The generated alert is set to immediately notify relevant parties.

[0637] 4. Create a report

[0638] The server generates detailed reports based on detected anomalies and generated alerts, including the time and location of the anomaly, its type, predicted impact, and recommended actions. Reports are generated periodically and provided to relevant parties.

[0639] 5. View the report

[0640] The terminal displays the reports sent from the server to the user. The user can easily check these reports through the provided user interface. The user can also leave comments on the reports and request additional investigations.

[0641] 6. Means of notification

[0642] The server is equipped with a notification means for sending the generated alert message to relevant parties. This notification is performed using push notifications, email notifications, etc. This allows relevant parties to respond quickly when an abnormality occurs.

[0643] The program of this system is implemented in the Python language and uses the following major libraries:

[0644] Requests library: Acquiring data from sensors

[0645] JSON Library: Data Analysis and Report Generation

[0646] SMTP Library: Email Notification

[0647] For example, the invention can be specifically implemented by inputting the following prompt sentences into a generative AI model:

[0648] text

[0649] "Design a program to analyze the data obtained from the sensors in real time, generate an alert and send a notification when the number of timber movements exceeds 100. Also create a detailed report of the generated alerts."

[0650] This invention makes it possible to monitor environmental data in real time and notify relevant parties promptly and appropriately if an abnormality occurs, thereby realizing early detection of environmental problems and rapid response.

[0651] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0652] Step 1:

[0653] The server acquires environmental data from various sensors via the network. The input is data sent from the sensors, and the output is environmental data stored in a temporary database on the server. Specifically, the server periodically sends API requests, receives JSON-formatted data from the sensors, parses it, and stores it in the database.

[0654] Step 2:

[0655] The server preprocesses the acquired data. The input is raw data acquired from the sensor, and the output is preprocessed data. Specifically, it filters out incomplete data and outliers, extracting only the necessary data. During this process, missing data is filled in and outliers are removed.

[0656] Step 3:

[0657] The server applies pattern recognition algorithms to the preprocessed data to detect anomalies. The input is the preprocessed data, and the output is the result of whether an anomaly was detected. Specifically, it analyzes patterns by comparing them with past data and using statistical models to determine whether an anomaly exists.

[0658] Step 4:

[0659] The server generates an alert when an anomaly is detected. The input is the anomaly detection result, and the output is an alert message. Specifically, it generates an alert message according to the type and severity of the anomaly, and adds that message to the anomaly list.

[0660] Step 5:

[0661] The server creates a detailed report based on the generated alert. The input is the alert message, and the output is a detailed report. Specifically, the server automatically generates a report that summarizes information such as the date and time the anomaly was detected, its location, the type of anomaly, its predicted impact, and recommended actions.

[0662] Step 6:

[0663] The server displays the generated report to the user. The input is a detailed report and the output is a report displayed through a user interface. Specifically, the server provides the report to the user through a web browser or a mobile application, allowing the user to view it.

[0664] Step 7:

[0665] The server generates an alert message and sends it to the relevant parties. The input is the alert message, and the output is the notification sent to the relevant parties. Specifically, the alert content is sent to the relevant parties using push notification, email notification, or SMS notification, allowing the relevant parties to respond to the anomaly quickly.

[0666] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0667] This invention relates to a system that uses monitoring data to detect environmental problems early and enable rapid response. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system dynamically adjusts display content and notifications based on the user's reactions.

[0668] 1. Reading monitoring data

[0669] Processing Description:

[0670] The server reads environmental data in real time from multiple sensors connected via a network, including temperature, humidity, CO2 concentration, and the amount of wood movement, and periodically acquires and stores data from each sensor.

[0671] Examples:

[0672] The server collects data every five minutes from temperature, humidity, CO2, and wood movement sensors installed in the forest, and stores the data in a database on the server for subsequent analysis.

[0673] 2. Data Analysis

[0674] Processing Description:

[0675] The server analyzes the collected environmental data. First, it preprocesses the data, filters out outliers, and fills incomplete data. Then, it uses pattern recognition algorithms to analyze the data and detect anomalies.

[0676] Examples:

[0677] The server preprocesses the acquired timber movement data and applies anomaly detection algorithms to detect sudden declines in timber volume in specific areas, thereby identifying areas with a high probability of illegal logging.

[0678] 3. Anomaly detection and alert generation

[0679] Processing Description:

[0680] The server detects anomalies from the analysis results and adds them to an anomaly list.Then, the alert generation means generates an appropriate alert message depending on the type and severity of the anomaly and notifies the relevant parties.

[0681] Examples:

[0682] After detecting a sudden decrease in timber in a specific area, the server creates an alert message such as "A sudden decrease in timber has been detected in area X" and notifies the administrator by email.

[0683] 4. Create a report

[0684] Processing Description:

[0685] The server generates a detailed report based on the detected anomalies and generated alerts, including the time and location of the anomaly, its type, predicted impact, and recommended remedial action, and sends the report to the relevant parties.

[0686] Examples:

[0687] A detailed report of the sudden timber loss is prepared and sent to the manager, including the date and time of detection, the location, specific details of the abnormality, and countermeasures.

[0688] 5. User Emotion Recognition and Response Adjustment

[0689] Processing Description:

[0690] The device uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, voice, typing speed, etc. to detect emotions such as pleasure, displeasure, and impatience. Based on this information, the device dynamically adjusts display content and notifications.

[0691] Examples:

[0692] When a user views a report, if the emotion engine detects that the user is anxious by analyzing their facial expressions, the server will provide more detailed information and an additional alert message, such as "Check out more information related to timber decline in region X."

[0693] 6. Report viewing and feedback

[0694] Processing Description:

[0695] The terminal displays the report sent from the server to the user. The user can easily check the report and take necessary measures through the provided user interface. The user can also leave comments on the report or request additional investigation. The user's feedback is sent to the server and used to improve the system.

[0696] Examples:

[0697] The user can view the report through a web browser on a laptop computer, issue instructions to immediately dispatch an investigation team to the anomalous area, add comments to the report, and send requests for future investigations to the server.

[0698] System benefits

[0699] This system makes it possible to detect environmental problems such as illegal logging and pollution early and respond quickly. Furthermore, by recognizing the user's emotions, it improves the user experience and supports more effective information provision and problem solving. As a result, it becomes easier to take concrete measures to protect the global environment and leave a beautiful planet for future generations.

[0700] The processing flow will be explained below.

[0701] Step 1:

[0702] The server connects to multiple sensors via the network. Specifically, it establishes a connection using the IP address and port number of the sensor. Once the connection is established, it is ready to periodically acquire data.

[0703] Step 2:

[0704] The server obtains environmental data from each sensor. For example, it calls the sensor.read() method to obtain data such as temperature, humidity, CO2 concentration, and the amount of wood movement. This data is collected in real time and stored in a database on the server.

[0705] Step 3:

[0706] The server performs preprocessing on the collected data, specifically filling in missing values, filtering out noise and outliers, and converting the data to a uniform format.

[0707] Step 4:

[0708] The server performs pattern recognition on the pre-processed data, comparing it with historical data and applying anomaly detection algorithms to detect unusual patterns or sudden changes, such as a sudden decrease in timber volume over a short period of time.

[0709] Step 5:

[0710] The server detects anomalies from the analysis results. Detected anomalies are added to an anomaly list. If multiple anomalies are detected, each anomaly is recorded in the list.

[0711] Step 6:

[0712] The server generates alerts based on the anomaly list. It creates appropriate alert messages depending on the type and severity of the anomaly. For example, it generates a message such as "Sudden timber decline detected in region X."

[0713] Step 7:

[0714] The server then distributes the generated alerts to relevant parties, specifically by sending notifications via email, SMS, and a dedicated dashboard, allowing relevant parties to respond to anomalies immediately.

[0715] Step 8:

[0716] The server generates a detailed report based on the detected anomalies and generated alerts, including when the anomaly was detected, its location, the type of anomaly, its predicted impact, and recommended remedial actions.

[0717] Step 9:

[0718] The server then sends the generated report to the user, who can then receive and review it, typically via email or a web server.

[0719] Step 10:

[0720] The terminal displays the report sent from the server to the user, who can easily check the report through the provided user interface, allowing the user to take any necessary measures immediately.

[0721] Step 11:

[0722] The device uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, voice, typing speed, etc. to detect the user's emotions. Based on the results, the device provides appropriate display content or additional input to respond to the user's reaction.

[0723] Step 12:

[0724] The device dynamically adjusts the display content based on the user's emotions detected by the emotion engine. For example, if the user is feeling anxious, the display content may include additional support information or encouraging messages.

[0725] Step 13:

[0726] The terminal provides an interface for receiving feedback and additional information from the user, who can leave comments on the report or request further investigation.

[0727] Step 14:

[0728] The server receives feedback from the devices and uses it to improve the overall system, adjusting system settings and alert generation rules accordingly.

[0729] Example 2

[0730] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0731] In modern society, early detection and rapid response to environmental problems are extremely important. However, existing monitoring systems often take time to analyze collected data and detect anomalies, resulting in delayed effective responses. Furthermore, they do not provide information tailored to the user's emotions, and the user experience is not sufficiently improved. This makes it difficult to quickly and effectively resolve problems. The purpose of this invention is to solve these problems by enabling early detection of environmental problems, taking appropriate measures, and improving the user experience.

[0732] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0733] In this invention, the server includes a reading means for collecting monitoring data, an analysis means for analyzing the collected data to detect abnormalities, an alert generation means for generating an alert when an abnormality is detected, a reporting means for creating a report based on the created alert, a display means for displaying the created report to the user, and an emotion recognition means for recognizing the user's emotions and dynamically adjusting the display content. This enables rapid collection and analysis of environmental data and early detection of abnormalities, and further realizes flexible information provision according to the user's emotions, thereby improving the user experience.

[0734] "Monitoring data" refers to numerical information obtained using sensors or other devices to measure environmental conditions and fluctuations.

[0735] A "reader" is a device or method for obtaining monitoring data from a sensor over a network.

[0736] The "analysis means" refers to an algorithm or method for processing the acquired monitoring data and determining whether or not there is an abnormality.

[0737] The "alert generation means" is a device or method that generates a message to notify relevant parties when an abnormality is detected by the analysis means.

[0738] A "reporting means" is a device or method that generates detailed reports based on generated alerts.

[0739] A "display means" is a device or method for visually presenting the generated report to a user.

[0740] The "emotion recognition means" is a device or method for analyzing a user's facial expression, voice, and typing speed to identify the user's emotion.

[0741] "Sensors" are measurement devices used to collect environmental data.

[0742] "Environmental data" refers to data that indicates the state of the natural environment, such as temperature, humidity, CO2 concentration, and the amount of wood movement.

[0743] "Preprocessing" is the process of shaping data in analytical means, removing outliers, and filling in missing values.

[0744] A "pattern recognition algorithm" is a mathematical technique or algorithm for detecting specific patterns or anomalies in data.

[0745] An "anomaly detection algorithm" is a specific algorithm for identifying anomalous data points in monitored data.

[0746] The present invention relates to an environmental monitoring system, and provides a method and system for monitoring specific environmental conditions and for early detection and notification of abnormalities. The system also has the ability to recognize a user's emotions and dynamically adjust the display content. Specific embodiments for implementing the invention are described below.

[0747] First, the server accesses multiple sensors via the network and periodically acquires environmental data from them. The main hardware used includes temperature sensors, humidity sensors, CO2 sensors, and wood movement sensors. The software used to acquire the data includes libraries for executing HTTP requests and a database management system (e.g., MySQL).

[0748] Specifically, the server sends an HTTP request to the IP address of each sensor every five minutes, parses the data returned from each sensor (e.g., temperature 30°C, humidity 60%, CO2 concentration 400 ppm, wood movement volume 10 cubic meters) in JSON format, and stores it in a database.

[0749] The server then analyzes the collected environmental data using software such as Python's pandas library and scikit-learn. The data is preprocessed by filtering outliers and imputing missing data. Pattern recognition algorithms are then applied to detect anomalies.

[0750] For example, the server executes the query "SELECT FROM environment_data WHERE timestamp >= NOW() - INTERVAL 5 MINUTE" and uses the z-score method to remove outliers from the retrieved data. It then applies the scikit-learn Isolation Forest algorithm to detect sudden changes in timber movement in a specific area. This process identifies areas suspected of illegal logging.

[0751] If an anomaly is detected, the server generates an alert message and notifies the relevant parties. For this purpose, the server creates the alert message using the Java Spring Framework and sends it via an email API (e.g., Azure SendGrid) with the content, for example, "Sudden timber decline detected in region X."

[0752] The server also creates detailed reports based on anomaly and alert information. The reports are generated in PDF format using document generation tools such as LaTeX. The reports include the date and time the anomaly was detected, its location, type, predicted impact, and recommended countermeasures. The reports are then sent to the relevant parties via an SMTP server.

[0753] When a user checks a report, an emotion recognition engine analyzes the user's facial expressions and voice. For example, Google Teachable Machine or Amazon Rekognition can be used as the emotion engine. The device collects data from the webcam and microphone, and if it determines that the user is anxious, the server provides more detailed information. For example, an additional message such as "Please check for more information related to timber depletion in area X" is displayed.

[0754] Users can view reports through a web browser on their laptop and take immediate action through the provided user interface. The system also has a feedback function, allowing users to leave comments and request additional investigations. This feedback is sent to the server and used to improve the system.

[0755] Examples of prompts include:

[0756] "Write a program that generates an email alert message to notify administrators when a sudden decline in timber is detected in a forest."

[0757] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0758] Step 1:

[0759] Monitoring Data Collection

[0760] The server obtains environmental data from multiple sensors via the network. Specifically, the server sends an HTTP GET request to a list of sensor IP addresses every five minutes and receives data from each sensor. The input is the response from the sensor, and the output is environmental data in JSON format. This data includes temperature, humidity, CO2 concentration, and wood movement.

[0761] Specific behavior:

[0762] The server uses the "requests" library to send HTTP requests to the sensors.

[0763] For example, the response might contain JSON data like "{temp: 30, humidity: 60, CO2: 400, wood_movement: 10}".

[0764] Parse the received data and insert it into a MySQL database.

[0765] Step 2:

[0766] Data preprocessing and analysis

[0767] The server first performs preprocessing to analyze the collected environmental data. The input for preprocessing is the environmental data obtained in step 1, and the output is data formatted in an analyzable format. Next, anomaly detection is performed using a pattern recognition algorithm. The input is the formatted data, and the output is a list of anomalous data.

[0768] Specific behavior:

[0769] The server uses the "pandas" library to read the data and filter outliers.

[0770] For example, a z-score is calculated for each data point and data exceeding 3 sigma are removed as anomalies.

[0771] Next, we use scikit-learn's "Isolation Forest" algorithm to detect anomalies.

[0772] Step 3:

[0773] Anomaly detection and alerting

[0774] The server detects anomalies from the analysis results and adds them to an anomaly list.Then, the alert generation means generates an appropriate alert message and notifies the relevant parties.The input is a list of abnormal data, and the output is the generated alert message.

[0775] Specific behavior:

[0776] Add the region or data point where the anomaly was detected to the anomaly list.

[0777] Automatically generate alert messages and send them to a mail server using the Spring Framework.

[0778] For example, a message such as "Sudden timber decline detected in region X" can be sent using Azure SendGrid.

[0779] Step 4:

[0780] Report creation and delivery

[0781] The server creates a detailed report based on the detected anomalies and generated alerts. The input of the report is the anomaly data and the alert message, and the output is a PDF report, which is then sent to the relevant parties.

[0782] Specific behavior:

[0783] "LaTeX" is used to generate reports based on abnormal data and alert messages.

[0784] For example, the PDF file may contain the date and time of detection, the location of the anomaly, the details of the anomaly, and recommended countermeasures.

[0785] The generated report is sent to the administrator's email address via the "SMTP server".

[0786] Step 5:

[0787] User emotion recognition and display adjustment

[0788] The device uses an emotion engine to recognize the user's emotions and adjust the display content accordingly. The input is the user's facial expressions and voice data, and the output is the optimal screen display according to the user's emotions.

[0789] Specific behavior:

[0790] A webcam and microphone are used to capture the user's facial and voice data.

[0791] Use "Google Teachable Machine" or "Amazon Rekognition" to perform sentiment analysis.

[0792] For example, if the user is concerned, display additional information such as "Check out more information related to timber decline in region X."

[0793] Step 6:

[0794] View reports and gather user feedback

[0795] The terminal displays the report sent from the server to the user and collects feedback from the user. The input is the PDF report and the user's comments, and the output is the feedback data to the server.

[0796] Specific behavior:

[0797] The terminal displays reports using front-end technologies such as "React.js".

[0798] Feedback is collected by users entering comments and pressing the "Submit" button.

[0799] The feedback data is sent to the server via a "POST request" and stored in a database.

[0800] (Application example 2)

[0801] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0802] While there is a need for early detection of environmental problems and swift countermeasures, current systems tend to be slow in collecting and analyzing data, making it difficult to provide timely alerts and countermeasures.Furthermore, there is a lack of functionality to adjust the information provided based on the user's emotions, which means that users' anxiety and tension cannot be alleviated.

[0803] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a reading means for collecting monitoring data, an analysis means for analyzing the collected data to detect abnormalities, an alert generation means for generating an alert when an abnormality is detected, a reporting means for creating a report based on the generated alert, a display means for displaying the created report to the user, and an emotion recognition means for recognizing the user's emotions and dynamically adjusting the notification content. This enables early detection and rapid response to environmental problems, and also allows appropriate information and alert notifications to be provided based on the user's emotions, thereby improving the user experience.

[0804] "Monitoring data" is a general term for various sensor data collected to detect changes and abnormalities in environmental conditions.

[0805] A "reader" is a device or system that collects monitoring data from a sensor.

[0806] "Analysis means" refers to algorithms and programs that process and analyze collected monitoring data and detect abnormalities.

[0807] An "alert generator" is a system or function that generates a warning message or notification based on an anomaly detected by the analysis means.

[0808] "Reporting tools" are systems or functions that create detailed reports based on generated alerts.

[0809] The "display means" refers to a device or interface for visually presenting the created report to the user.

[0810] An "emotion recognition means" is a system or algorithm that recognizes emotions from a user's facial expressions, voice, actions, etc., and analyzes that information.

[0811] A "sensor" is a measuring device that measures environmental data such as temperature, humidity, and CO2 concentration and provides that information.

[0812] An "anomaly detection algorithm" is a calculation method or program for detecting abnormal conditions based on monitoring data.

[0813] "Environmental data" refers to measurements of temperature, humidity, CO2 concentration, etc. related to the natural and artificial environments.

[0814] A "user interface" is the means by which a user interacts with a system or device.

[0815] "Notification content" is the details of the message or alert, including information about the abnormality or warning.

[0816] "Dynamic adjustment" means changing the content in real time according to the situation and the user's state.

[0817] This invention is a system that collects environmental data in real time, detects abnormalities, generates alerts, and dynamically adjusts notification content based on the user's emotions.

[0818] System configuration

[0819] Hardware Configuration

[0820] The system uses the following hardware:

[0821] 1. Sensor

[0822] Temperature sensors, humidity sensors, CO2 concentration sensors, wood movement sensors, etc.

[0823] These sensors are connected to a server via a network.

[0824] 2. Server

[0825] Reads data, analyzes it, generates alerts, and creates reports.

[0826] Store the data in a database and run analysis algorithms.

[0827] 3. User Device

[0828] Smart glasses (e.g., regular smart glasses), laptops, etc.

[0829] It allows users to receive emotion recognition, view reports, and provide feedback.

[0830] Software Configuration

[0831] The system uses the following software:

[0832] 1. Data Collection Module

[0833] Data is collected periodically from each sensor and sent to a server.

[0834] 2. Data Analysis Module

[0835] Run preprocessing and anomaly detection algorithms.

[0836] We use Scikit-learn for data preprocessing and Keras and TensorFlow for anomaly detection models.

[0837] 3. Emotion Recognition Module

[0838] The system analyzes the user's facial expressions captured by smart glasses and recognizes their emotions.

[0839] Extract facial features and infer emotions using OpenCV and dlib.

[0840] 4. Alert Generation Module

[0841] When an anomaly is detected, an appropriate alert message is generated.

[0842] 5. Report Generation Module

[0843] Create detailed reports based on generated alerts.

[0844] 6. Notification Module

[0845] The notification content is dynamically adjusted based on the user's emotions and sent to the user terminal.

[0846] System Operation

[0847] 1. Data Collection

[0848] Sensors periodically read environmental data (e.g., temperature, humidity, CO2 concentration, wood movement).

[0849] The data collection module sends these data to the server.

[0850] 2. Data Analysis

[0851] The server preprocesses the collected data and applies anomaly detection algorithms.

[0852] For example, detecting a sudden decrease in timber volume in a particular area.

[0853] 3. Emotion recognition

[0854] The smart glasses capture images of the user's face, which are then analyzed by an emotion recognition module.

[0855] Determine whether the user is feeling anxious.

[0856] 4. Alert Generation and Notification

[0857] Based on the detected anomalies, an alert generation module generates a warning message.

[0858] The notification module adjusts the notification content according to the user's emotions and displays it on the user terminal.

[0859] Specific examples

[0860] For example, if a sensor in a forest detects a sudden decrease in the amount of timber in a set area, the system operates as follows:

[0861] 1. The server collects data and applies an anomaly detection algorithm to detect sudden declines in timber volume.

[0862] 2. The alert generation module generates a warning message such as "Sudden timber decline detected in region X."

[0863] 3. The user views the report through the smart glasses, and the emotion recognition module detects the user's anxiety.

[0864] 4. The notification module provides additional information to allay concerns, displaying a message such as "Check out more information related to timber decline in region X."

[0865] Prompt Sentence Examples

[0866] We train a model to detect anomalies under each of the following conditions: a sudden rise in temperature, a sudden increase in CO2 concentration, and abnormally low humidity. We also provide the model with facial expression data showing when the user is feeling happy, uncomfortable, or anxious.

[0867] This enables the system to detect environmental problems in real time, respond quickly, and provide information based on user emotions.

[0868] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0869] Step 1:

[0870] The server collects environmental data in real time from multiple sensors via the network. Specifically, it acquires data every five minutes from temperature sensors, humidity sensors, CO2 concentration sensors, and wood movement sensors and stores it in a database. The input is data from each sensor, and the output is environmental data stored in the server's database.

[0871] Step 2:

[0872] The server preprocesses the collected environmental data, filtering out outliers and imputing incomplete data as appropriate. The input is the environmental data stored in the database, and the output is the preprocessed clean data. Specific operations include data cleaning techniques such as mean value imputation and outlier removal.

[0873] Step 3:

[0874] The server analyzes the preprocessed data and applies an anomaly detection algorithm. It uses an anomaly detection model (trained with Keras / TensorFlow) to recognize patterns in the data and detect anomalies. The input is the preprocessed clean data, and the output is a list of detected anomalies. Specifically, it detects abnormal wood movement and abnormal CO2 concentrations.

[0875] Step 4:

[0876] The server generates alerts based on the detected anomalies. The alert generation module creates an alert message depending on the type and severity of the anomaly and notifies the administrator. The input is a list of anomalies, and the output is the generated alert message. Specific operations include generating a warning message such as "Sudden timber movement detected in region X."

[0877] Step 5:

[0878] The server creates a detailed report based on the generated alert. The reporting means generates a report including the detection date and time of the anomaly, its location, type, impact, and countermeasures, and sends it to the user. The input is the generated alert message, and the output is a detailed report. Specifically, the report is generated in HTML or PDF format and sent to the user's email.

[0879] Step 6:

[0880] The device recognizes the user's emotions using an emotion recognition module. It detects emotions by analyzing the user's facial expressions, voice, typing speed, etc. The input is the user's facial expression data and voice data, and the output is the recognized emotion. Specifically, the smart glasses capture facial expressions and estimate the emotion using OpenCV and dlib.

[0881] Step 7:

[0882] The server dynamically adjusts the notification content based on the recognized emotion. If the emotion recognition module detects an emotion such as anxiety, it provides more information or an additional alert message. The input is the recognized emotion, and the output is the adjusted notification message. Specific behaviors include adding a supplemental message such as "Check for more information."

[0883] Step 8:

[0884] The terminal displays the report sent from the server to the user. Through the user interface, the user can check the report and take necessary measures. The input is the report sent from the server, and the output is the user's viewing information and feedback. Specifically, the terminal opens the report in a browser, enters comments, and sends it.

[0885] By executing the above processing steps in succession, the system realizes early detection of environmental problems, prompt response, and provision of information based on the user's emotions.

[0886] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0887] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0888] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0889] [Third embodiment]

[0890] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0891] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0892] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0893] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0894] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0895] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0896] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0897] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0898] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0899] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0900] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0901] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0902] This invention relates to a system for early detection of environmental problems based on monitoring data, enabling rapid response. This system integrates the reading, analysis, anomaly detection, alert generation, report creation, and display of monitoring data.

[0903] 1. Reading monitoring data

[0904] Processing Description:

[0905] The server automatically collects environmental data from multiple sensors connected via a network. The sensors include various devices that measure temperature, humidity, CO2 concentration, and wood movement. The server reads data from each sensor at regular intervals and temporarily stores this data. This process provides real-time environmental information.

[0906] Examples:

[0907] The server collects data on temperature, humidity, and wood movement every five minutes from multiple sensors installed in the forest, and stores this data in a database on the server.

[0908] 2. Data Analysis

[0909] Processing Description:

[0910] The server analyzes the collected data in real time. First, it preprocesses the data to filter out incomplete data and outliers. Then it applies pattern recognition algorithms to detect anomalies by comparing it with past data. This analysis process is used to identify sudden changes and unusual patterns.

[0911] Examples:

[0912] The server analyzes the collected timber movement data and detects sudden declines in timber volume in specific areas, which are recorded as anomalies.

[0913] 3. Anomaly detection and alert generation

[0914] Processing Description:

[0915] The server detects anomalies based on the analysis results. Detected anomalies are added to an anomaly list. Then, an appropriate alert message is generated depending on the type and severity of the anomaly. The generated alert is set to immediately notify relevant parties.

[0916] Examples:

[0917] The server generates an alert saying "A sudden decrease in timber has been detected in region X" and notifies the administrator by email.

[0918] 4. Create a report

[0919] Processing Description:

[0920] The server generates detailed reports based on detected anomalies and generated alerts, including the time and location of the anomaly, its type, predicted impact, and recommended actions. Reports are generated periodically and provided to relevant parties.

[0921] Examples:

[0922] A detailed report of "Sudden timber decline in area X" is generated, detailing when and where the problem occurred, the specific nature of the anomaly, and the remedial action taken.

[0923] 5. View the report

[0924] Processing Description:

[0925] The terminal displays the reports sent from the server to the user. The user can easily check these reports through the provided user interface. The user can also leave comments on the reports and request additional investigations.

[0926] Examples:

[0927] The user uses a laptop computer to view the "Rapid Timber Decline Report in Region X" through a web browser and instructs on necessary measures.

[0928] System benefits

[0929] This system makes it possible to detect environmental problems such as illegal logging and pollution early, allowing for swift action. Furthermore, detailed reports are provided, making it easier for relevant parties to share information and take effective measures. This will help protect the global environment and leave a beautiful planet for future generations.

[0930] The processing flow will be explained below.

[0931] Step 1:

[0932] The server connects to multiple sensors via the network. Specifically, it establishes a connection using the IP address and port information of the sensor. Once the connection is established, it is ready to periodically acquire data from the sensor.

[0933] Step 2:

[0934] The server reads environmental data from each sensor. For example, it calls the sensor.read() method to obtain data such as temperature, humidity, CO2 concentration, and the amount of wood movement. The obtained data is temporarily stored in memory or a database.

[0935] Step 3:

[0936] The server performs preprocessing on the collected data. Specifically, it filters out incomplete data and abnormal values. It also performs processing to fill in missing parts of the data. This improves the accuracy of the analysis.

[0937] Step 4:

[0938] The server performs pattern recognition on the pre-processed data, comparing historical data with current data and applying anomaly detection algorithms to identify unusual patterns or sudden changes, for example, using moving averages or sudden change detection algorithms.

[0939] Step 5:

[0940] The server detects anomalies from the analysis results. Detected anomalies are added to an anomaly list. If multiple anomalies are detected, each anomaly is recorded in the list.

[0941] Step 6:

[0942] The server generates alerts based on the anomaly list. Depending on the type and severity of the anomaly, it creates an appropriate alert message. For example, it generates a message such as "Sudden timber decline detected in region X."

[0943] Step 7:

[0944] The server then distributes the generated alerts to relevant parties via email, SMS, and a dedicated dashboard, allowing them to respond to any anomalies immediately.

[0945] Step 8:

[0946] The server generates a detailed report based on the anomalies detected and the alerts generated, including the time, location, type, predicted impact, and recommended remedial action of the anomaly.

[0947] Step 9:

[0948] The server then sends the generated report to the user, typically via email or a web server.

[0949] Step 10:

[0950] The terminal displays the report sent from the server to the user, who can easily check the report through the provided user interface, allowing the user to take any necessary measures immediately.

[0951] Step 11:

[0952] The terminal provides an interface for receiving feedback and additional information from users. Users can leave comments on reports or request further investigation. Feedback is sent to the server and used to improve the overall system, if necessary.

[0953] Example 1

[0954] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0955] Currently, there are many systems that require early detection and rapid response to environmental problems, but the functions of data collection, analysis, anomaly detection, alert generation, report creation, and display are all dispersed, making it difficult to integrate the entire system.In addition, because the functions are not seamlessly linked, it is difficult to process data in real time or respond quickly, and there is a risk that the accuracy and immediacy of information will be lost.

[0956] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0957] In this invention, the server includes an acquisition means for collecting monitoring data, an analysis means for analyzing the collected data in real time, filtering out abnormal values ​​and incomplete data, and detecting abnormalities by applying a pattern recognition algorithm, an alert generation means for generating an alert message in response to the detected abnormality and notifying relevant parties, a reporting means for creating a detailed report based on the generated alert and describing the date and time of the abnormality detection, location, type, predicted impact, and recommended response, and a display means for providing an interface that allows a user to easily view the created report and for comments or requests for additional investigation. This makes it possible to seamlessly perform the entire process from collecting environmental monitoring data to analyzing, detecting abnormalities, generating alerts, and creating and displaying reports.

[0958] The "acquisition means" is a function that periodically acquires environmental data from multiple sensor devices via a network in order to collect monitoring data.

[0959] The "analysis means" is a function that preprocesses the collected data, filters out incomplete data and outliers, and then applies pattern recognition algorithms to detect anomalies.

[0960] The "alert generation means" is a function that generates an alert message in response to a detected abnormality and notifies the relevant parties of the message.

[0961] "Reporting tools" refers to the ability to create detailed reports based on generated alerts, detailing the time, location, type, predicted impact, and recommended response of the anomaly.

[0962] The "display means" is a function that provides an interface that allows the user to easily check the created report, and to make comments or request additional investigations.

[0963] This invention relates to a method for seamlessly integrating a series of processes through an environmental monitoring system, from automatically collecting monitoring data, analyzing it, detecting abnormalities, generating alerts, creating reports, and displaying them to users. This system includes the following major hardware and software components:

[0964] Hardware Components

[0965] 1. Sensor Devices

[0966] This is a device for measuring various environmental data, such as temperature, humidity, CO2 concentration, and the amount of wood movement.

[0967] 2. Server

[0968] It performs the primary functions of data collection, analysis, alerting, and reporting.

[0969] 3. Terminal

[0970] The user uses the terminal to view the report sent from the server and, if necessary, make comments or request additional investigations.

[0971] Software Components

[0972] 1. Data Collection Software

[0973] The server periodically retrieves environmental data from the sensor devices, using HTTP or MQTT protocols for this process.

[0974] 2. Database Management System

[0975] The collected data is stored in a database on the server for later analysis, often using an in-memory database (e.g., Redis).

[0976] 3. Data Analysis Software

[0977] The server runs programs to analyze the collected data in real time, using Python scripts and machine learning models (e.g., LSTM architecture).

[0978] 4. Alert Generation Software

[0979] Based on the detected anomaly, an alert message is generated, and this message is sent to relevant parties via email or SMS via the SMTP server.

[0980] 5. Report Generation Software

[0981] The server creates reports based on the generated alerts and saves them in HTML or PDF format, using Python libraries (e.g. Jinja2) to insert data into templates.

[0982] 6. User Interface

[0983] The terminal provides a user interface via a web browser, allowing users to easily view the generated reports and also allows users to add comments through this interface.

[0984] Specific example explanation

[0985] Specific operational examples include the following seeds:

[0986] Monitoring Data Collection

[0987] The server collects data on temperature, humidity, and wood movement every five minutes from sensors installed in the forest, and stores this data in a database on the server.

[0988] Data analysis

[0989] The server analyzes the collected timber movement data and detects sudden declines in timber volume in specific areas, which are recorded as anomalies.

[0990] Anomaly detection and alerting

[0991] The server generates an alert saying "A sudden decrease in timber has been detected in region X" and notifies the administrator by email.

[0992] Creating a report

[0993] A detailed report of "Sudden timber decline in area X" is generated, detailing when and where the problem occurred, the specific nature of the anomaly, and the remedial action taken.

[0994] Viewing Reports

[0995] The user uses a laptop computer to view the "Rapid Timber Decline Report in Region X" through a web browser and instructs on necessary measures.

[0996] Examples of prompt statements

[0997] "Please give us an overview of the system that detects sudden changes in the amount of wood movement within a forest in real time and generates an alert if an abnormality is discovered."

[0998] The above procedures and methods make it possible to realize a system that seamlessly collects, analyzes, detects abnormalities, generates alerts, and creates and displays reports on environmental monitoring data.

[0999] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1000] Step 1:

[1001] Obtaining monitoring data

[1002] Specifically, the server acquires environmental data from multiple sensors connected via a network.

[1003] Input: Real-time environmental data sent from sensors (temperature, humidity, CO2 concentration, wood movement, etc.)

[1004] Output: Temporarily saved environmental data

[1005] Specific behavior:

[1006] The sensor sends the measured data to the server using HTTP or MQTT protocol.

[1007] The data received by the server is temporarily stored in an in-memory database (e.g. Redis) to enable fast access.

[1008] Step 2:

[1009] Data Preprocessing

[1010] Specifically: The server preprocesses the collected environmental data, filtering out incomplete data and outliers, and preparing the data for analysis.

[1011] Input: Temporarily saved environmental data

[1012] Output: Filtered data for analysis

[1013] Specific behavior:

[1014] The server runs a Python script to complete missing values ​​and remove irrational data from the collected data.

[1015] The filtered data is prepared as a dataset for analysis.

[1016] Step 3:

[1017] Applying pattern recognition algorithms and detecting anomalies

[1018] What it means: The server applies pattern recognition algorithms to the pre-processed data and compares it with historical data to detect anomalies.

[1019] Input: Filtered data for analysis

[1020] Output: Detected anomalies

[1021] Specific behavior:

[1022] The server uses a machine learning model (e.g., LSTM architecture) to analyze the data for analysis in real time.

[1023] Detects abnormal patterns and sudden changes and lists them as abnormal data.

[1024] Step 4:

[1025] Alert generation and notification

[1026] Specific Description: The server generates an alert message based on the detected anomaly and notifies relevant parties.

[1027] Input: Detected anomaly data

[1028] Output: Generated alert messages and notifications

[1029] Specific behavior:

[1030] The server generates an alert message using a template based on the type and severity of the abnormality.

[1031] The generated alerts are notified to the administrator via email or SMS via the SMTP server.

[1032] Step 5:

[1033] Creating a report

[1034] Specifically: The server generates detailed reports based on generated alerts, including the time, location, type, predicted impact, and recommended actions to detect the anomaly.

[1035] Input: The generated alert message

[1036] Output: Generated report (HTML or PDF format)

[1037] Specific behavior:

[1038] The server uses a Python library (e.g., Jinja2) to insert the alert data into the template.

[1039] Generate the completed report in PDF or HTML format and save it to your file system.

[1040] Step 6:

[1041] Viewing Reports

[1042] Specific explanation: The terminal displays the report sent from the server to the user. The user checks the report through the provided user interface and makes comments or requests for further investigation as necessary.

[1043] Input: Generated report (HTML or PDF format)

[1044] Output: The report displayed in the user interface and any user comments

[1045] Specific behavior:

[1046] The device issues an HTTP request to retrieve the latest report from the server.

[1047] The terminal displays the report via a web browser and provides an interface for the user to enter comments and requests for further investigation.

[1048] (Application example 1)

[1049] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1050] Existing monitoring systems have the problem of being unable to detect anomalies in environmental data in real time and promptly notify the appropriate parties. In particular, delayed response after an anomaly is detected can potentially exacerbate environmental problems. To solve this problem, a system is needed that integrates real-time monitoring of environmental data, anomaly detection, alert generation, and prompt notification to the relevant parties.

[1051] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1052] In this invention, the server includes a reading means for collecting monitoring data, an analyzing means for analyzing the collected data to detect abnormalities, an alert generating means for generating an alert when an abnormality is detected, a reporting means for creating a report based on the created alert, a display means for displaying the created report to a user, and a notifying means for sending the alert to relevant parties using various notifying means. This makes it possible to monitor environmental data in real time and to notify relevant parties promptly and appropriately when an abnormality occurs.

[1053] "Monitoring data" refers to data such as temperature, humidity, CO2 concentration, and amount of wood movement collected from environmental sensors and various measuring devices.

[1054] A "reader" is a mechanism for acquiring data from multiple sensors over a network.

[1055] "Analysis means" is a function for preprocessing collected data and detecting anomalies using pattern recognition algorithms.

[1056] The "alert generation means" is a mechanism for generating an alert message to notify relevant parties based on a detected abnormality.

[1057] The "reporting tool" is a function that creates a detailed report based on the generated alert, describing the problem occurrence and recommended countermeasures.

[1058] The "display means" is an interface that displays the created report to the user so that the user can easily check it.

[1059] "Notification means" refers to a mechanism for sending the generated alert message to relevant parties via push notification, email notification, etc.

[1060] This invention relates to a system for early detection of environmental problems based on monitoring data, enabling prompt responses. This system includes the following elements: reading means for collecting monitoring data, analysis means for analyzing the collected data to detect abnormalities, alert generation means for generating an alert when an abnormality is detected, reporting means for creating a report based on the generated alert, display means for displaying the created report to a user, and notification means for sending the alert to relevant parties using various notification means.

[1061] 1. Reading monitoring data

[1062] The server automatically collects environmental data from multiple sensors connected via a network. The sensors include various devices that measure temperature, humidity, CO2 concentration, and wood movement. The server reads data from each sensor at regular intervals and temporarily stores this data. This process provides real-time environmental information.

[1063] 2. Data Analysis

[1064] The server analyzes the collected data in real time. First, it preprocesses the data to filter out incomplete data and outliers. Then it applies pattern recognition algorithms to detect anomalies by comparing it with past data. This analysis process is used to identify sudden changes and unusual patterns.

[1065] 3. Anomaly detection and alert generation

[1066] The server detects anomalies based on the analysis results. Detected anomalies are added to an anomaly list. Then, an appropriate alert message is generated depending on the type and severity of the anomaly. The generated alert is set to immediately notify relevant parties.

[1067] 4. Create a report

[1068] The server generates detailed reports based on detected anomalies and generated alerts, including the time and location of the anomaly, its type, predicted impact, and recommended actions. Reports are generated periodically and provided to relevant parties.

[1069] 5. View the report

[1070] The terminal displays the reports sent from the server to the user. The user can easily check these reports through the provided user interface. The user can also leave comments on the reports and request additional investigations.

[1071] 6. Means of notification

[1072] The server is equipped with a notification means for sending the generated alert message to relevant parties. This notification is performed using push notifications, email notifications, etc. This allows relevant parties to respond quickly when an abnormality occurs.

[1073] The program of this system is implemented in the Python language and uses the following major libraries:

[1074] Requests library: Acquiring data from sensors

[1075] JSON Library: Data Analysis and Report Generation

[1076] SMTP Library: Email Notification

[1077] For example, the invention can be specifically implemented by inputting the following prompt sentences into a generative AI model:

[1078] text

[1079] "Design a program to analyze the data obtained from the sensors in real time, generate an alert and send a notification when the number of timber movements exceeds 100. Also create a detailed report of the generated alerts."

[1080] This invention makes it possible to monitor environmental data in real time and notify relevant parties promptly and appropriately if an abnormality occurs, thereby realizing early detection of environmental problems and rapid response.

[1081] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1082] Step 1:

[1083] The server acquires environmental data from various sensors via the network. The input is data sent from the sensors, and the output is environmental data stored in a temporary database on the server. Specifically, the server periodically sends API requests, receives JSON-formatted data from the sensors, parses it, and stores it in the database.

[1084] Step 2:

[1085] The server preprocesses the acquired data. The input is raw data acquired from the sensor, and the output is preprocessed data. Specifically, it filters out incomplete data and outliers, extracting only the necessary data. During this process, missing data is filled in and outliers are removed.

[1086] Step 3:

[1087] The server applies pattern recognition algorithms to the preprocessed data to detect anomalies. The input is the preprocessed data, and the output is the result of whether an anomaly was detected. Specifically, it analyzes patterns by comparing them with past data and using statistical models to determine whether an anomaly exists.

[1088] Step 4:

[1089] The server generates an alert when an anomaly is detected. The input is the anomaly detection result, and the output is an alert message. Specifically, it generates an alert message according to the type and severity of the anomaly, and adds that message to the anomaly list.

[1090] Step 5:

[1091] The server creates a detailed report based on the generated alert. The input is the alert message, and the output is a detailed report. Specifically, the server automatically generates a report that summarizes information such as the date and time the anomaly was detected, its location, the type of anomaly, its predicted impact, and recommended actions.

[1092] Step 6:

[1093] The server displays the generated report to the user. The input is a detailed report and the output is a report displayed through a user interface. Specifically, the server provides the report to the user through a web browser or a mobile application, allowing the user to view it.

[1094] Step 7:

[1095] The server generates an alert message and sends it to the relevant parties. The input is the alert message, and the output is the notification sent to the relevant parties. Specifically, the alert content is sent to the relevant parties using push notification, email notification, or SMS notification, allowing the relevant parties to respond to the anomaly quickly.

[1096] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1097] This invention relates to a system that uses monitoring data to detect environmental problems early and enable rapid response. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system dynamically adjusts display content and notifications based on the user's reactions.

[1098] 1. Reading monitoring data

[1099] Processing Description:

[1100] The server reads environmental data in real time from multiple sensors connected via a network, including temperature, humidity, CO2 concentration, and the amount of wood movement, and periodically acquires and stores data from each sensor.

[1101] Examples:

[1102] The server collects data every five minutes from temperature, humidity, CO2, and wood movement sensors installed in the forest, and stores the data in a database on the server for subsequent analysis.

[1103] 2. Data Analysis

[1104] Processing Description:

[1105] The server analyzes the collected environmental data. First, it preprocesses the data, filters out outliers, and fills incomplete data. Then, it uses pattern recognition algorithms to analyze the data and detect anomalies.

[1106] Examples:

[1107] The server preprocesses the acquired timber movement data and applies anomaly detection algorithms to detect sudden declines in timber volume in specific areas, thereby identifying areas with a high probability of illegal logging.

[1108] 3. Anomaly detection and alert generation

[1109] Processing Description:

[1110] The server detects anomalies from the analysis results and adds them to an anomaly list.Then, the alert generation means generates an appropriate alert message depending on the type and severity of the anomaly and notifies the relevant parties.

[1111] Examples:

[1112] After detecting a sudden decrease in timber in a specific area, the server creates an alert message such as "A sudden decrease in timber has been detected in area X" and notifies the administrator by email.

[1113] 4. Create a report

[1114] Processing Description:

[1115] The server generates a detailed report based on the detected anomalies and generated alerts, including the time and location of the anomaly, its type, predicted impact, and recommended remedial action, and sends the report to the relevant parties.

[1116] Examples:

[1117] A detailed report of the sudden timber loss is prepared and sent to the manager, including the date and time of detection, the location, specific details of the abnormality, and countermeasures.

[1118] 5. User Emotion Recognition and Response Adjustment

[1119] Processing Description:

[1120] The device uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, voice, typing speed, etc. to detect emotions such as pleasure, displeasure, and impatience. Based on this information, the device dynamically adjusts display content and notifications.

[1121] Examples:

[1122] When a user views a report, if the emotion engine detects that the user is anxious by analyzing their facial expressions, the server will provide more detailed information and an additional alert message, such as "Check out more information related to timber decline in region X."

[1123] 6. Report viewing and feedback

[1124] Processing Description:

[1125] The terminal displays the report sent from the server to the user. The user can easily check the report and take necessary measures through the provided user interface. The user can also leave comments on the report or request additional investigation. The user's feedback is sent to the server and used to improve the system.

[1126] Examples:

[1127] The user can view the report through a web browser on a laptop computer, issue instructions to immediately dispatch an investigation team to the anomalous area, add comments to the report, and send requests for future investigations to the server.

[1128] System benefits

[1129] This system makes it possible to detect environmental problems such as illegal logging and pollution early and respond quickly. Furthermore, by recognizing the user's emotions, it improves the user experience and supports more effective information provision and problem solving. As a result, it becomes easier to take concrete measures to protect the global environment and leave a beautiful planet for future generations.

[1130] The processing flow will be explained below.

[1131] Step 1:

[1132] The server connects to multiple sensors via the network. Specifically, it establishes a connection using the IP address and port number of the sensor. Once the connection is established, it is ready to periodically acquire data.

[1133] Step 2:

[1134] The server obtains environmental data from each sensor. For example, it calls the sensor.read() method to obtain data such as temperature, humidity, CO2 concentration, and the amount of wood movement. This data is collected in real time and stored in a database on the server.

[1135] Step 3:

[1136] The server performs preprocessing on the collected data, specifically filling in missing values, filtering out noise and outliers, and converting the data to a uniform format.

[1137] Step 4:

[1138] The server performs pattern recognition on the pre-processed data, comparing it with historical data and applying anomaly detection algorithms to detect unusual patterns or sudden changes, such as a sudden decrease in timber volume over a short period of time.

[1139] Step 5:

[1140] The server detects anomalies from the analysis results. Detected anomalies are added to an anomaly list. If multiple anomalies are detected, each anomaly is recorded in the list.

[1141] Step 6:

[1142] The server generates alerts based on the anomaly list. It creates appropriate alert messages depending on the type and severity of the anomaly. For example, it generates a message such as "Sudden timber decline detected in region X."

[1143] Step 7:

[1144] The server then distributes the generated alerts to relevant parties, specifically by sending notifications via email, SMS, and a dedicated dashboard, allowing relevant parties to respond to anomalies immediately.

[1145] Step 8:

[1146] The server generates a detailed report based on the detected anomalies and generated alerts, including when the anomaly was detected, its location, the type of anomaly, its predicted impact, and recommended remedial actions.

[1147] Step 9:

[1148] The server then sends the generated report to the user, who can then receive and review it, typically via email or a web server.

[1149] Step 10:

[1150] The terminal displays the report sent from the server to the user, who can easily check the report through the provided user interface, allowing the user to take any necessary measures immediately.

[1151] Step 11:

[1152] The device uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, voice, typing speed, etc. to detect the user's emotions. Based on the results, the device provides appropriate display content or additional input to respond to the user's reaction.

[1153] Step 12:

[1154] The device dynamically adjusts the display content based on the user's emotions detected by the emotion engine. For example, if the user is feeling anxious, the display content may include additional support information or encouraging messages.

[1155] Step 13:

[1156] The terminal provides an interface for receiving feedback and additional information from the user, who can leave comments on the report or request further investigation.

[1157] Step 14:

[1158] The server receives feedback from the devices and uses it to improve the overall system, adjusting system settings and alert generation rules accordingly.

[1159] Example 2

[1160] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1161] In modern society, early detection and rapid response to environmental problems are extremely important. However, existing monitoring systems often take time to analyze collected data and detect anomalies, resulting in delayed effective responses. Furthermore, they do not provide information tailored to the user's emotions, and the user experience is not sufficiently improved. This makes it difficult to quickly and effectively resolve problems. The purpose of this invention is to solve these problems by enabling early detection of environmental problems, taking appropriate measures, and improving the user experience.

[1162] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1163] In this invention, the server includes a reading means for collecting monitoring data, an analysis means for analyzing the collected data to detect abnormalities, an alert generation means for generating an alert when an abnormality is detected, a reporting means for creating a report based on the created alert, a display means for displaying the created report to the user, and an emotion recognition means for recognizing the user's emotions and dynamically adjusting the display content. This enables rapid collection and analysis of environmental data and early detection of abnormalities, and further realizes flexible information provision according to the user's emotions, thereby improving the user experience.

[1164] "Monitoring data" refers to numerical information obtained using sensors or other devices to measure environmental conditions and fluctuations.

[1165] A "reader" is a device or method for obtaining monitoring data from a sensor over a network.

[1166] The "analysis means" refers to an algorithm or method for processing the acquired monitoring data and determining whether or not there is an abnormality.

[1167] The "alert generation means" is a device or method that generates a message to notify relevant parties when an abnormality is detected by the analysis means.

[1168] A "reporting means" is a device or method that generates detailed reports based on generated alerts.

[1169] A "display means" is a device or method for visually presenting the generated report to a user.

[1170] The "emotion recognition means" is a device or method for analyzing a user's facial expression, voice, and typing speed to identify the user's emotion.

[1171] "Sensors" are measurement devices used to collect environmental data.

[1172] "Environmental data" refers to data that indicates the state of the natural environment, such as temperature, humidity, CO2 concentration, and the amount of wood movement.

[1173] "Preprocessing" is the process of shaping data in analytical means, removing outliers, and filling in missing values.

[1174] A "pattern recognition algorithm" is a mathematical technique or algorithm for detecting specific patterns or anomalies in data.

[1175] An "anomaly detection algorithm" is a specific algorithm for identifying anomalous data points in monitored data.

[1176] The present invention relates to an environmental monitoring system, and provides a method and system for monitoring specific environmental conditions and for early detection and notification of abnormalities. The system also has the ability to recognize a user's emotions and dynamically adjust the display content. Specific embodiments for implementing the invention are described below.

[1177] First, the server accesses multiple sensors via the network and periodically acquires environmental data from them. The main hardware used includes temperature sensors, humidity sensors, CO2 sensors, and wood movement sensors. The software used to acquire the data includes libraries for executing HTTP requests and a database management system (e.g., MySQL).

[1178] Specifically, the server sends an HTTP request to the IP address of each sensor every five minutes, parses the data returned from each sensor (e.g., temperature 30°C, humidity 60%, CO2 concentration 400 ppm, wood movement volume 10 cubic meters) in JSON format, and stores it in a database.

[1179] The server then analyzes the collected environmental data using software such as Python's pandas library and scikit-learn. The data is preprocessed by filtering outliers and imputing missing data. Pattern recognition algorithms are then applied to detect anomalies.

[1180] For example, the server executes the query "SELECT FROM environment_data WHERE timestamp >= NOW() - INTERVAL 5 MINUTE" and uses the z-score method to remove outliers from the retrieved data. It then applies the scikit-learn Isolation Forest algorithm to detect sudden changes in timber movement in a specific area. This process identifies areas suspected of illegal logging.

[1181] If an anomaly is detected, the server generates an alert message and notifies the relevant parties. For this purpose, the server creates the alert message using the Java Spring Framework and sends it via an email API (e.g., Azure SendGrid) with the content, for example, "Sudden timber decline detected in region X."

[1182] The server also creates detailed reports based on anomaly and alert information. The reports are generated in PDF format using document generation tools such as LaTeX. The reports include the date and time the anomaly was detected, its location, type, predicted impact, and recommended countermeasures. The reports are then sent to the relevant parties via an SMTP server.

[1183] When a user checks a report, an emotion recognition engine analyzes the user's facial expressions and voice. For example, Google Teachable Machine or Amazon Rekognition can be used as the emotion engine. The device collects data from the webcam and microphone, and if it determines that the user is anxious, the server provides more detailed information. For example, an additional message such as "Please check for more information related to timber depletion in area X" is displayed.

[1184] Users can view reports through a web browser on their laptop and take immediate action through the provided user interface. The system also has a feedback function, allowing users to leave comments and request additional investigations. This feedback is sent to the server and used to improve the system.

[1185] Examples of prompts include:

[1186] "Write a program that generates an email alert message to notify administrators when a sudden decline in timber is detected in a forest."

[1187] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1188] Step 1:

[1189] Monitoring Data Collection

[1190] The server obtains environmental data from multiple sensors via the network. Specifically, the server sends an HTTP GET request to a list of sensor IP addresses every five minutes and receives data from each sensor. The input is the response from the sensor, and the output is environmental data in JSON format. This data includes temperature, humidity, CO2 concentration, and wood movement.

[1191] Specific behavior:

[1192] The server uses the "requests" library to send HTTP requests to the sensors.

[1193] For example, the response might contain JSON data like "{temp: 30, humidity: 60, CO2: 400, wood_movement: 10}".

[1194] Parse the received data and insert it into a MySQL database.

[1195] Step 2:

[1196] Data preprocessing and analysis

[1197] The server first performs preprocessing to analyze the collected environmental data. The input for preprocessing is the environmental data obtained in step 1, and the output is data formatted in an analyzable format. Next, anomaly detection is performed using a pattern recognition algorithm. The input is the formatted data, and the output is a list of anomalous data.

[1198] Specific behavior:

[1199] The server uses the "pandas" library to read the data and filter outliers.

[1200] For example, a z-score is calculated for each data point and data exceeding 3 sigma are removed as anomalies.

[1201] Next, we use scikit-learn's "Isolation Forest" algorithm to detect anomalies.

[1202] Step 3:

[1203] Anomaly detection and alerting

[1204] The server detects anomalies from the analysis results and adds them to an anomaly list.Then, the alert generation means generates an appropriate alert message and notifies the relevant parties.The input is a list of abnormal data, and the output is the generated alert message.

[1205] Specific behavior:

[1206] Add the region or data point where the anomaly was detected to the anomaly list.

[1207] Automatically generate alert messages and send them to a mail server using the Spring Framework.

[1208] For example, a message such as "Sudden timber decline detected in region X" can be sent using Azure SendGrid.

[1209] Step 4:

[1210] Report creation and delivery

[1211] The server creates a detailed report based on the detected anomalies and generated alerts. The input of the report is the anomaly data and the alert message, and the output is a PDF report, which is then sent to the relevant parties.

[1212] Specific behavior:

[1213] "LaTeX" is used to generate reports based on abnormal data and alert messages.

[1214] For example, the PDF file may contain the date and time of detection, the location of the anomaly, the details of the anomaly, and recommended countermeasures.

[1215] The generated report is sent to the administrator's email address via the "SMTP server".

[1216] Step 5:

[1217] User emotion recognition and display adjustment

[1218] The device uses an emotion engine to recognize the user's emotions and adjust the display content accordingly. The input is the user's facial expressions and voice data, and the output is the optimal screen display according to the user's emotions.

[1219] Specific behavior:

[1220] A webcam and microphone are used to capture the user's facial and voice data.

[1221] Use "Google Teachable Machine" or "Amazon Rekognition" to perform sentiment analysis.

[1222] For example, if the user is concerned, display additional information such as "Check out more information related to timber decline in region X."

[1223] Step 6:

[1224] View reports and gather user feedback

[1225] The terminal displays the report sent from the server to the user and collects feedback from the user. The input is the PDF report and the user's comments, and the output is the feedback data to the server.

[1226] Specific behavior:

[1227] The terminal displays reports using front-end technologies such as "React.js".

[1228] Feedback is collected by users entering comments and pressing the "Submit" button.

[1229] The feedback data is sent to the server via a "POST request" and stored in a database.

[1230] (Application example 2)

[1231] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1232] While there is a need for early detection of environmental problems and swift countermeasures, current systems tend to be slow in collecting and analyzing data, making it difficult to provide timely alerts and countermeasures.Furthermore, there is a lack of functionality to adjust the information provided based on the user's emotions, which means that users' anxiety and tension cannot be alleviated.

[1233] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a reading means for collecting monitoring data, an analysis means for analyzing the collected data to detect abnormalities, an alert generation means for generating an alert when an abnormality is detected, a reporting means for creating a report based on the generated alert, a display means for displaying the created report to the user, and an emotion recognition means for recognizing the user's emotions and dynamically adjusting the notification content. This enables early detection and rapid response to environmental problems, and also allows appropriate information and alert notifications to be provided based on the user's emotions, thereby improving the user experience.

[1234] "Monitoring data" is a general term for various sensor data collected to detect changes and abnormalities in environmental conditions.

[1235] A "reader" is a device or system that collects monitoring data from a sensor.

[1236] "Analysis means" refers to algorithms and programs that process and analyze collected monitoring data and detect abnormalities.

[1237] An "alert generator" is a system or function that generates a warning message or notification based on an anomaly detected by the analysis means.

[1238] "Reporting tools" are systems or functions that create detailed reports based on generated alerts.

[1239] The "display means" refers to a device or interface for visually presenting the created report to the user.

[1240] An "emotion recognition means" is a system or algorithm that recognizes emotions from a user's facial expressions, voice, actions, etc., and analyzes that information.

[1241] A "sensor" is a measuring device that measures environmental data such as temperature, humidity, and CO2 concentration and provides that information.

[1242] An "anomaly detection algorithm" is a calculation method or program for detecting abnormal conditions based on monitoring data.

[1243] "Environmental data" refers to measurements of temperature, humidity, CO2 concentration, etc. related to the natural and artificial environments.

[1244] A "user interface" is the means by which a user interacts with a system or device.

[1245] "Notification content" is the details of the message or alert, including information about the abnormality or warning.

[1246] "Dynamic adjustment" means changing the content in real time according to the situation and the user's state.

[1247] This invention is a system that collects environmental data in real time, detects abnormalities, generates alerts, and dynamically adjusts notification content based on the user's emotions.

[1248] System configuration

[1249] Hardware Configuration

[1250] The system uses the following hardware:

[1251] 1. Sensor

[1252] Temperature sensors, humidity sensors, CO2 concentration sensors, wood movement sensors, etc.

[1253] These sensors are connected to a server via a network.

[1254] 2. Server

[1255] Reads data, analyzes it, generates alerts, and creates reports.

[1256] Store the data in a database and run analysis algorithms.

[1257] 3. User Device

[1258] Smart glasses (e.g., regular smart glasses), laptops, etc.

[1259] It allows users to receive emotion recognition, view reports, and provide feedback.

[1260] Software Configuration

[1261] The system uses the following software:

[1262] 1. Data Collection Module

[1263] Data is collected periodically from each sensor and sent to a server.

[1264] 2. Data Analysis Module

[1265] Run preprocessing and anomaly detection algorithms.

[1266] We use Scikit-learn for data preprocessing and Keras and TensorFlow for anomaly detection models.

[1267] 3. Emotion Recognition Module

[1268] The system analyzes the user's facial expressions captured by smart glasses and recognizes their emotions.

[1269] Extract facial features and infer emotions using OpenCV and dlib.

[1270] 4. Alert Generation Module

[1271] When an anomaly is detected, an appropriate alert message is generated.

[1272] 5. Report Generation Module

[1273] Create detailed reports based on generated alerts.

[1274] 6. Notification Module

[1275] The notification content is dynamically adjusted based on the user's emotions and sent to the user terminal.

[1276] System Operation

[1277] 1. Data Collection

[1278] Sensors periodically read environmental data (e.g., temperature, humidity, CO2 concentration, wood movement).

[1279] The data collection module sends these data to the server.

[1280] 2. Data Analysis

[1281] The server preprocesses the collected data and applies anomaly detection algorithms.

[1282] For example, detecting a sudden decrease in timber volume in a particular area.

[1283] 3. Emotion recognition

[1284] The smart glasses capture images of the user's face, which are then analyzed by an emotion recognition module.

[1285] Determine whether the user is feeling anxious.

[1286] 4. Alert Generation and Notification

[1287] Based on the detected anomalies, an alert generation module generates a warning message.

[1288] The notification module adjusts the notification content according to the user's emotions and displays it on the user terminal.

[1289] Specific examples

[1290] For example, if a sensor in a forest detects a sudden decrease in the amount of timber in a set area, the system operates as follows:

[1291] 1. The server collects data and applies an anomaly detection algorithm to detect sudden declines in timber volume.

[1292] 2. The alert generation module generates a warning message such as "Sudden timber decline detected in region X."

[1293] 3. The user views the report through the smart glasses, and the emotion recognition module detects the user's anxiety.

[1294] 4. The notification module provides additional information to allay concerns, displaying a message such as "Check out more information related to timber decline in region X."

[1295] Prompt Sentence Examples

[1296] We train a model to detect anomalies under each of the following conditions: a sudden rise in temperature, a sudden increase in CO2 concentration, and abnormally low humidity. We also provide the model with facial expression data showing when the user is feeling happy, uncomfortable, or anxious.

[1297] This enables the system to detect environmental problems in real time, respond quickly, and provide information based on user emotions.

[1298] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1299] Step 1:

[1300] The server collects environmental data in real time from multiple sensors via the network. Specifically, it acquires data every five minutes from temperature sensors, humidity sensors, CO2 concentration sensors, and wood movement sensors and stores it in a database. The input is data from each sensor, and the output is environmental data stored in the server's database.

[1301] Step 2:

[1302] The server preprocesses the collected environmental data, filtering out outliers and imputing incomplete data as appropriate. The input is the environmental data stored in the database, and the output is the preprocessed clean data. Specific operations include data cleaning techniques such as mean value imputation and outlier removal.

[1303] Step 3:

[1304] The server analyzes the preprocessed data and applies an anomaly detection algorithm. It uses an anomaly detection model (trained with Keras / TensorFlow) to recognize patterns in the data and detect anomalies. The input is the preprocessed clean data, and the output is a list of detected anomalies. Specifically, it detects abnormal wood movement and abnormal CO2 concentrations.

[1305] Step 4:

[1306] The server generates alerts based on the detected anomalies. The alert generation module creates an alert message depending on the type and severity of the anomaly and notifies the administrator. The input is a list of anomalies, and the output is the generated alert message. Specific operations include generating a warning message such as "Sudden timber movement detected in region X."

[1307] Step 5:

[1308] The server creates a detailed report based on the generated alert. The reporting means generates a report including the detection date and time of the anomaly, its location, type, impact, and countermeasures, and sends it to the user. The input is the generated alert message, and the output is a detailed report. Specifically, the report is generated in HTML or PDF format and sent to the user's email.

[1309] Step 6:

[1310] The device recognizes the user's emotions using an emotion recognition module. It detects emotions by analyzing the user's facial expressions, voice, typing speed, etc. The input is the user's facial expression data and voice data, and the output is the recognized emotion. Specifically, the smart glasses capture facial expressions and estimate the emotion using OpenCV and dlib.

[1311] Step 7:

[1312] The server dynamically adjusts the notification content based on the recognized emotion. If the emotion recognition module detects an emotion such as anxiety, it provides more information or an additional alert message. The input is the recognized emotion, and the output is the adjusted notification message. Specific behaviors include adding a supplemental message such as "Check for more information."

[1313] Step 8:

[1314] The terminal displays the report sent from the server to the user. Through the user interface, the user can check the report and take necessary measures. The input is the report sent from the server, and the output is the user's viewing information and feedback. Specifically, the terminal opens the report in a browser, enters comments, and sends it.

[1315] By executing the above processing steps in succession, the system realizes early detection of environmental problems, prompt response, and provision of information based on the user's emotions.

[1316] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1317] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1318] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1319] [Fourth embodiment]

[1320] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1321] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1322] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1323] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1324] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1325] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1326] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1327] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1328] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1329] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1330] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1331] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1332] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1333] This invention relates to a system for early detection of environmental problems based on monitoring data, enabling rapid response. This system integrates the reading, analysis, anomaly detection, alert generation, report creation, and display of monitoring data.

[1334] 1. Reading monitoring data

[1335] Processing Description:

[1336] The server automatically collects environmental data from multiple sensors connected via a network. The sensors include various devices that measure temperature, humidity, CO2 concentration, and wood movement. The server reads data from each sensor at regular intervals and temporarily stores this data. This process provides real-time environmental information.

[1337] Examples:

[1338] The server collects data on temperature, humidity, and wood movement every five minutes from multiple sensors installed in the forest, and stores this data in a database on the server.

[1339] 2. Data Analysis

[1340] Processing Description:

[1341] The server analyzes the collected data in real time. First, it preprocesses the data to filter out incomplete data and outliers. Then it applies pattern recognition algorithms to detect anomalies by comparing it with past data. This analysis process is used to identify sudden changes and unusual patterns.

[1342] Examples:

[1343] The server analyzes the collected timber movement data and detects sudden declines in timber volume in specific areas, which are recorded as anomalies.

[1344] 3. Anomaly detection and alert generation

[1345] Processing Description:

[1346] The server detects anomalies based on the analysis results. Detected anomalies are added to an anomaly list. Then, an appropriate alert message is generated depending on the type and severity of the anomaly. The generated alert is set to immediately notify relevant parties.

[1347] Examples:

[1348] The server generates an alert saying "A sudden decrease in timber has been detected in region X" and notifies the administrator by email.

[1349] 4. Create a report

[1350] Processing Description:

[1351] The server generates detailed reports based on detected anomalies and generated alerts, including the time and location of the anomaly, its type, predicted impact, and recommended actions. Reports are generated periodically and provided to relevant parties.

[1352] Examples:

[1353] A detailed report of "Sudden timber decline in area X" is generated, detailing when and where the problem occurred, the specific nature of the anomaly, and the remedial action taken.

[1354] 5. View the report

[1355] Processing Description:

[1356] The terminal displays the reports sent from the server to the user. The user can easily check these reports through the provided user interface. The user can also leave comments on the reports and request additional investigations.

[1357] Examples:

[1358] The user uses a laptop computer to view the "Rapid Timber Decline Report in Region X" through a web browser and instructs on necessary measures.

[1359] System benefits

[1360] This system makes it possible to detect environmental problems such as illegal logging and pollution early, allowing for swift action. Furthermore, detailed reports are provided, making it easier for relevant parties to share information and take effective measures. This will help protect the global environment and leave a beautiful planet for future generations.

[1361] The processing flow will be explained below.

[1362] Step 1:

[1363] The server connects to multiple sensors via the network. Specifically, it establishes a connection using the IP address and port information of the sensor. Once the connection is established, it is ready to periodically acquire data from the sensor.

[1364] Step 2:

[1365] The server reads environmental data from each sensor. For example, it calls the sensor.read() method to obtain data such as temperature, humidity, CO2 concentration, and the amount of wood movement. The obtained data is temporarily stored in memory or a database.

[1366] Step 3:

[1367] The server performs preprocessing on the collected data. Specifically, it filters out incomplete data and abnormal values. It also performs processing to fill in missing parts of the data. This improves the accuracy of the analysis.

[1368] Step 4:

[1369] The server performs pattern recognition on the pre-processed data, comparing historical data with current data and applying anomaly detection algorithms to identify unusual patterns or sudden changes, for example, using moving averages or sudden change detection algorithms.

[1370] Step 5:

[1371] The server detects anomalies from the analysis results. Detected anomalies are added to an anomaly list. If multiple anomalies are detected, each anomaly is recorded in the list.

[1372] Step 6:

[1373] The server generates alerts based on the anomaly list. Depending on the type and severity of the anomaly, it creates an appropriate alert message. For example, it generates a message such as "Sudden timber decline detected in region X."

[1374] Step 7:

[1375] The server then distributes the generated alerts to relevant parties via email, SMS, and a dedicated dashboard, allowing them to respond to any anomalies immediately.

[1376] Step 8:

[1377] The server generates a detailed report based on the anomalies detected and the alerts generated, including the time, location, type, predicted impact, and recommended remedial action of the anomaly.

[1378] Step 9:

[1379] The server then sends the generated report to the user, typically via email or a web server.

[1380] Step 10:

[1381] The terminal displays the report sent from the server to the user, who can easily check the report through the provided user interface, allowing the user to take any necessary measures immediately.

[1382] Step 11:

[1383] The terminal provides an interface for receiving feedback and additional information from users. Users can leave comments on reports or request further investigation. Feedback is sent to the server and used to improve the overall system, if necessary.

[1384] Example 1

[1385] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1386] Currently, there are many systems that require early detection and rapid response to environmental problems, but the functions of data collection, analysis, anomaly detection, alert generation, report creation, and display are all dispersed, making it difficult to integrate the entire system.In addition, because the functions are not seamlessly linked, it is difficult to process data in real time or respond quickly, and there is a risk that the accuracy and immediacy of information will be lost.

[1387] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1388] In this invention, the server includes an acquisition means for collecting monitoring data, an analysis means for analyzing the collected data in real time, filtering out abnormal values ​​and incomplete data, and detecting abnormalities by applying a pattern recognition algorithm, an alert generation means for generating an alert message in response to the detected abnormality and notifying relevant parties, a reporting means for creating a detailed report based on the generated alert and describing the date and time of the abnormality detection, location, type, predicted impact, and recommended response, and a display means for providing an interface that allows a user to easily view the created report and for comments or requests for additional investigation. This makes it possible to seamlessly perform the entire process from collecting environmental monitoring data to analyzing, detecting abnormalities, generating alerts, and creating and displaying reports.

[1389] The "acquisition means" is a function that periodically acquires environmental data from multiple sensor devices via a network in order to collect monitoring data.

[1390] The "analysis means" is a function that preprocesses the collected data, filters out incomplete data and outliers, and then applies pattern recognition algorithms to detect anomalies.

[1391] The "alert generation means" is a function that generates an alert message in response to a detected abnormality and notifies the relevant parties of the message.

[1392] "Reporting tools" refers to the ability to create detailed reports based on generated alerts, detailing the time, location, type, predicted impact, and recommended response of the anomaly.

[1393] The "display means" is a function that provides an interface that allows the user to easily check the created report, and to make comments or request additional investigations.

[1394] This invention relates to a method for seamlessly integrating a series of processes through an environmental monitoring system, from automatically collecting monitoring data, analyzing it, detecting abnormalities, generating alerts, creating reports, and displaying them to users. This system includes the following major hardware and software components:

[1395] Hardware Components

[1396] 1. Sensor Devices

[1397] This is a device for measuring various environmental data, such as temperature, humidity, CO2 concentration, and the amount of wood movement.

[1398] 2. Server

[1399] It performs the primary functions of data collection, analysis, alerting, and reporting.

[1400] 3. Terminal

[1401] The user uses the terminal to view the report sent from the server and, if necessary, make comments or request additional investigations.

[1402] Software Components

[1403] 1. Data Collection Software

[1404] The server periodically retrieves environmental data from the sensor devices, using HTTP or MQTT protocols for this process.

[1405] 2. Database Management System

[1406] The collected data is stored in a database on the server for later analysis, often using an in-memory database (e.g., Redis).

[1407] 3. Data Analysis Software

[1408] The server runs programs to analyze the collected data in real time, using Python scripts and machine learning models (e.g., LSTM architecture).

[1409] 4. Alert Generation Software

[1410] Based on the detected anomaly, an alert message is generated, and this message is sent to relevant parties via email or SMS via the SMTP server.

[1411] 5. Report Generation Software

[1412] The server creates reports based on the generated alerts and saves them in HTML or PDF format, using Python libraries (e.g. Jinja2) to insert data into templates.

[1413] 6. User Interface

[1414] The terminal provides a user interface via a web browser, allowing users to easily view the generated reports and also allows users to add comments through this interface.

[1415] Specific example explanation

[1416] Specific operational examples include the following seeds:

[1417] Monitoring Data Collection

[1418] The server collects data on temperature, humidity, and wood movement every five minutes from sensors installed in the forest, and stores this data in a database on the server.

[1419] Data analysis

[1420] The server analyzes the collected timber movement data and detects sudden declines in timber volume in specific areas, which are recorded as anomalies.

[1421] Anomaly detection and alerting

[1422] The server generates an alert saying "A sudden decrease in timber has been detected in region X" and notifies the administrator by email.

[1423] Creating a report

[1424] A detailed report of "Sudden timber decline in area X" is generated, detailing when and where the problem occurred, the specific nature of the anomaly, and the remedial action taken.

[1425] Viewing Reports

[1426] The user uses a laptop computer to view the "Rapid Timber Decline Report in Region X" through a web browser and instructs on necessary measures.

[1427] Examples of prompt statements

[1428] "Please give us an overview of the system that detects sudden changes in the amount of wood movement within a forest in real time and generates an alert if an abnormality is discovered."

[1429] The above procedures and methods make it possible to realize a system that seamlessly collects, analyzes, detects abnormalities, generates alerts, and creates and displays reports on environmental monitoring data.

[1430] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1431] Step 1:

[1432] Obtaining monitoring data

[1433] Specifically, the server acquires environmental data from multiple sensors connected via a network.

[1434] Input: Real-time environmental data sent from sensors (temperature, humidity, CO2 concentration, wood movement, etc.)

[1435] Output: Temporarily saved environmental data

[1436] Specific behavior:

[1437] The sensor sends the measured data to the server using HTTP or MQTT protocol.

[1438] The data received by the server is temporarily stored in an in-memory database (e.g. Redis) to enable fast access.

[1439] Step 2:

[1440] Data Preprocessing

[1441] Specifically: The server preprocesses the collected environmental data, filtering out incomplete data and outliers, and preparing the data for analysis.

[1442] Input: Temporarily saved environmental data

[1443] Output: Filtered data for analysis

[1444] Specific behavior:

[1445] The server runs a Python script to complete missing values ​​and remove irrational data from the collected data.

[1446] The filtered data is prepared as a dataset for analysis.

[1447] Step 3:

[1448] Applying pattern recognition algorithms and detecting anomalies

[1449] What it means: The server applies pattern recognition algorithms to the pre-processed data and compares it with historical data to detect anomalies.

[1450] Input: Filtered data for analysis

[1451] Output: Detected anomalies

[1452] Specific behavior:

[1453] The server uses a machine learning model (e.g., LSTM architecture) to analyze the data for analysis in real time.

[1454] Detects abnormal patterns and sudden changes and lists them as abnormal data.

[1455] Step 4:

[1456] Alert generation and notification

[1457] Specific Description: The server generates an alert message based on the detected anomaly and notifies relevant parties.

[1458] Input: Detected anomaly data

[1459] Output: Generated alert messages and notifications

[1460] Specific behavior:

[1461] The server generates an alert message using a template based on the type and severity of the abnormality.

[1462] The generated alerts are notified to the administrator via email or SMS via the SMTP server.

[1463] Step 5:

[1464] Creating a report

[1465] Specifically: The server generates detailed reports based on generated alerts, including the time, location, type, predicted impact, and recommended actions to detect the anomaly.

[1466] Input: The generated alert message

[1467] Output: Generated report (HTML or PDF format)

[1468] Specific behavior:

[1469] The server uses a Python library (e.g., Jinja2) to insert the alert data into the template.

[1470] Generate the completed report in PDF or HTML format and save it to your file system.

[1471] Step 6:

[1472] Viewing Reports

[1473] Specific explanation: The terminal displays the report sent from the server to the user. The user checks the report through the provided user interface and makes comments or requests for further investigation as necessary.

[1474] Input: Generated report (HTML or PDF format)

[1475] Output: The report displayed in the user interface and any user comments

[1476] Specific behavior:

[1477] The device issues an HTTP request to retrieve the latest report from the server.

[1478] The terminal displays the report via a web browser and provides an interface for the user to enter comments and requests for further investigation.

[1479] (Application example 1)

[1480] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1481] Existing monitoring systems have the problem of being unable to detect anomalies in environmental data in real time and promptly notify the appropriate parties. In particular, delayed response after an anomaly is detected can potentially exacerbate environmental problems. To solve this problem, a system is needed that integrates real-time monitoring of environmental data, anomaly detection, alert generation, and prompt notification to the relevant parties.

[1482] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1483] In this invention, the server includes a reading means for collecting monitoring data, an analyzing means for analyzing the collected data to detect abnormalities, an alert generating means for generating an alert when an abnormality is detected, a reporting means for creating a report based on the created alert, a display means for displaying the created report to a user, and a notifying means for sending the alert to relevant parties using various notifying means. This makes it possible to monitor environmental data in real time and to notify relevant parties promptly and appropriately when an abnormality occurs.

[1484] "Monitoring data" refers to data such as temperature, humidity, CO2 concentration, and amount of wood movement collected from environmental sensors and various measuring devices.

[1485] A "reader" is a mechanism for acquiring data from multiple sensors over a network.

[1486] "Analysis means" is a function for preprocessing collected data and detecting anomalies using pattern recognition algorithms.

[1487] The "alert generation means" is a mechanism for generating an alert message to notify relevant parties based on a detected abnormality.

[1488] The "reporting tool" is a function that creates a detailed report based on the generated alert, describing the problem occurrence and recommended countermeasures.

[1489] The "display means" is an interface that displays the created report to the user so that the user can easily check it.

[1490] "Notification means" refers to a mechanism for sending the generated alert message to relevant parties via push notification, email notification, etc.

[1491] This invention relates to a system for early detection of environmental problems based on monitoring data, enabling prompt responses. This system includes the following elements: reading means for collecting monitoring data, analysis means for analyzing the collected data to detect abnormalities, alert generation means for generating an alert when an abnormality is detected, reporting means for creating a report based on the generated alert, display means for displaying the created report to a user, and notification means for sending the alert to relevant parties using various notification means.

[1492] 1. Reading monitoring data

[1493] The server automatically collects environmental data from multiple sensors connected via a network. The sensors include various devices that measure temperature, humidity, CO2 concentration, and wood movement. The server reads data from each sensor at regular intervals and temporarily stores this data. This process provides real-time environmental information.

[1494] 2. Data Analysis

[1495] The server analyzes the collected data in real time. First, it preprocesses the data to filter out incomplete data and outliers. Then it applies pattern recognition algorithms to detect anomalies by comparing it with past data. This analysis process is used to identify sudden changes and unusual patterns.

[1496] 3. Anomaly detection and alert generation

[1497] The server detects anomalies based on the analysis results. Detected anomalies are added to an anomaly list. Then, an appropriate alert message is generated depending on the type and severity of the anomaly. The generated alert is set to immediately notify relevant parties.

[1498] 4. Create a report

[1499] The server generates detailed reports based on detected anomalies and generated alerts, including the time and location of the anomaly, its type, predicted impact, and recommended actions. Reports are generated periodically and provided to relevant parties.

[1500] 5. View the report

[1501] The terminal displays the reports sent from the server to the user. The user can easily check these reports through the provided user interface. The user can also leave comments on the reports and request additional investigations.

[1502] 6. Means of notification

[1503] The server is equipped with a notification means for sending the generated alert message to relevant parties. This notification is performed using push notifications, email notifications, etc. This allows relevant parties to respond quickly when an abnormality occurs.

[1504] The program of this system is implemented in the Python language and uses the following major libraries:

[1505] Requests library: Acquiring data from sensors

[1506] JSON Library: Data Analysis and Report Generation

[1507] SMTP Library: Email Notification

[1508] For example, the invention can be specifically implemented by inputting the following prompt sentences into a generative AI model:

[1509] text

[1510] "Design a program to analyze the data obtained from the sensors in real time, generate an alert and send a notification when the number of timber movements exceeds 100. Also create a detailed report of the generated alerts."

[1511] This invention makes it possible to monitor environmental data in real time and notify relevant parties promptly and appropriately if an abnormality occurs, thereby realizing early detection of environmental problems and rapid response.

[1512] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1513] Step 1:

[1514] The server acquires environmental data from various sensors via the network. The input is data sent from the sensors, and the output is environmental data stored in a temporary database on the server. Specifically, the server periodically sends API requests, receives JSON-formatted data from the sensors, parses it, and stores it in the database.

[1515] Step 2:

[1516] The server preprocesses the acquired data. The input is raw data acquired from the sensor, and the output is preprocessed data. Specifically, it filters out incomplete data and outliers, extracting only the necessary data. During this process, missing data is filled in and outliers are removed.

[1517] Step 3:

[1518] The server applies pattern recognition algorithms to the preprocessed data to detect anomalies. The input is the preprocessed data, and the output is the result of whether an anomaly was detected. Specifically, it analyzes patterns by comparing them with past data and using statistical models to determine whether an anomaly exists.

[1519] Step 4:

[1520] The server generates an alert when an anomaly is detected. The input is the anomaly detection result, and the output is an alert message. Specifically, it generates an alert message according to the type and severity of the anomaly, and adds that message to the anomaly list.

[1521] Step 5:

[1522] The server creates a detailed report based on the generated alert. The input is the alert message, and the output is a detailed report. Specifically, the server automatically generates a report that summarizes information such as the date and time the anomaly was detected, its location, the type of anomaly, its predicted impact, and recommended actions.

[1523] Step 6:

[1524] The server displays the generated report to the user. The input is a detailed report and the output is a report displayed through a user interface. Specifically, the server provides the report to the user through a web browser or a mobile application, allowing the user to view it.

[1525] Step 7:

[1526] The server generates an alert message and sends it to the relevant parties. The input is the alert message, and the output is the notification sent to the relevant parties. Specifically, the alert content is sent to the relevant parties using push notification, email notification, or SMS notification, allowing the relevant parties to respond to the anomaly quickly.

[1527] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1528] This invention relates to a system that uses monitoring data to detect environmental problems early and enable rapid response. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system dynamically adjusts display content and notifications based on the user's reactions.

[1529] 1. Reading monitoring data

[1530] Processing Description:

[1531] The server reads environmental data in real time from multiple sensors connected via a network, including temperature, humidity, CO2 concentration, and the amount of wood movement, and periodically acquires and stores data from each sensor.

[1532] Examples:

[1533] The server collects data every five minutes from temperature, humidity, CO2, and wood movement sensors installed in the forest, and stores the data in a database on the server for subsequent analysis.

[1534] 2. Data Analysis

[1535] Processing Description:

[1536] The server analyzes the collected environmental data. First, it preprocesses the data, filters out outliers, and fills incomplete data. Then, it uses pattern recognition algorithms to analyze the data and detect anomalies.

[1537] Examples:

[1538] The server preprocesses the acquired timber movement data and applies anomaly detection algorithms to detect sudden declines in timber volume in specific areas, thereby identifying areas with a high probability of illegal logging.

[1539] 3. Anomaly detection and alert generation

[1540] Processing Description:

[1541] The server detects anomalies from the analysis results and adds them to an anomaly list.Then, the alert generation means generates an appropriate alert message depending on the type and severity of the anomaly and notifies the relevant parties.

[1542] Examples:

[1543] After detecting a sudden decrease in timber in a specific area, the server creates an alert message such as "A sudden decrease in timber has been detected in area X" and notifies the administrator by email.

[1544] 4. Create a report

[1545] Processing Description:

[1546] The server generates a detailed report based on the detected anomalies and generated alerts, including the time and location of the anomaly, its type, predicted impact, and recommended remedial action, and sends the report to the relevant parties.

[1547] Examples:

[1548] A detailed report of the sudden timber loss is prepared and sent to the manager, including the date and time of detection, the location, specific details of the abnormality, and countermeasures.

[1549] 5. User Emotion Recognition and Response Adjustment

[1550] Processing Description:

[1551] The device uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, voice, typing speed, etc. to detect emotions such as pleasure, displeasure, and impatience. Based on this information, the device dynamically adjusts display content and notifications.

[1552] Examples:

[1553] When a user views a report, if the emotion engine detects that the user is anxious by analyzing their facial expressions, the server will provide more detailed information and an additional alert message, such as "Check out more information related to timber decline in region X."

[1554] 6. Report viewing and feedback

[1555] Processing Description:

[1556] The terminal displays the report sent from the server to the user. The user can easily check the report and take necessary measures through the provided user interface. The user can also leave comments on the report or request additional investigation. The user's feedback is sent to the server and used to improve the system.

[1557] Examples:

[1558] The user can view the report through a web browser on a laptop computer, issue instructions to immediately dispatch an investigation team to the anomalous area, add comments to the report, and send requests for future investigations to the server.

[1559] System benefits

[1560] This system makes it possible to detect environmental problems such as illegal logging and pollution early and respond quickly. Furthermore, by recognizing the user's emotions, it improves the user experience and supports more effective information provision and problem solving. As a result, it becomes easier to take concrete measures to protect the global environment and leave a beautiful planet for future generations.

[1561] The processing flow will be explained below.

[1562] Step 1:

[1563] The server connects to multiple sensors via the network. Specifically, it establishes a connection using the IP address and port number of the sensor. Once the connection is established, it is ready to periodically acquire data.

[1564] Step 2:

[1565] The server obtains environmental data from each sensor. For example, it calls the sensor.read() method to obtain data such as temperature, humidity, CO2 concentration, and the amount of wood movement. This data is collected in real time and stored in a database on the server.

[1566] Step 3:

[1567] The server performs preprocessing on the collected data, specifically filling in missing values, filtering out noise and outliers, and converting the data to a uniform format.

[1568] Step 4:

[1569] The server performs pattern recognition on the pre-processed data, comparing it with historical data and applying anomaly detection algorithms to detect unusual patterns or sudden changes, such as a sudden decrease in timber volume over a short period of time.

[1570] Step 5:

[1571] The server detects anomalies from the analysis results. Detected anomalies are added to an anomaly list. If multiple anomalies are detected, each anomaly is recorded in the list.

[1572] Step 6:

[1573] The server generates alerts based on the anomaly list. It creates appropriate alert messages depending on the type and severity of the anomaly. For example, it generates a message such as "Sudden timber decline detected in region X."

[1574] Step 7:

[1575] The server then distributes the generated alerts to relevant parties, specifically by sending notifications via email, SMS, and a dedicated dashboard, allowing relevant parties to respond to anomalies immediately.

[1576] Step 8:

[1577] The server generates a detailed report based on the detected anomalies and generated alerts, including when the anomaly was detected, its location, the type of anomaly, its predicted impact, and recommended remedial actions.

[1578] Step 9:

[1579] The server then sends the generated report to the user, who can then receive and review it, typically via email or a web server.

[1580] Step 10:

[1581] The terminal displays the report sent from the server to the user, who can easily check the report through the provided user interface, allowing the user to take any necessary measures immediately.

[1582] Step 11:

[1583] The device uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, voice, typing speed, etc. to detect the user's emotions. Based on the results, the device provides appropriate display content or additional input to respond to the user's reaction.

[1584] Step 12:

[1585] The device dynamically adjusts the display content based on the user's emotions detected by the emotion engine. For example, if the user is feeling anxious, the display content may include additional support information or encouraging messages.

[1586] Step 13:

[1587] The terminal provides an interface for receiving feedback and additional information from the user, who can leave comments on the report or request further investigation.

[1588] Step 14:

[1589] The server receives feedback from the devices and uses it to improve the overall system, adjusting system settings and alert generation rules accordingly.

[1590] Example 2

[1591] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1592] In modern society, early detection and rapid response to environmental problems are extremely important. However, existing monitoring systems often take time to analyze collected data and detect anomalies, resulting in delayed effective responses. Furthermore, they do not provide information tailored to the user's emotions, and the user experience is not sufficiently improved. This makes it difficult to quickly and effectively resolve problems. The purpose of this invention is to solve these problems by enabling early detection of environmental problems, taking appropriate measures, and improving the user experience.

[1593] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1594] In this invention, the server includes a reading means for collecting monitoring data, an analysis means for analyzing the collected data to detect abnormalities, an alert generation means for generating an alert when an abnormality is detected, a reporting means for creating a report based on the created alert, a display means for displaying the created report to the user, and an emotion recognition means for recognizing the user's emotions and dynamically adjusting the display content. This enables rapid collection and analysis of environmental data and early detection of abnormalities, and further realizes flexible information provision according to the user's emotions, thereby improving the user experience.

[1595] "Monitoring data" refers to numerical information obtained using sensors or other devices to measure environmental conditions and fluctuations.

[1596] A "reader" is a device or method for obtaining monitoring data from a sensor over a network.

[1597] The "analysis means" refers to an algorithm or method for processing the acquired monitoring data and determining whether or not there is an abnormality.

[1598] The "alert generation means" is a device or method that generates a message to notify relevant parties when an abnormality is detected by the analysis means.

[1599] A "reporting means" is a device or method that generates detailed reports based on generated alerts.

[1600] A "display means" is a device or method for visually presenting the generated report to a user.

[1601] The "emotion recognition means" is a device or method for analyzing a user's facial expression, voice, and typing speed to identify the user's emotion.

[1602] "Sensors" are measurement devices used to collect environmental data.

[1603] "Environmental data" refers to data that indicates the state of the natural environment, such as temperature, humidity, CO2 concentration, and the amount of wood movement.

[1604] "Preprocessing" is the process of shaping data in analytical means, removing outliers, and filling in missing values.

[1605] A "pattern recognition algorithm" is a mathematical technique or algorithm for detecting specific patterns or anomalies in data.

[1606] An "anomaly detection algorithm" is a specific algorithm for identifying anomalous data points in monitored data.

[1607] The present invention relates to an environmental monitoring system, and provides a method and system for monitoring specific environmental conditions and for early detection and notification of abnormalities. The system also has the ability to recognize a user's emotions and dynamically adjust the display content. Specific embodiments for implementing the invention are described below.

[1608] First, the server accesses multiple sensors via the network and periodically acquires environmental data from them. The main hardware used includes temperature sensors, humidity sensors, CO2 sensors, and wood movement sensors. The software used to acquire the data includes libraries for executing HTTP requests and a database management system (e.g., MySQL).

[1609] Specifically, the server sends an HTTP request to the IP address of each sensor every five minutes, parses the data returned from each sensor (e.g., temperature 30°C, humidity 60%, CO2 concentration 400 ppm, wood movement volume 10 cubic meters) in JSON format, and stores it in a database.

[1610] The server then analyzes the collected environmental data using software such as Python's pandas library and scikit-learn. The data is preprocessed by filtering outliers and imputing missing data. Pattern recognition algorithms are then applied to detect anomalies.

[1611] For example, the server executes the query "SELECT FROM environment_data WHERE timestamp >= NOW() - INTERVAL 5 MINUTE" and uses the z-score method to remove outliers from the retrieved data. It then applies the scikit-learn Isolation Forest algorithm to detect sudden changes in timber movement in a specific area. This process identifies areas suspected of illegal logging.

[1612] If an anomaly is detected, the server generates an alert message and notifies the relevant parties. For this purpose, the server creates the alert message using the Java Spring Framework and sends it via an email API (e.g., Azure SendGrid) with the content, for example, "Sudden timber decline detected in region X."

[1613] The server also creates detailed reports based on anomaly and alert information. The reports are generated in PDF format using document generation tools such as LaTeX. The reports include the date and time the anomaly was detected, its location, type, predicted impact, and recommended countermeasures. The reports are then sent to the relevant parties via an SMTP server.

[1614] When a user checks a report, an emotion recognition engine analyzes the user's facial expressions and voice. For example, Google Teachable Machine or Amazon Rekognition can be used as the emotion engine. The device collects data from the webcam and microphone, and if it determines that the user is anxious, the server provides more detailed information. For example, an additional message such as "Please check for more information related to timber depletion in area X" is displayed.

[1615] Users can view reports through a web browser on their laptop and take immediate action through the provided user interface. The system also has a feedback function, allowing users to leave comments and request additional investigations. This feedback is sent to the server and used to improve the system.

[1616] Examples of prompts include:

[1617] "Write a program that generates an email alert message to notify administrators when a sudden decline in timber is detected in a forest."

[1618] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1619] Step 1:

[1620] Monitoring Data Collection

[1621] The server obtains environmental data from multiple sensors via the network. Specifically, the server sends an HTTP GET request to a list of sensor IP addresses every five minutes and receives data from each sensor. The input is the response from the sensor, and the output is environmental data in JSON format. This data includes temperature, humidity, CO2 concentration, and wood movement.

[1622] Specific behavior:

[1623] The server uses the "requests" library to send HTTP requests to the sensors.

[1624] For example, the response might contain JSON data like "{temp: 30, humidity: 60, CO2: 400, wood_movement: 10}".

[1625] Parse the received data and insert it into a MySQL database.

[1626] Step 2:

[1627] Data preprocessing and analysis

[1628] The server first performs preprocessing to analyze the collected environmental data. The input for preprocessing is the environmental data obtained in step 1, and the output is data formatted in an analyzable format. Next, anomaly detection is performed using a pattern recognition algorithm. The input is the formatted data, and the output is a list of anomalous data.

[1629] Specific behavior:

[1630] The server uses the "pandas" library to read the data and filter outliers.

[1631] For example, a z-score is calculated for each data point and data exceeding 3 sigma are removed as anomalies.

[1632] Next, we use scikit-learn's "Isolation Forest" algorithm to detect anomalies.

[1633] Step 3:

[1634] Anomaly detection and alerting

[1635] The server detects anomalies from the analysis results and adds them to an anomaly list.Then, the alert generation means generates an appropriate alert message and notifies the relevant parties.The input is a list of abnormal data, and the output is the generated alert message.

[1636] Specific behavior:

[1637] Add the region or data point where the anomaly was detected to the anomaly list.

[1638] Automatically generate alert messages and send them to a mail server using the Spring Framework.

[1639] For example, a message such as "Sudden timber decline detected in region X" can be sent using Azure SendGrid.

[1640] Step 4:

[1641] Report creation and delivery

[1642] The server creates a detailed report based on the detected anomalies and generated alerts. The input of the report is the anomaly data and the alert message, and the output is a PDF report, which is then sent to the relevant parties.

[1643] Specific behavior:

[1644] "LaTeX" is used to generate reports based on abnormal data and alert messages.

[1645] For example, the PDF file may contain the date and time of detection, the location of the anomaly, the details of the anomaly, and recommended countermeasures.

[1646] The generated report is sent to the administrator's email address via the "SMTP server".

[1647] Step 5:

[1648] User emotion recognition and display adjustment

[1649] The device uses an emotion engine to recognize the user's emotions and adjust the display content accordingly. The input is the user's facial expressions and voice data, and the output is the optimal screen display according to the user's emotions.

[1650] Specific behavior:

[1651] A webcam and microphone are used to capture the user's facial and voice data.

[1652] Use "Google Teachable Machine" or "Amazon Rekognition" to perform sentiment analysis.

[1653] For example, if the user is concerned, display additional information such as "Check out more information related to timber decline in region X."

[1654] Step 6:

[1655] View reports and gather user feedback

[1656] The terminal displays the report sent from the server to the user and collects feedback from the user. The input is the PDF report and the user's comments, and the output is the feedback data to the server.

[1657] Specific behavior:

[1658] The terminal displays reports using front-end technologies such as "React.js".

[1659] Feedback is collected by users entering comments and pressing the "Submit" button.

[1660] The feedback data is sent to the server via a "POST request" and stored in a database.

[1661] (Application example 2)

[1662] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1663] While there is a need for early detection of environmental problems and swift countermeasures, current systems tend to be slow in collecting and analyzing data, making it difficult to provide timely alerts and countermeasures.Furthermore, there is a lack of functionality to adjust the information provided based on the user's emotions, which means that users' anxiety and tension cannot be alleviated.

[1664] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a reading means for collecting monitoring data, an analysis means for analyzing the collected data to detect abnormalities, an alert generation means for generating an alert when an abnormality is detected, a reporting means for creating a report based on the generated alert, a display means for displaying the created report to the user, and an emotion recognition means for recognizing the user's emotions and dynamically adjusting the notification content. This enables early detection and rapid response to environmental problems, and also allows appropriate information and alert notifications to be provided based on the user's emotions, thereby improving the user experience.

[1665] "Monitoring data" is a general term for various sensor data collected to detect changes and abnormalities in environmental conditions.

[1666] A "reader" is a device or system that collects monitoring data from a sensor.

[1667] "Analysis means" refers to algorithms and programs that process and analyze collected monitoring data and detect abnormalities.

[1668] An "alert generator" is a system or function that generates a warning message or notification based on an anomaly detected by the analysis means.

[1669] "Reporting tools" are systems or functions that create detailed reports based on generated alerts.

[1670] The "display means" refers to a device or interface for visually presenting the created report to the user.

[1671] An "emotion recognition means" is a system or algorithm that recognizes emotions from a user's facial expressions, voice, actions, etc., and analyzes that information.

[1672] A "sensor" is a measuring device that measures environmental data such as temperature, humidity, and CO2 concentration and provides that information.

[1673] An "anomaly detection algorithm" is a calculation method or program for detecting abnormal conditions based on monitoring data.

[1674] "Environmental data" refers to measurements of temperature, humidity, CO2 concentration, etc. related to the natural and artificial environments.

[1675] A "user interface" is the means by which a user interacts with a system or device.

[1676] "Notification content" is the details of the message or alert, including information about the abnormality or warning.

[1677] "Dynamic adjustment" means changing the content in real time according to the situation and the user's state.

[1678] This invention is a system that collects environmental data in real time, detects abnormalities, generates alerts, and dynamically adjusts notification content based on the user's emotions.

[1679] System configuration

[1680] Hardware Configuration

[1681] The system uses the following hardware:

[1682] 1. Sensor

[1683] Temperature sensors, humidity sensors, CO2 concentration sensors, wood movement sensors, etc.

[1684] These sensors are connected to a server via a network.

[1685] 2. Server

[1686] Reads data, analyzes it, generates alerts, and creates reports.

[1687] Store the data in a database and run analysis algorithms.

[1688] 3. User Device

[1689] Smart glasses (e.g., regular smart glasses), laptops, etc.

[1690] It allows users to receive emotion recognition, view reports, and provide feedback.

[1691] Software Configuration

[1692] The system uses the following software:

[1693] 1. Data Collection Module

[1694] Data is collected periodically from each sensor and sent to a server.

[1695] 2. Data Analysis Module

[1696] Run preprocessing and anomaly detection algorithms.

[1697] We use Scikit-learn for data preprocessing and Keras and TensorFlow for anomaly detection models.

[1698] 3. Emotion Recognition Module

[1699] The system analyzes the user's facial expressions captured by smart glasses and recognizes their emotions.

[1700] Extract facial features and infer emotions using OpenCV and dlib.

[1701] 4. Alert Generation Module

[1702] When an anomaly is detected, an appropriate alert message is generated.

[1703] 5. Report Generation Module

[1704] Create detailed reports based on generated alerts.

[1705] 6. Notification Module

[1706] The notification content is dynamically adjusted based on the user's emotions and sent to the user terminal.

[1707] System Operation

[1708] 1. Data Collection

[1709] Sensors periodically read environmental data (e.g., temperature, humidity, CO2 concentration, wood movement).

[1710] The data collection module sends these data to the server.

[1711] 2. Data Analysis

[1712] The server preprocesses the collected data and applies anomaly detection algorithms.

[1713] For example, detecting a sudden decrease in timber volume in a particular area.

[1714] 3. Emotion recognition

[1715] The smart glasses capture images of the user's face, which are then analyzed by an emotion recognition module.

[1716] Determine whether the user is feeling anxious.

[1717] 4. Alert Generation and Notification

[1718] Based on the detected anomalies, an alert generation module generates a warning message.

[1719] The notification module adjusts the notification content according to the user's emotions and displays it on the user terminal.

[1720] Specific examples

[1721] For example, if a sensor in a forest detects a sudden decrease in the amount of timber in a set area, the system operates as follows:

[1722] 1. The server collects data and applies an anomaly detection algorithm to detect sudden declines in timber volume.

[1723] 2. The alert generation module generates a warning message such as "Sudden timber decline detected in region X."

[1724] 3. The user views the report through the smart glasses, and the emotion recognition module detects the user's anxiety.

[1725] 4. The notification module provides additional information to allay concerns, displaying a message such as "Check out more information related to timber decline in region X."

[1726] Prompt Sentence Examples

[1727] We train a model to detect anomalies under each of the following conditions: a sudden rise in temperature, a sudden increase in CO2 concentration, and abnormally low humidity. We also provide the model with facial expression data showing when the user is feeling happy, uncomfortable, or anxious.

[1728] This enables the system to detect environmental problems in real time, respond quickly, and provide information based on user emotions.

[1729] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1730] Step 1:

[1731] The server collects environmental data in real time from multiple sensors via the network. Specifically, it acquires data every five minutes from temperature sensors, humidity sensors, CO2 concentration sensors, and wood movement sensors and stores it in a database. The input is data from each sensor, and the output is environmental data stored in the server's database.

[1732] Step 2:

[1733] The server preprocesses the collected environmental data, filtering out outliers and imputing incomplete data as appropriate. The input is the environmental data stored in the database, and the output is the preprocessed clean data. Specific operations include data cleaning techniques such as mean value imputation and outlier removal.

[1734] Step 3:

[1735] The server analyzes the preprocessed data and applies an anomaly detection algorithm. It uses an anomaly detection model (trained with Keras / TensorFlow) to recognize patterns in the data and detect anomalies. The input is the preprocessed clean data, and the output is a list of detected anomalies. Specifically, it detects abnormal wood movement and abnormal CO2 concentrations.

[1736] Step 4:

[1737] The server generates alerts based on the detected anomalies. The alert generation module creates an alert message depending on the type and severity of the anomaly and notifies the administrator. The input is a list of anomalies, and the output is the generated alert message. Specific operations include generating a warning message such as "Sudden timber movement detected in region X."

[1738] Step 5:

[1739] The server creates a detailed report based on the generated alert. The reporting means generates a report including the detection date and time of the anomaly, its location, type, impact, and countermeasures, and sends it to the user. The input is the generated alert message, and the output is a detailed report. Specifically, the report is generated in HTML or PDF format and sent to the user's email.

[1740] Step 6:

[1741] The device recognizes the user's emotions using an emotion recognition module. It detects emotions by analyzing the user's facial expressions, voice, typing speed, etc. The input is the user's facial expression data and voice data, and the output is the recognized emotion. Specifically, the smart glasses capture facial expressions and estimate the emotion using OpenCV and dlib.

[1742] Step 7:

[1743] The server dynamically adjusts the notification content based on the recognized emotion. If the emotion recognition module detects an emotion such as anxiety, it provides more information or an additional alert message. The input is the recognized emotion, and the output is the adjusted notification message. Specific behaviors include adding a supplemental message such as "Check for more information."

[1744] Step 8:

[1745] The terminal displays the report sent from the server to the user. Through the user interface, the user can check the report and take necessary measures. The input is the report sent from the server, and the output is the user's viewing information and feedback. Specifically, the terminal opens the report in a browser, enters comments, and sends it.

[1746] By executing the above processing steps in succession, the system realizes early detection of environmental problems, prompt response, and provision of information based on the user's emotions.

[1747] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1748] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1749] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1750] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1751] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1752] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1753] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1754] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1755] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1756] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1757] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1758] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1759] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1760] 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.

[1761] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1762] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1763] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1764] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1765] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1766] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1767] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1768] The following is further disclosed regarding the above embodiment.

[1769] (Claim 1)

[1770] reading means for collecting monitoring data;

[1771] an analysis means for analyzing the collected data to detect anomalies;

[1772] alert generation means for generating an alert when an abnormality is detected;

[1773] reporting means for generating reports based on the generated alerts;

[1774] display means for displaying the generated report to a user;

[1775] A system including:

[1776] (Claim 2)

[1777] 2. The system according to claim 1, wherein the reading means is connected to a plurality of sensors through a network and acquires environmental data from each of the sensors.

[1778] (Claim 3)

[1779] 2. The system of claim 1, wherein the analysis means performs pre-processing and pattern recognition on the acquired data and applies anomaly detection algorithms.

[1780] (Claim 4)

[1781] 2. The system according to claim 1, wherein the alert generating means generates an appropriate alert message depending on the type and severity of the abnormality, and delivers the alert to relevant parties via a communication means.

[1782] (Claim 5)

[1783] 10. The system of claim 1, wherein the display means provides a user interface to allow a user to review the generated report and enter feedback.

[1784] "Example 1"

[1785] (Claim 1)

[1786] an acquisition means for collecting monitoring data;

[1787] analytical means for analyzing the collected data in real time, filtering outliers and incomplete data, and applying pattern recognition algorithms to detect anomalies;

[1788] an alert generating means for generating an alert message in response to the detected abnormality and notifying relevant parties;

[1789] A reporting mechanism to generate detailed reports based on generated alerts, detailing the anomaly's detection time, location, type, expected impact, and recommended response;

[1790] A display means for providing an interface that allows a user to easily check the created report and for making comments or requesting additional investigation;

[1791] A system including:

[1792] (Claim 2)

[1793] The system according to claim 1, characterized in that the acquisition means is connected to a plurality of sensor devices via a network and periodically acquires environmental data such as temperature, humidity, CO2 concentration, and amount of wood movement from each sensor.

[1794] (Claim 3)

[1795] 2. The system of claim 1, wherein the analysis means pre-processes the acquired data to filter out incomplete data and outliers, and then applies a pattern recognition algorithm.

[1796] "Application Example 1"

[1797] (Claim 1)

[1798] reading means for collecting monitoring data;

[1799] an analysis means for analyzing the collected data to detect anomalies;

[1800] alert generation means for generating an alert when an abnormality is detected;

[1801] reporting means for generating reports based on the generated alerts;

[1802] display means for displaying the generated report to a user;

[1803] notification means for sending alerts to interested parties using various notification means;

[1804] A system including:

[1805] (Claim 2)

[1806] 2. The system according to claim 1, wherein the reading means is connected to a plurality of sensors through a network and acquires environmental data from each of the sensors.

[1807] (Claim 3)

[1808] 2. The system of claim 1, wherein the analysis means performs pre-processing and pattern recognition on the acquired data and applies anomaly detection algorithms.

[1809] "Example 2: Combining Emotion Engines"

[1810] (Claim 1)

[1811] reading means for collecting monitoring data;

[1812] an analysis means for analyzing the collected data to detect anomalies;

[1813] alert generation means for generating an alert when an abnormality is detected;

[1814] reporting means for generating reports based on the generated alerts;

[1815] display means for displaying the generated report to a user;

[1816] emotion recognition means for recognizing a user's emotion and dynamically adjusting the display content;

[1817] A system including:

[1818] (Claim 2)

[1819] 2. The system according to claim 1, wherein the reading means is connected to a plurality of sensors through a network and acquires environmental data from each of the sensors.

[1820] (Claim 3)

[1821] 2. The system of claim 1, wherein the analysis means performs pre-processing and pattern recognition on the acquired data and applies anomaly detection algorithms.

[1822] (Claim 4)

[1823] 2. The system according to claim 1, wherein the emotion recognition means analyzes the user's facial expression, voice, and typing speed to identify the user's emotion and adjusts the display content accordingly.

[1824] (Claim 5)

[1825] 2. The system of claim 1, wherein the generated report includes the date and time the anomaly was detected, the location, the type of anomaly, its predicted impact, and recommended remedial action.

[1826] "Application example 2 when combining emotion engines"

[1827] (Claim 1)

[1828] reading means for collecting monitoring data;

[1829] an analysis means for analyzing the collected data to detect anomalies;

[1830] alert generation means for generating an alert when an abnormality is detected;

[1831] reporting means for generating reports based on the generated alerts;

[1832] display means for displaying the generated report to a user;

[1833] emotion recognition means for recognizing a user's emotion and dynamically adjusting notification content;

[1834] A system including:

[1835] (Claim 2)

[1836] 2. The system according to claim 1, wherein the reading means is connected to a plurality of sensors through a network and acquires environmental data from each of the sensors.

[1837] (Claim 3)

[1838] 2. The system of claim 1, wherein the analysis means performs pre-processing and pattern recognition on the acquired data and applies anomaly detection algorithms. [Explanation of symbols]

[1839] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. reading means for collecting monitoring data; an analysis means for analyzing the collected data to detect anomalies; alert generation means for generating an alert when an abnormality is detected; reporting means for generating reports based on the generated alerts; display means for displaying the generated report to a user; A system including:

2. 2. The system according to claim 1, wherein said reading means is connected to a plurality of sensors through a network and acquires environmental data from each of said sensors.

3. 2. The system of claim 1, wherein the analysis means performs pre-processing and pattern recognition on the acquired data and applies anomaly detection algorithms.

4. 2. The system according to claim 1, wherein said alert generating means generates an appropriate alert message depending on the type and severity of the abnormality, and distributes the alert to relevant parties via communication means.

5. 2. The system of claim 1, wherein the display means provides a user interface to allow a user to review generated reports and enter feedback.

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