system

Abstract

We provide the system. [Solution] Means of collecting environmental data, A means equipped with an artificial intelligence algorithm for analyzing the collected data, A means of generating notifications based on analysis results and providing information to the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Modern agriculture is becoming difficult to maintain productivity due to population decline and aging. Also, it is difficult to quickly respond to environmental changes such as the occurrence of pests and diseases. Furthermore, there is a problem that timely data analysis and decision-making for optimizing the quality and yield of agricultural crops cannot be performed by conventional methods.

Means for Solving the Problems

[0005] This invention is a system that combines the collection of environmental data using IoT, data analysis using AI, and real-time communication using 5G. This provides the functionality to collect environmental information in real time, analyze that data immediately, and provide it to the user. Furthermore, by obtaining user feedback and making timely adjustments to the AI ​​algorithm, it optimizes crop growth and quality, thereby realizing sustainable agriculture.

[0006] "Environmental data" refers to information such as temperature, humidity, soil pH, and sunlight levels that affect the growth and health of crops.

[0007] "Means of collection" refers to devices and technologies used to acquire environmental data, such as IoT sensors.

[0008] An "artificial intelligence algorithm" is a computational method used to analyze collected data and evaluate the condition of crops.

[0009] "Analysis results" refer to the results of analyzing data generated by an artificial intelligence algorithm.

[0010] "Means for generating notifications" refer to technologies and devices that provide information and alerts to users based on analysis results.

[0011] "High-speed data communication" refers to communication technologies that enable high-speed, real-time data transmission and reception using technologies such as 5G.

[0012] "Feedback data" refers to information about actions taken by users after receiving system notifications and the results of those actions.

[0013] "Methods for fine-tuning" refer to methods for improving the accuracy and performance of artificial intelligence algorithms based on user feedback. [Brief explanation of the drawing]

[0014] [Figure 1]It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] As an embodiment of the present invention, a system is described in which IoT sensors are used to collect data from the agricultural environment, and a server analyzes the collected data. A terminal communicates with the server, provides the analysis results to the user, and the user makes decisions regarding agricultural management based on that information.

[0036] First, the terminal collects environmental data such as temperature, humidity, soil pH, and sunlight from IoT sensors installed on the farm. This data is transmitted to a server in real time using 5G communication. The server uses artificial intelligence algorithms to analyze the received data and evaluate the growth status of crops and the risk of pests and diseases.

[0037] The analysis results are generated as notifications to the user regarding the optimal harvesting time and necessary agricultural tasks. The device displays these notifications to the user and provides specific advice to improve the efficiency of agricultural work. When the user performs tasks based on the notifications, the feedback is sent from the device to the server and used to improve the performance of the AI ​​algorithm.

[0038] As a concrete example, let's say a farm is growing tomatoes. The terminal acquires humidity and temperature data from sensors and sends it to a server. The server determines that the humidity is very high and the risk of disease is increasing. This information is notified to the user, who can quickly take action such as activating a dehumidifying fan or applying fungicide. Through such actions, efficient farm management becomes possible while maintaining the health of the crops.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The terminal periodically acquires environmental data such as temperature, humidity, soil pH, and sunlight from IoT sensors installed on the farm. The acquired data is transmitted to a server using 5G communication.

[0042] Step 2:

[0043] The server executes an artificial intelligence algorithm based on the received environmental data. Through data analysis, it evaluates crop growth status and the risk of pest and disease outbreaks. The analysis results are recorded in a database to prepare for subsequent information provision.

[0044] Step 3:

[0045] Based on the analysis results, the server generates alerts and advice regarding agricultural practices. This includes, for example, early detection of pests and diseases, the amount of water needed, and the appropriate timing for fertilizer application.

[0046] Step 4:

[0047] The terminal receives notifications from the server and presents the information to the user visually. The notifications include concise instructions and detailed analysis results regarding agricultural management.

[0048] Step 5:

[0049] Users check notifications from their devices and perform necessary farming tasks. For example, they might activate a dehumidifying fan or spray mold inhibitor based on the notification.

[0050] Step 6:

[0051] The device collects feedback about the user's actions and sends it back to the server. This feedback includes information such as the content and timing of the actions.

[0052] Step 7:

[0053] The server receives feedback and fine-tunes its artificial intelligence algorithms. This improves the accuracy of subsequent analyses and notifications. This process is repeated to continuously support the efficiency of agriculture.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] In agriculture, environmental factors significantly impact crop growth and health, making it essential to collect and analyze environmental data in real time to implement appropriate agricultural management. However, conventional systems have faced challenges such as delays in data collection and analysis, and difficulty in effectively improving AI algorithms through feedback.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes means for collecting various environmental parameters from the agricultural environment, means for analyzing the collected data using an artificial intelligence algorithm to evaluate the health and risks of crops, means for generating farm work guidelines as notifications based on the analysis results and providing information to the user via a terminal, and means for receiving user work feedback sent from the terminal and improving the performance of the AI ​​algorithm. This enables highly accurate agricultural management through real-time data processing and effective improvement of the AI.

[0059] "Environmental parameters" are numerical information that directly affects crop growth conditions in an agricultural environment, such as temperature, humidity, soil pH, and sunlight.

[0060] An "artificial intelligence algorithm" is a procedure or method that uses machine learning and data analysis techniques to analyze collected data and automatically assess the health and risks of crops.

[0061] "Analysis results" refer to information about conclusions and recommended actions obtained by artificial intelligence algorithms, and are provided to users as notifications.

[0062] A "notification" is information generated based on analysis results, and it is a message that provides users with guidance and warnings for agricultural management.

[0063] "Feedback" refers to data about the farming tasks actually performed by users and the results thereof, and is used to improve the performance of AI algorithms.

[0064] "Mobile communication devices" are digital devices that users can carry with them, such as smartphones and tablets, and are used to receive notifications.

[0065] This invention is a system that combines IoT sensors and advanced data analysis technology in an agricultural environment to achieve efficient management of crops.

[0066] The terminal continuously collects environmental parameters such as temperature, humidity, soil pH, and sunlight using various sensors installed on the farm. This system can, for example, measure temperature and humidity using a DHT22 sensor and evaluate soil moisture content using a YL-69 sensor.

[0067] The terminal transmits collected environmental data to the server via high-speed wireless communication. The server receives this data in real time and analyzes it using artificial intelligence algorithms. The algorithms operate using AI frameworks such as TENSORFLOW® and PyTorch, and evaluate the health of crops and the risk of pests and diseases from the data. Predictions are made during the analysis using machine learning models based on historical data.

[0068] The server generates agricultural management guidelines as notifications based on the analysis results and provides them to users via their devices. These notifications are displayed on mobile communication devices such as smartphones and tablets, helping users to properly plan and carry out their farm work.

[0069] Users perform farming tasks based on the notifications and send the results and feedback to the server via their devices. This allows the server to continuously adjust the AI ​​algorithms and improve the overall accuracy of the system.

[0070] As a concrete example, in a tomato farm, if humidity rises and the risk of disease increases, the server analyzes this information and sends a notification to the user instructing them to activate a dehumidifying fan. By following this instruction and performing dehumidification, the user can maintain the health of the crops while performing efficient farm work.

[0071] Example prompt: "Analyze data from farm sensors and assess crop health. If there are abnormalities in humidity and temperature, suggest a way to notify the user of appropriate actions."

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The terminal acquires environmental parameters from IoT sensors installed on the farm. Specifically, the terminal collects temperature and humidity data from the DHT22 sensor and soil moisture data from the YL-69 sensor. This data is acquired as real-time, accurate input information that reflects the environmental conditions and is ready to be transmitted to the server.

[0075] Step 2:

[0076] The terminal transmits the collected environmental data to the server using 5G communication. This transmission process requires data to be sent without delay, allowing the server to begin analysis immediately. The information received by the server becomes output data in the form of real-time environmental parameters.

[0077] Step 3:

[0078] The server records the received environmental data in a database and performs analysis using artificial intelligence algorithms. Here, TensorFlow or PyTorch is used to perform data calculations to predict crop health and pest / disease risks from the data. The output derived from the data input is a risk assessment and recommended actions based on the analysis results.

[0079] Step 4:

[0080] The server uses the analysis results to generate notifications that provide guidance for agricultural work. These notifications include appropriate actions and countermeasures and are provided to the user via the terminal. The generated notifications output guidelines that serve as actual actions for the user.

[0081] Step 5:

[0082] Users check notifications sent via their devices and perform farm work based on the instructions. Specifically, based on the notification, users might activate a dehumidifying fan or spray fungicide on the farm. The user's actions serve as input for feedback to the system, recording the completed work.

[0083] Step 6:

[0084] Feedback from user actions is sent to the server via the terminal. The server uses this feedback to fine-tune the artificial intelligence algorithm and improve analysis accuracy. The output derived from the feedback results in more accurate risk predictions and recommended actions for future use.

[0085] (Application Example 1)

[0086] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0087] In factories and other facilities, optimizing equipment operation and maintenance schedules is crucial. However, traditionally, understanding equipment operation and streamlining preventative maintenance have been challenging. Furthermore, equipment failures can lead to decreased production efficiency and wasted energy, necessitating real-time monitoring and rapid response.

[0088] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0089] In this invention, the server includes a device for acquiring environmental data, a device equipped with a machine learning algorithm for analyzing the acquired information, and a device for monitoring the operating patterns of equipment and performing maintenance improvements. This enables efficient monitoring of the operating status of factory equipment and optimization of preventive maintenance.

[0090] "Environmental data" refers to information that indicates environmental conditions such as temperature, humidity, and light intensity, and is important information related to the operation of the equipment.

[0091] A "data acquisition device" refers to equipment that collects environmental data using sensors and other means, and transmits that data to a server.

[0092] A "machine learning algorithm for analysis" is a computational method for analyzing large amounts of data and finding meaningful patterns within it.

[0093] A "device for monitoring equipment operating patterns" is a device that continuously monitors the operating status of equipment and understands trends in its operation and performance.

[0094] A "device for maintenance improvement" is a device that develops an appropriate maintenance plan for equipment based on the analysis results of monitored operating patterns, thereby improving operational efficiency.

[0095] To implement this invention, first, environmental data such as temperature, humidity, and light intensity are acquired in the factory environment using IoT sensors. The acquired data is transmitted to a server in real time using the MQTT protocol. The server analyzes the data using machine learning algorithms with the TensorFlow library. Based on the analysis results, the operating patterns of the equipment are evaluated, and maintenance needs and an optimized operating schedule are generated. The results are notified to the user's smartphone or tablet via a user interface using React Native or Flutter®. The user then takes appropriate maintenance actions based on the received information.

[0096] As a concrete example, when a sensor detects that a cooling system in a factory is overheating, the server predicts a malfunction and immediately notifies the user that maintenance is required. The user can check the notification on a smartphone app and schedule the cleaning of the cooling system's filters. This enables preventative maintenance and efficient factory operations.

[0097] An example of an input prompt for the generated AI model is: "Analyze the temperature data of factory equipment and propose an optimized approach for preventive maintenance in case of anomalies."

[0098] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0099] Step 1:

[0100] The terminal acquires environmental data such as temperature, humidity, and light intensity through IoT sensors within the factory. This data is acquired using a sensor interface, and initial data format conversion is performed on the terminal. The output is ready data for transmission to the server.

[0101] Step 2:

[0102] The terminal sends prepared environment data to the server using the MQTT protocol. It receives formatted data as input and relays that data to the server in real time. The output of this step is the data stream received by the server.

[0103] Step 3:

[0104] The server analyzes the received data stream using a machine learning algorithm based on TensorFlow. It takes the received data as input, performs data cleansing and transformation, and formats it suitable for analysis. As a result, it outputs the results of the equipment operation pattern analysis.

[0105] Step 4:

[0106] Based on the analysis results, the server uses a generating AI model to generate instructions for preventative maintenance and optimization of the operating schedule. In this step, the analysis results are taken as input, and the recommended actions from the generating AI are obtained as output. The output is a message containing the instructions.

[0107] Step 5:

[0108] The user's device receives instructions sent from the server and displays notifications through an application built with React Native or Flutter. It receives the instruction message as input and outputs that message on the screen in a user-friendly format.

[0109] Step 6:

[0110] The user performs actual maintenance and adjustment work based on the information they receive. Specifically, the user refers to the app to perform tasks such as cleaning the cooling system filters, and sends the details of these actions to the server as feedback through the app. This feedback data becomes the output.

[0111] Step 7:

[0112] The server receives feedback data from users and incorporates it into a dataset to fine-tune the machine learning algorithm. This improves the accuracy of the data algorithm and is used as input for new analysis processes.

[0113] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0114] In embodiments for carrying out the present invention, a system incorporating an emotion engine for recognizing user emotions and providing users with information useful for agricultural management is described. This system not only collects environmental data and provides analysis results, but also has the feature of customizing information based on the user's emotional state using the emotion engine.

[0115] First, the terminal collects environmental data from IoT sensors installed on the farm and transmits it to the server via high-speed data communication. The server analyzes the received data using an artificial intelligence algorithm to assess the health of the crops and the risk of pests and diseases. When generating notifications based on these analysis results, the emotion engine analyzes the user's emotional data and customizes the notification content and how it is delivered. For example, if it is determined that the user is feeling stressed, the notification will be made more friendly and will include advice to reduce stress.

[0116] As a concrete example, suppose a user is managing a tomato field. Environmental data is collected as usual, and the analysis results indicate a high risk of pest and disease outbreaks. On the other hand, the emotion engine determines that the user's emotional state is stressful, so in addition to the usual notification, the device presents the user with a message such as, "Take a break and relax." In this way, providing information according to emotions can reduce the user's psychological burden and support efficient agricultural management.

[0117] User feedback is collected by the terminal and sent to the server. The server uses this feedback to fine-tune the algorithm and further improve the quality of information provided. Through this continuous process, the present invention achieves improved efficiency in agricultural management.

[0118] The following describes the processing flow.

[0119] Step 1:

[0120] The terminal acquires environmental data such as temperature, humidity, soil pH, and sunlight from IoT sensors installed on the farm. This data is transmitted to the server in real time using 5G communication.

[0121] Step 2:

[0122] The server analyzes the received environmental data using artificial intelligence algorithms. Through this analysis, it assesses the health of the crops and the risk of pest and disease outbreaks, and stores the results in a database.

[0123] Step 3:

[0124] The server collects user emotion data using an emotion engine. This data is obtained, for example, from emotion analysis based on user facial recognition and analysis of voice tone.

[0125] Step 4:

[0126] The server generates optimized notifications based on analyzed environmental and sentiment data. These notifications may include specific farming task suggestions or encouraging messages tailored to the user's emotional state.

[0127] Step 5:

[0128] The device displays notifications sent from the server to the user. The notification content is adjusted according to the user's emotional state, thus reducing the user's psychological burden.

[0129] Step 6:

[0130] Users perform agricultural tasks based on notifications from their devices. User feedback is sent to the server via the device. This feedback includes information such as what tasks were performed and whether they were successful or not.

[0131] Step 7:

[0132] The server analyzes user feedback and fine-tunes its artificial intelligence algorithms. This improves the accuracy of subsequent data analysis and notification generation, thereby enhancing the overall usefulness of the system.

[0133] (Example 2)

[0134] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0135] Traditional agricultural management systems provide notifications based on the analysis of environmental information, but they fail to consider the user's emotions and psychological state, resulting in a lack of useful information for users experiencing stress. Furthermore, they are insufficient in terms of real-time data communication and continuous improvement based on user feedback.

[0136] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0137] In this invention, the server includes means for collecting environmental information, means equipped with an intelligent algorithm for analyzing the collected information, and means for customizing notification content with an emotion engine that evaluates the user's emotional state. This makes it possible to provide information that takes into account the user's psychological state. Furthermore, by further including high-speed information communication means for immediate information exchange and means for receiving evaluation data from the user and fine-tuning the intelligent algorithm, the quality of information provision can be continuously improved.

[0138] "Environmental information" is a general term for information related to crop growth and health in agricultural settings, such as temperature, humidity, soil pH, and moisture content.

[0139] An "intelligent algorithm" is an algorithm used for data analysis, utilizing machine learning and artificial intelligence technologies to extract useful information and patterns from data.

[0140] "Users" refer to agricultural workers and managers who use this system and are the entities that make decisions based on the information provided.

[0141] An "emotional engine" is an engine for analyzing and evaluating a user's emotional state, using technologies such as natural language processing and vital sign analysis to quantify stress, fatigue, and other emotional states.

[0142] "Customizing notification content" refers to the process of adjusting the format and expression of the information provided according to the user's emotional state.

[0143] Embodiments of the present invention relate to a management system in the agricultural field. Specific implementation methods are described below.

[0144] The system primarily consists of terminals, servers, and users. The terminals are installed on farms and collect environmental information using various sensors. These sensors include temperature sensors to measure air temperature, humidity sensors to detect humidity, and soil sensors to measure soil pH and moisture content. This data is transmitted to the server using network technologies such as Wi-Fi and LoRa.

[0145] The server receives transmitted environmental information and analyzes the data using intelligent algorithms. Specifically, it utilizes software such as Python and TensorFlow, and uses machine learning models to assess crop health and pest / disease risk. Simultaneously, the server uses an emotion engine to analyze vital data and user input information to assess the user's emotional state. This analysis also utilizes natural language processing techniques to quantify the user's stress and fatigue levels.

[0146] Based on the analysis results, the server generates notifications for the user and customizes them according to the emotion engine's analysis. For example, if the user is feeling stressed, the server will make the notification more user-friendly and add encouraging messages. For example, for a user managing crops while traveling, a possible notification might be, "The risk of pests and diseases is high, so pay attention to your crops. Take a break and refresh yourself."

[0147] Users can view notifications sent from the server via their devices. These notifications are displayed through a dedicated application on smartphones and tablets, supporting the user's actions. User feedback is sent to the server via the device. This feedback information is used by the server to continuously refine its intelligent algorithms and emotion engine, improving the accuracy of information delivery.

[0148] Examples of prompts for a generative AI model include the following:

[0149] "Based on the management status of the tomato field, please suggest what kind of notification should be sent if a user is experiencing stress."

[0150] Through this mechanism, the present invention enables effective information provision that takes into account the user's psychological state, thereby supporting improved efficiency in agricultural management.

[0151] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0152] Step 1:

[0153] The terminal collects environmental information using various sensors installed on the farm. Specifically, a temperature sensor measures the temperature, a humidity sensor measures the humidity, and a soil sensor measures the pH value and moisture content. This environmental information is obtained as input data. The terminal packages this data and prepares it for transmission to the server. The output is the data package of environmental information that is sent to the server.

[0154] Step 2:

[0155] The server receives environmental information sent from the terminal. The input data is a data package containing various environmental factors of the farm. The server analyzes this data using intelligent algorithms. For example, it uses Python and TensorFlow to run machine learning models and perform crop health assessments and predict pest and disease risks. The output is the crop status and risk assessment information as a result of the analysis.

[0156] Step 3:

[0157] The server receives vital data and user input transmitted from the terminal to evaluate the user's emotional state. The input data consists of individual emotional indicators such as heart rate and stress level. The emotion engine performs natural language processing and data analysis to quantify and diagnose the user's emotional state. The output is an evaluation report regarding the user's emotional state.

[0158] Step 4:

[0159] The server customizes the notification content based on the analysis results and sentiment evaluation from the previous step. The inputs are crop status evaluation information and the user's sentiment evaluation report. The server uses a generative AI model to generate friendly notification messages and advice tailored to the sentiment. The output is the optimized notification message.

[0160] Step 5:

[0161] The terminal displays notification messages received from the server to the user via a smartphone or tablet application. The input data is the notification message sent from the server. The output is an information notification screen that the user can view through the terminal. This allows the user to receive information necessary for efficient farm management in real time.

[0162] Step 6:

[0163] Users input feedback on notifications into a terminal application and send it to the server. The input data is user feedback information regarding the notification. The server receives this feedback and uses it to fine-tune the intelligent algorithms and emotion engine. The output is the improved algorithm settings. This allows the system to continuously improve and provide more accurate information.

[0164] (Application Example 2)

[0165] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0166] In factory and other work environments, providing information without considering the emotional state of workers can negatively impact work efficiency and safety. In particular, uniform notifications and information provision to workers experiencing stress or fatigue can actually increase their workload; therefore, a flexible approach tailored to the emotional state of workers is required.

[0167] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0168] In this invention, the server includes means for collecting environmental information, means equipped with a machine learning algorithm for analyzing the collected information, means for generating warnings based on the analysis results and providing information to the user, and means for customizing the information according to the user's emotional state using an emotion recognition engine. This enables flexible information provision according to the emotional state of the worker.

[0169] "Means of collecting environmental information" refers to devices and mechanisms that use sensors to acquire various data such as temperature, humidity, light intensity, and sound from specific environments such as factories and farms.

[0170] A "machine learning algorithm" is a type of artificial intelligence used to identify patterns based on collected data and perform analysis and predictions. It is a technology that can automatically improve its accuracy based on past data.

[0171] "Means for generating warnings and providing information to users" refers to a system that uses various forms of notification, such as visuals and audio, to alert or instruct users based on data analysis results.

[0172] An "emotion recognition engine" is software or algorithms that determine a user's emotions and psychological state from their biometric data and behavioral data, and then provide appropriate responses and information based on that state.

[0173] The system implementing this invention aims to recognize the emotions of workers in a factory and provide them with appropriate information. The system mainly consists of a server, peripheral terminals, and users.

[0174] The server collects data from sensors installed throughout the factory to gather environmental information. This information includes temperature, humidity, noise levels, and more. The collected data is transmitted to the server via high-speed data communication.

[0175] The transmitted data is analyzed on the server using machine learning algorithms that leverage programming languages ​​such as Python. Machine learning frameworks like TensorFlow and PyTorch are used for this analysis. The analysis results serve as the basis for generating subsequent notifications.

[0176] Simultaneously, an emotion recognition engine uses the worker's biometric data (e.g., heart rate data obtained from a smartwatch) to determine their current emotional state. This engine optimizes the method of information transmission based on the user's psychological state.

[0177] For example, if a worker is feeling stressed, the terminal will display a message such as "Let's take a short break" in a visually and audibly appealing format, based on analysis results from the server. This allows workers to continue their work safely and efficiently while reducing their burden.

[0178] An example of a prompt message to be fed into a generative AI model is: "Based on data received from a wearable device, analyze the worker's current emotional state and provide a stress-reducing message at the appropriate time."

[0179] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0180] Step 1:

[0181] The terminal collects environmental information from sensors within the factory. Input data includes temperature, humidity, and noise levels. The terminal temporarily stores this data and transmits it to the server using high-speed data communication.

[0182] Step 2:

[0183] The server uses machine learning algorithms to analyze the received environmental data. TensorFlow and PyTorch are used for pattern recognition and anomaly detection. The analysis output provides indicators of the working environment's state and potential risks.

[0184] Step 3:

[0185] Simultaneously, the server receives biometric data from the worker's smartwatch. This input includes heart rate and body movements. The server uses an emotion recognition engine to estimate the worker's emotional state from the input data. The output is an evaluation of the emotional state at that moment.

[0186] Step 4:

[0187] The server integrates the results of environmental data analysis and emotion recognition evaluation to generate appropriate notifications. This notification generation process includes evaluating data correlations and determining optimized messages to reduce the mental burden on workers.

[0188] Step 5:

[0189] The terminal receives notifications from the server and provides them to the worker in an appropriate manner. Using visual displays and audio guidance, it presents messages prompting the worker to take specific actions, such as "take a break." This allows the worker to receive feedback tailored to their own situation.

[0190] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0191] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0192] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0193] [Second Embodiment]

[0194] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0195] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0196] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0197] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0198] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0199] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0200] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0201] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0202] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0203] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0204] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0205] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0206] As an embodiment of the present invention, a system is described in which IoT sensors are used to collect data from the agricultural environment, and a server analyzes the collected data. A terminal communicates with the server, provides the analysis results to the user, and the user makes decisions regarding agricultural management based on that information.

[0207] First, the terminal collects environmental data such as temperature, humidity, soil pH, and sunlight from IoT sensors installed on the farm. This data is transmitted to a server in real time using 5G communication. The server uses artificial intelligence algorithms to analyze the received data and evaluate the growth status of crops and the risk of pests and diseases.

[0208] The analysis results are generated as notifications to the user regarding the optimal harvesting time and necessary agricultural tasks. The device displays these notifications to the user and provides specific advice to improve the efficiency of agricultural work. When the user performs tasks based on the notifications, the feedback is sent from the device to the server and used to improve the performance of the AI ​​algorithm.

[0209] As a concrete example, let's say a farm is growing tomatoes. The terminal acquires humidity and temperature data from sensors and sends it to a server. The server determines that the humidity is very high and the risk of disease is increasing. This information is notified to the user, who can quickly take action such as activating a dehumidifying fan or applying fungicide. Through such actions, efficient farm management becomes possible while maintaining the health of the crops.

[0210] The following describes the processing flow.

[0211] Step 1:

[0212] The terminal periodically acquires environmental data such as temperature, humidity, soil pH, and sunlight from IoT sensors installed on the farm. The acquired data is transmitted to a server using 5G communication.

[0213] Step 2:

[0214] The server executes an artificial intelligence algorithm based on the received environmental data. Through data analysis, it evaluates crop growth status and the risk of pest and disease outbreaks. The analysis results are recorded in a database to prepare for subsequent information provision.

[0215] Step 3:

[0216] Based on the analysis results, the server generates alerts and advice regarding agricultural practices. This includes, for example, early detection of pests and diseases, the amount of water needed, and the appropriate timing for fertilizer application.

[0217] Step 4:

[0218] The terminal receives notifications from the server and presents the information to the user visually. The notifications include concise instructions and detailed analysis results regarding agricultural management.

[0219] Step 5:

[0220] Users check notifications from their devices and perform necessary farming tasks. For example, they might activate a dehumidifying fan or spray mold inhibitor based on the notification.

[0221] Step 6:

[0222] The device collects feedback about the user's actions and sends it back to the server. This feedback includes information such as the content and timing of the actions.

[0223] Step 7:

[0224] The server receives feedback and fine-tunes its artificial intelligence algorithms. This improves the accuracy of subsequent analyses and notifications. This process is repeated to continuously support the efficiency of agriculture.

[0225] (Example 1)

[0226] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0227] In agriculture, environmental factors significantly impact crop growth and health, making it essential to collect and analyze environmental data in real time to implement appropriate agricultural management. However, conventional systems have faced challenges such as delays in data collection and analysis, and difficulty in effectively improving AI algorithms through feedback.

[0228] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0229] In this invention, the server includes means for collecting various environmental parameters from the agricultural environment, means for analyzing the collected data using an artificial intelligence algorithm to evaluate the health and risks of crops, means for generating farm work guidelines as notifications based on the analysis results and providing information to the user via a terminal, and means for receiving user work feedback sent from the terminal and improving the performance of the AI ​​algorithm. This enables highly accurate agricultural management through real-time data processing and effective improvement of the AI.

[0230] "Environmental parameters" are numerical information that directly affects crop growth conditions in an agricultural environment, such as temperature, humidity, soil pH, and sunlight.

[0231] An "artificial intelligence algorithm" is a procedure or method that uses machine learning and data analysis techniques to analyze collected data and automatically assess the health and risks of crops.

[0232] "Analysis results" refer to information about conclusions and recommended actions obtained by artificial intelligence algorithms, and are provided to users as notifications.

[0233] A "notification" is information generated based on analysis results, and it is a message that provides users with guidance and warnings for agricultural management.

[0234] "Feedback" refers to data about the farming tasks actually performed by users and the results thereof, and is used to improve the performance of AI algorithms.

[0235] "Mobile communication devices" are digital devices that users can carry with them, such as smartphones and tablets, and are used to receive notifications.

[0236] This invention is a system that combines IoT sensors and advanced data analysis technology in an agricultural environment to achieve efficient management of crops.

[0237] The terminal continuously collects environmental parameters such as temperature, humidity, soil pH, and sunlight using various sensors installed on the farm. This system can, for example, measure temperature and humidity using a DHT22 sensor and evaluate soil moisture content using a YL-69 sensor.

[0238] The terminal transmits collected environmental data to the server via high-speed wireless communication. The server receives this data in real time and analyzes it using artificial intelligence algorithms. The algorithms operate using AI frameworks such as TensorFlow and PyTorch, and evaluate the health of crops and the risk of pests and diseases from the data. Predictions are made during the analysis using machine learning models based on historical data.

[0239] The server generates agricultural management guidelines as notifications based on the analysis results and provides them to users via their devices. These notifications are displayed on mobile communication devices such as smartphones and tablets, helping users to properly plan and carry out their farm work.

[0240] Users perform farming tasks based on the notifications and send the results and feedback to the server via their devices. This allows the server to continuously adjust the AI ​​algorithms and improve the overall accuracy of the system.

[0241] As a concrete example, in a tomato farm, if humidity rises and the risk of disease increases, the server analyzes this information and sends a notification to the user instructing them to activate a dehumidifying fan. By following this instruction and performing dehumidification, the user can maintain the health of the crops while performing efficient farm work.

[0242] Example prompt: "Analyze data from farm sensors and assess crop health. If there are abnormalities in humidity and temperature, suggest a way to notify the user of appropriate actions."

[0243] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0244] Step 1:

[0245] The terminal acquires environmental parameters from IoT sensors installed on the farm. Specifically, the terminal collects temperature and humidity data from the DHT22 sensor and soil moisture data from the YL-69 sensor. This data is acquired as real-time, accurate input information that reflects the environmental conditions and is ready to be transmitted to the server.

[0246] Step 2:

[0247] The terminal transmits the collected environmental data to the server using 5G communication. This transmission process requires data to be sent without delay, allowing the server to begin analysis immediately. The information received by the server becomes output data in the form of real-time environmental parameters.

[0248] Step 3:

[0249] The server records the received environmental data in a database and performs analysis using artificial intelligence algorithms. Here, TensorFlow or PyTorch is used to perform data calculations to predict crop health and pest / disease risks from the data. The output derived from the data input is a risk assessment and recommended actions based on the analysis results.

[0250] Step 4:

[0251] The server uses the analysis results to generate notifications that provide guidance for agricultural work. These notifications include appropriate actions and countermeasures and are provided to the user via the terminal. The generated notifications output guidelines that serve as actual actions for the user.

[0252] Step 5:

[0253] Users check notifications sent via their devices and perform farm work based on the instructions. Specifically, based on the notification, users might activate a dehumidifying fan or spray fungicide on the farm. The user's actions serve as input for feedback to the system, recording the completed work.

[0254] Step 6:

[0255] Feedback from user actions is sent to the server via the terminal. The server uses this feedback to fine-tune the artificial intelligence algorithm and improve analysis accuracy. The output derived from the feedback results in more accurate risk predictions and recommended actions for future use.

[0256] (Application Example 1)

[0257] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0258] In factories and other facilities, optimizing equipment operation and maintenance schedules is crucial. However, traditionally, understanding equipment operation and streamlining preventative maintenance have been challenging. Furthermore, equipment failures can lead to decreased production efficiency and wasted energy, necessitating real-time monitoring and rapid response.

[0259] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0260] In this invention, the server includes a device for acquiring environmental data, a device equipped with a machine learning algorithm for analyzing the acquired information, and a device for monitoring the operating patterns of equipment and performing maintenance improvements. This enables efficient monitoring of the operating status of factory equipment and optimization of preventive maintenance.

[0261] "Environmental data" refers to information that indicates environmental conditions such as temperature, humidity, and light intensity, and is important information related to the operation of the equipment.

[0262] A "data acquisition device" refers to equipment that collects environmental data using sensors and other means, and transmits that data to a server.

[0263] A "machine learning algorithm for analysis" is a computational method for analyzing large amounts of data and finding meaningful patterns within it.

[0264] A "device for monitoring equipment operating patterns" is a device that continuously monitors the operating status of equipment and understands trends in its operation and performance.

[0265] A "device for maintenance improvement" is a device that develops an appropriate maintenance plan for equipment based on the analysis results of monitored operating patterns, thereby improving operational efficiency.

[0266] To implement this invention, first, environmental data such as temperature, humidity, and light intensity are acquired in the factory environment using IoT sensors. The acquired data is transmitted to a server in real time using the MQTT protocol. The server analyzes the data using machine learning algorithms with the TensorFlow library. Based on the analysis results, the operating patterns of the equipment are evaluated, and maintenance needs and an optimized operating schedule are generated. The results are notified to the user's smartphone or tablet via a user interface using React Native or Flutter. The user then takes appropriate maintenance actions based on the received information.

[0267] As a concrete example, when a sensor detects that a cooling system in a factory is overheating, the server predicts a malfunction and immediately notifies the user that maintenance is required. The user can check the notification on a smartphone app and schedule the cleaning of the cooling system's filters. This enables preventative maintenance and efficient factory operations.

[0268] An example of an input prompt for the generated AI model is: "Analyze the temperature data of factory equipment and propose an optimized approach for preventive maintenance in case of anomalies."

[0269] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0270] Step 1:

[0271] The terminal acquires environmental data such as temperature, humidity, and light intensity through IoT sensors within the factory. This data is acquired using a sensor interface, and initial data format conversion is performed on the terminal. The output is ready data for transmission to the server.

[0272] Step 2:

[0273] The terminal sends prepared environment data to the server using the MQTT protocol. It receives formatted data as input and relays that data to the server in real time. The output of this step is the data stream received by the server.

[0274] Step 3:

[0275] The server analyzes the received data stream using a machine learning algorithm based on TensorFlow. It takes the received data as input, performs data cleansing and transformation, and formats it suitable for analysis. As a result, it outputs the results of the equipment operation pattern analysis.

[0276] Step 4:

[0277] Based on the analysis results, the server uses the generated AI model to generate instructions regarding preventive maintenance and optimization of the operation schedule. In this step, the analysis results are taken as input, and the recommended actions by the generated AI are obtained as output. The output is a message with the content of the instruction.

[0278] Step 5:

[0279] The user's terminal receives the instructions sent from the server and displays a notification through an application built with React Native or Flutter. It receives the message with the content of the instruction as input and outputs the message in a user-friendly format on the screen.

[0280] Step 6:

[0281] The user performs actual maintenance and adjustment work based on the notified information. As specific operations, the user refers to the application to perform tasks such as cleaning the filter of the cooling device and sends the content as feedback to the server through the application. This feedback data becomes the output.

[0282] Step 7:

[0283] The server receives the feedback data from the user and incorporates the feedback into the dataset for fine-tuning the machine learning algorithm. Thereby, the accuracy of the data algorithm is improved and it is used as input for a new analysis process.

[0284] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0285] In the embodiments for implementing the present invention, a system incorporating an emotion engine for recognizing the user's emotions and providing information useful for agricultural management to the user will be described. This system not only collects environmental data and provides analysis results, but also has the feature of customizing information based on the user's emotional state using an emotion engine.

[0286] First, the terminal collects environmental data from IoT sensors installed on the farm and transmits this data to the server via high-speed data communication. The server analyzes the received data using an artificial intelligence algorithm and evaluates the health status of the crops and the risk of pests and diseases. When generating a notification based on this analysis result, the emotion engine analyzes the user's emotion data and customizes the notification content and its transmission method. For example, if the user is determined to be feeling stressed, the notification is made more friendly and advice for reducing stress is also added.

[0287] As a specific example, assume that a certain user is managing a tomato field. Environmental data is collected as usual, and an analysis result indicating a high risk of pests and diseases is obtained. On the other hand, since the emotion engine determines that the user's emotional state is stressful, in addition to the normal notification, the terminal presents the user with a message such as "Take a break and relax." In this way, by providing information according to emotions, it is possible to reduce the user's psychological burden and support efficient agricultural management.

[0288] The user's feedback is collected by the terminal and transmitted to the server. The server utilizes this feedback to finely adjust the algorithm and further improve the quality of information provision. Through this continuous process, the present invention realizes an improvement in the efficiency of agricultural management.

[0289] The following will describe the processing flow.

[0290] Step 1:

[0291] The terminal acquires environmental data such as temperature, humidity, soil pH, and sunlight from IoT sensors installed on the farm. This data is transmitted to the server in real time using 5G communication.

[0292] Step 2:

[0293] The server analyzes the received environmental data using artificial intelligence algorithms. Through this analysis, it assesses the health of the crops and the risk of pest and disease outbreaks, and stores the results in a database.

[0294] Step 3:

[0295] The server collects user emotion data using an emotion engine. This data is obtained, for example, from emotion analysis based on user facial recognition and analysis of voice tone.

[0296] Step 4:

[0297] The server generates optimized notifications based on analyzed environmental and sentiment data. These notifications may include specific farming task suggestions or encouraging messages tailored to the user's emotional state.

[0298] Step 5:

[0299] The device displays notifications sent from the server to the user. The notification content is adjusted according to the user's emotional state, thus reducing the user's psychological burden.

[0300] Step 6:

[0301] Users perform agricultural tasks based on notifications from their devices. User feedback is sent to the server via the device. This feedback includes information such as what tasks were performed and whether they were successful or not.

[0302] Step 7:

[0303] The server analyzes the user's feedback and fine-tunes the artificial intelligence algorithm. This can improve the accuracy of subsequent data analysis and notification generation, enhancing the overall usefulness of the system.

[0304] (Example 2)

[0305] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0306] Conventional agricultural management systems provide notifications based on the analysis of environmental information, but they do not consider the user's emotions and psychological state, so there is a problem that useful information cannot be provided to users who feel stressed. Also, they were insufficient in terms of real-time data communication and continuous improvement that reflects feedback from users.

[0307] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0308] In this invention, the server includes means for collecting environmental information, means equipped with an intelligent algorithm for analyzing the collected information, an emotion engine for evaluating the user's emotional state, and means for customizing the notification content. This enables the provision of information that takes into account the user's psychological state. Also, by further including high-speed information communication means for immediately exchanging information and means for receiving evaluation data from the user and fine-tuning the intelligent algorithm, the quality of information provision can be continuously improved.

[0309] "Environmental information" is a general term for information related to the growth and health of crops, such as temperature, humidity, soil pH, and moisture content at the agricultural site.

[0310] "Intelligent algorithm" is an algorithm used for data analysis, and is a technology that utilizes machine learning and artificial intelligence technologies to extract useful information and patterns from data.

[0311] "Users" refer to agricultural workers and managers who use this system and are the entities that make decisions based on the information provided.

[0312] An "emotional engine" is an engine for analyzing and evaluating a user's emotional state, using technologies such as natural language processing and vital sign analysis to quantify stress, fatigue, and other emotional states.

[0313] "Customizing notification content" refers to the process of adjusting the format and expression of the information provided according to the user's emotional state.

[0314] Embodiments of the present invention relate to a management system in the agricultural field. Specific implementation methods are described below.

[0315] The system primarily consists of terminals, servers, and users. The terminals are installed on farms and collect environmental information using various sensors. These sensors include temperature sensors to measure air temperature, humidity sensors to detect humidity, and soil sensors to measure soil pH and moisture content. This data is transmitted to the server using network technologies such as Wi-Fi and LoRa.

[0316] The server receives transmitted environmental information and analyzes the data using intelligent algorithms. Specifically, it utilizes software such as Python and TensorFlow, and uses machine learning models to assess crop health and pest / disease risk. Simultaneously, the server uses an emotion engine to analyze vital data and user input information to assess the user's emotional state. This analysis also utilizes natural language processing techniques to quantify the user's stress and fatigue levels.

[0317] Based on the analysis results, the server generates notifications for the user and customizes them according to the emotion engine's analysis. For example, if the user is feeling stressed, the server will make the notification more user-friendly and add encouraging messages. For example, for a user managing crops while traveling, a possible notification might be, "The risk of pests and diseases is high, so pay attention to your crops. Take a break and refresh yourself."

[0318] Users can view notifications sent from the server via their devices. These notifications are displayed through a dedicated application on smartphones and tablets, supporting the user's actions. User feedback is sent to the server via the device. This feedback information is used by the server to continuously refine its intelligent algorithms and emotion engine, improving the accuracy of information delivery.

[0319] Examples of prompts for a generative AI model include the following:

[0320] "Based on the management status of the tomato field, please suggest what kind of notification should be sent if a user is experiencing stress."

[0321] Through this mechanism, the present invention enables effective information provision that takes into account the user's psychological state, thereby supporting improved efficiency in agricultural management.

[0322] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0323] Step 1:

[0324] The terminal collects environmental information using various sensors installed on the farm. Specifically, a temperature sensor measures the temperature, a humidity sensor measures the humidity, and a soil sensor measures the pH value and moisture content. This environmental information is obtained as input data. The terminal packages this data and prepares it for transmission to the server. The output is the data package of environmental information that is sent to the server.

[0325] Step 2:

[0326] The server receives environmental information sent from the terminal. The input data is a data package containing various environmental factors of the farm. The server analyzes this data using intelligent algorithms. For example, it uses Python and TensorFlow to run machine learning models and perform crop health assessments and predict pest and disease risks. The output is the crop status and risk assessment information as a result of the analysis.

[0327] Step 3:

[0328] The server receives vital data and user input transmitted from the terminal to evaluate the user's emotional state. The input data consists of individual emotional indicators such as heart rate and stress level. The emotion engine performs natural language processing and data analysis to quantify and diagnose the user's emotional state. The output is an evaluation report regarding the user's emotional state.

[0329] Step 4:

[0330] The server customizes the notification content based on the analysis results and sentiment evaluation from the previous step. The inputs are crop status evaluation information and the user's sentiment evaluation report. The server uses a generative AI model to generate friendly notification messages and advice tailored to the sentiment. The output is the optimized notification message.

[0331] Step 5:

[0332] The terminal displays notification messages received from the server to the user via a smartphone or tablet application. The input data is the notification message sent from the server. The output is an information notification screen that the user can view through the terminal. This allows the user to receive information necessary for efficient farm management in real time.

[0333] Step 6:

[0334] Users input feedback on notifications into a terminal application and send it to the server. The input data is user feedback information regarding the notification. The server receives this feedback and uses it to fine-tune the intelligent algorithms and emotion engine. The output is the improved algorithm settings. This allows the system to continuously improve and provide more accurate information.

[0335] (Application Example 2)

[0336] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0337] In factory and other work environments, providing information without considering the emotional state of workers can negatively impact work efficiency and safety. In particular, uniform notifications and information provision to workers experiencing stress or fatigue can actually increase their workload; therefore, a flexible approach tailored to the emotional state of workers is required.

[0338] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0339] In this invention, the server includes means for collecting environmental information, means equipped with a machine learning algorithm for analyzing the collected information, means for generating warnings based on the analysis results and providing information to the user, and means for customizing the information according to the user's emotional state using an emotion recognition engine. This enables flexible information provision according to the emotional state of the worker.

[0340] "Means of collecting environmental information" refers to devices and mechanisms that use sensors to acquire various data such as temperature, humidity, light intensity, and sound from specific environments such as factories and farms.

[0341] A "machine learning algorithm" is a type of artificial intelligence used to identify patterns based on collected data and perform analysis and predictions. It is a technology that can automatically improve its accuracy based on past data.

[0342] "Means for generating warnings and providing information to users" refers to a system that uses various forms of notification, such as visuals and audio, to alert or instruct users based on data analysis results.

[0343] An "emotion recognition engine" is software or algorithms that determine a user's emotions and psychological state from their biometric data and behavioral data, and then provide appropriate responses and information based on that state.

[0344] The system implementing this invention aims to recognize the emotions of workers in a factory and provide them with appropriate information. The system mainly consists of a server, peripheral terminals, and users.

[0345] The server collects data from sensors installed throughout the factory to gather environmental information. This information includes temperature, humidity, noise levels, and more. The collected data is transmitted to the server via high-speed data communication.

[0346] The transmitted data is analyzed on the server using machine learning algorithms that leverage programming languages ​​such as Python. Machine learning frameworks like TensorFlow and PyTorch are used for this analysis. The analysis results serve as the basis for generating subsequent notifications.

[0347] Simultaneously, an emotion recognition engine uses the worker's biometric data (e.g., heart rate data obtained from a smartwatch) to determine their current emotional state. This engine optimizes the method of information transmission based on the user's psychological state.

[0348] For example, if a worker is feeling stressed, the terminal will display a message such as "Let's take a short break" in a visually and audibly appealing format, based on analysis results from the server. This allows workers to continue their work safely and efficiently while reducing their burden.

[0349] An example of a prompt message to be fed into a generative AI model is: "Based on data received from a wearable device, analyze the worker's current emotional state and provide a stress-reducing message at the appropriate time."

[0350] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0351] Step 1:

[0352] The terminal collects environmental information from sensors within the factory. Input data includes temperature, humidity, and noise levels. The terminal temporarily stores this data and transmits it to the server using high-speed data communication.

[0353] Step 2:

[0354] The server uses machine learning algorithms to analyze the received environmental data. TensorFlow and PyTorch are used for pattern recognition and anomaly detection. The analysis output provides indicators of the working environment's state and potential risks.

[0355] Step 3:

[0356] Simultaneously, the server receives biometric data from the worker's smartwatch. This input includes heart rate and body movements. The server uses an emotion recognition engine to estimate the worker's emotional state from the input data. The output is an evaluation of the emotional state at that moment.

[0357] Step 4:

[0358] The server integrates the results of environmental data analysis and emotion recognition evaluation to generate appropriate notifications. This notification generation process includes evaluating data correlations and determining optimized messages to reduce the mental burden on workers.

[0359] Step 5:

[0360] The terminal receives notifications from the server and provides them to the worker in an appropriate manner. Using visual displays and audio guidance, it presents messages prompting the worker to take specific actions, such as "take a break." This allows the worker to receive feedback tailored to their own situation.

[0361] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0362] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0363] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0364] [Third Embodiment]

[0365] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0366] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0367] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0368] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0369] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0370] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0371] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0372] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0373] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0374] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0375] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0376] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0377] As an embodiment of the present invention, a system is described in which IoT sensors are used to collect data from the agricultural environment, and a server analyzes the collected data. A terminal communicates with the server, provides the analysis results to the user, and the user makes decisions regarding agricultural management based on that information.

[0378] First, the terminal collects environmental data such as temperature, humidity, soil pH, and sunlight from IoT sensors installed on the farm. This data is transmitted to a server in real time using 5G communication. The server uses artificial intelligence algorithms to analyze the received data and evaluate the growth status of crops and the risk of pests and diseases.

[0379] The analysis results are generated as notifications to the user regarding the optimal harvesting time and necessary agricultural tasks. The device displays these notifications to the user and provides specific advice to improve the efficiency of agricultural work. When the user performs tasks based on the notifications, the feedback is sent from the device to the server and used to improve the performance of the AI ​​algorithm.

[0380] As a concrete example, let's say a farm is growing tomatoes. The terminal acquires humidity and temperature data from sensors and sends it to a server. The server determines that the humidity is very high and the risk of disease is increasing. This information is notified to the user, who can quickly take action such as activating a dehumidifying fan or applying fungicide. Through such actions, efficient farm management becomes possible while maintaining the health of the crops.

[0381] The following describes the processing flow.

[0382] Step 1:

[0383] The terminal periodically acquires environmental data such as temperature, humidity, soil pH, and sunlight from IoT sensors installed on the farm. The acquired data is transmitted to a server using 5G communication.

[0384] Step 2:

[0385] The server executes an artificial intelligence algorithm based on the received environmental data. Through data analysis, it evaluates crop growth status and the risk of pest and disease outbreaks. The analysis results are recorded in a database to prepare for subsequent information provision.

[0386] Step 3:

[0387] Based on the analysis results, the server generates alerts and advice regarding agricultural practices. This includes, for example, early detection of pests and diseases, the amount of water needed, and the appropriate timing for fertilizer application.

[0388] Step 4:

[0389] The terminal receives notifications from the server and presents the information to the user visually. The notifications include concise instructions and detailed analysis results regarding agricultural management.

[0390] Step 5:

[0391] Users check notifications from their devices and perform necessary farming tasks. For example, they might activate a dehumidifying fan or spray mold inhibitor based on the notification.

[0392] Step 6:

[0393] The device collects feedback about the user's actions and sends it back to the server. This feedback includes information such as the content and timing of the actions.

[0394] Step 7:

[0395] The server receives feedback and fine-tunes its artificial intelligence algorithms. This improves the accuracy of subsequent analyses and notifications. This process is repeated to continuously support the efficiency of agriculture.

[0396] (Example 1)

[0397] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0398] In agriculture, environmental factors significantly impact crop growth and health, making it essential to collect and analyze environmental data in real time to implement appropriate agricultural management. However, conventional systems have faced challenges such as delays in data collection and analysis, and difficulty in effectively improving AI algorithms through feedback.

[0399] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0400] In this invention, the server includes means for collecting various environmental parameters from the agricultural environment, means for analyzing the collected data using an artificial intelligence algorithm to evaluate the health and risks of crops, means for generating farm work guidelines as notifications based on the analysis results and providing information to the user via a terminal, and means for receiving user work feedback sent from the terminal and improving the performance of the AI ​​algorithm. This enables highly accurate agricultural management through real-time data processing and effective improvement of the AI.

[0401] "Environmental parameters" are numerical information that directly affects crop growth conditions in an agricultural environment, such as temperature, humidity, soil pH, and sunlight.

[0402] An "artificial intelligence algorithm" is a procedure or method that uses machine learning and data analysis techniques to analyze collected data and automatically assess the health and risks of crops.

[0403] "Analysis results" refer to information about conclusions and recommended actions obtained by artificial intelligence algorithms, and are provided to users as notifications.

[0404] A "notification" is information generated based on analysis results, and it is a message that provides users with guidance and warnings for agricultural management.

[0405] "Feedback" refers to data about the farming tasks actually performed by users and the results thereof, and is used to improve the performance of AI algorithms.

[0406] "Mobile communication devices" are digital devices that users can carry with them, such as smartphones and tablets, and are used to receive notifications.

[0407] This invention is a system that combines IoT sensors and advanced data analysis technology in an agricultural environment to achieve efficient management of crops.

[0408] The terminal continuously collects environmental parameters such as temperature, humidity, soil pH, and sunlight using various sensors installed on the farm. This system can, for example, measure temperature and humidity using a DHT22 sensor and evaluate soil moisture content using a YL-69 sensor.

[0409] The terminal transmits collected environmental data to the server via high-speed wireless communication. The server receives this data in real time and analyzes it using artificial intelligence algorithms. The algorithms operate using AI frameworks such as TensorFlow and PyTorch, and evaluate the health of crops and the risk of pests and diseases from the data. Predictions are made during the analysis using machine learning models based on historical data.

[0410] The server generates agricultural management guidelines as notifications based on the analysis results and provides them to users via their devices. These notifications are displayed on mobile communication devices such as smartphones and tablets, helping users to properly plan and carry out their farm work.

[0411] Users perform farming tasks based on the notifications and send the results and feedback to the server via their devices. This allows the server to continuously adjust the AI ​​algorithms and improve the overall accuracy of the system.

[0412] As a concrete example, in a tomato farm, if humidity rises and the risk of disease increases, the server analyzes this information and sends a notification to the user instructing them to activate a dehumidifying fan. By following this instruction and performing dehumidification, the user can maintain the health of the crops while performing efficient farm work.

[0413] Example prompt: "Analyze data from farm sensors and assess crop health. If there are abnormalities in humidity and temperature, suggest a way to notify the user of appropriate actions."

[0414] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0415] Step 1:

[0416] The terminal acquires environmental parameters from IoT sensors installed on the farm. Specifically, the terminal collects temperature and humidity data from the DHT22 sensor and soil moisture data from the YL-69 sensor. This data is acquired as real-time, accurate input information that reflects the environmental conditions and is ready to be transmitted to the server.

[0417] Step 2:

[0418] The terminal transmits the collected environmental data to the server using 5G communication. This transmission process requires data to be sent without delay, allowing the server to begin analysis immediately. The information received by the server becomes output data in the form of real-time environmental parameters.

[0419] Step 3:

[0420] The server records the received environmental data in a database and performs analysis using artificial intelligence algorithms. Here, TensorFlow or PyTorch is used to perform data calculations to predict crop health and pest / disease risks from the data. The output derived from the data input is a risk assessment and recommended actions based on the analysis results.

[0421] Step 4:

[0422] The server uses the analysis results to generate notifications that provide guidance for agricultural work. These notifications include appropriate actions and countermeasures and are provided to the user via the terminal. The generated notifications output guidelines that serve as actual actions for the user.

[0423] Step 5:

[0424] Users check notifications sent via their devices and perform farm work based on the instructions. Specifically, based on the notification, users might activate a dehumidifying fan or spray fungicide on the farm. The user's actions serve as input for feedback to the system, recording the completed work.

[0425] Step 6:

[0426] Feedback from user actions is sent to the server via the terminal. The server uses this feedback to fine-tune the artificial intelligence algorithm and improve analysis accuracy. The output derived from the feedback results in more accurate risk predictions and recommended actions for future use.

[0427] (Application Example 1)

[0428] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0429] In factories and other facilities, optimizing equipment operation and maintenance schedules is crucial. However, traditionally, understanding equipment operation and streamlining preventative maintenance have been challenging. Furthermore, equipment failures can lead to decreased production efficiency and wasted energy, necessitating real-time monitoring and rapid response.

[0430] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0431] In this invention, the server includes a device for acquiring environmental data, a device equipped with a machine learning algorithm for analyzing the acquired information, and a device for monitoring the operating patterns of equipment and performing maintenance improvements. This enables efficient monitoring of the operating status of factory equipment and optimization of preventive maintenance.

[0432] "Environmental data" refers to information that indicates environmental conditions such as temperature, humidity, and light intensity, and is important information related to the operation of the equipment.

[0433] A "data acquisition device" refers to equipment that collects environmental data using sensors and other means, and transmits that data to a server.

[0434] A "machine learning algorithm for analysis" is a computational method for analyzing large amounts of data and finding meaningful patterns within it.

[0435] A "device for monitoring equipment operating patterns" is a device that continuously monitors the operating status of equipment and understands trends in its operation and performance.

[0436] A "device for maintenance improvement" is a device that develops an appropriate maintenance plan for equipment based on the analysis results of monitored operating patterns, thereby improving operational efficiency.

[0437] To implement this invention, first, environmental data such as temperature, humidity, and light intensity are acquired in the factory environment using IoT sensors. The acquired data is transmitted to a server in real time using the MQTT protocol. The server analyzes the data using machine learning algorithms with the TensorFlow library. Based on the analysis results, the operating patterns of the equipment are evaluated, and maintenance needs and an optimized operating schedule are generated. The results are notified to the user's smartphone or tablet via a user interface using React Native or Flutter. The user then takes appropriate maintenance actions based on the received information.

[0438] As a concrete example, when a sensor detects that a cooling system in a factory is overheating, the server predicts a malfunction and immediately notifies the user that maintenance is required. The user can check the notification on a smartphone app and schedule the cleaning of the cooling system's filters. This enables preventative maintenance and efficient factory operations.

[0439] An example of an input prompt for the generated AI model is: "Analyze the temperature data of factory equipment and propose an optimized approach for preventive maintenance in case of anomalies."

[0440] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0441] Step 1:

[0442] The terminal acquires environmental data such as temperature, humidity, and light intensity through IoT sensors within the factory. This data is acquired using a sensor interface, and initial data format conversion is performed on the terminal. The output is ready data for transmission to the server.

[0443] Step 2:

[0444] The terminal sends prepared environment data to the server using the MQTT protocol. It receives formatted data as input and relays that data to the server in real time. The output of this step is the data stream received by the server.

[0445] Step 3:

[0446] The server analyzes the received data stream using a machine learning algorithm based on TensorFlow. It takes the received data as input, performs data cleansing and transformation, and formats it suitable for analysis. As a result, it outputs the results of the equipment operation pattern analysis.

[0447] Step 4:

[0448] Based on the analysis results, the server uses a generating AI model to generate instructions for preventative maintenance and optimization of the operating schedule. In this step, the analysis results are taken as input, and the recommended actions from the generating AI are obtained as output. The output is a message containing the instructions.

[0449] Step 5:

[0450] The user's device receives instructions sent from the server and displays notifications through an application built with React Native or Flutter. It receives the instruction message as input and outputs that message on the screen in a user-friendly format.

[0451] Step 6:

[0452] The user performs actual maintenance and adjustment work based on the information they receive. Specifically, the user refers to the app to perform tasks such as cleaning the cooling system filters, and sends the details of these actions to the server as feedback through the app. This feedback data becomes the output.

[0453] Step 7:

[0454] The server receives feedback data from users and incorporates it into a dataset to fine-tune the machine learning algorithm. This improves the accuracy of the data algorithm and is used as input for new analysis processes.

[0455] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0456] In embodiments for carrying out the present invention, a system incorporating an emotion engine for recognizing user emotions and providing users with information useful for agricultural management is described. This system not only collects environmental data and provides analysis results, but also has the feature of customizing information based on the user's emotional state using the emotion engine.

[0457] First, the terminal collects environmental data from IoT sensors installed on the farm and transmits it to the server via high-speed data communication. The server analyzes the received data using an artificial intelligence algorithm to assess the health of the crops and the risk of pests and diseases. When generating notifications based on these analysis results, the emotion engine analyzes the user's emotional data and customizes the notification content and how it is delivered. For example, if it is determined that the user is feeling stressed, the notification will be made more friendly and will include advice to reduce stress.

[0458] As a concrete example, suppose a user is managing a tomato field. Environmental data is collected as usual, and the analysis results indicate a high risk of pest and disease outbreaks. On the other hand, the emotion engine determines that the user's emotional state is stressful, so in addition to the usual notification, the device presents the user with a message such as, "Take a break and relax." In this way, providing information according to emotions can reduce the user's psychological burden and support efficient agricultural management.

[0459] User feedback is collected by the terminal and sent to the server. The server uses this feedback to fine-tune the algorithm and further improve the quality of information provided. Through this continuous process, the present invention achieves improved efficiency in agricultural management.

[0460] The following describes the processing flow.

[0461] Step 1:

[0462] The terminal acquires environmental data such as temperature, humidity, soil pH, and sunlight from IoT sensors installed on the farm. This data is transmitted to the server in real time using 5G communication.

[0463] Step 2:

[0464] The server analyzes the received environmental data using artificial intelligence algorithms. Through this analysis, it assesses the health of the crops and the risk of pest and disease outbreaks, and stores the results in a database.

[0465] Step 3:

[0466] The server collects user emotion data using an emotion engine. This data is obtained, for example, from emotion analysis based on user facial recognition and analysis of voice tone.

[0467] Step 4:

[0468] The server generates optimized notifications based on analyzed environmental and sentiment data. These notifications may include specific farming task suggestions or encouraging messages tailored to the user's emotional state.

[0469] Step 5:

[0470] The device displays notifications sent from the server to the user. The notification content is adjusted according to the user's emotional state, thus reducing the user's psychological burden.

[0471] Step 6:

[0472] Users perform agricultural tasks based on notifications from their devices. User feedback is sent to the server via the device. This feedback includes information such as what tasks were performed and whether they were successful or not.

[0473] Step 7:

[0474] The server analyzes user feedback and fine-tunes its artificial intelligence algorithms. This improves the accuracy of subsequent data analysis and notification generation, thereby enhancing the overall usefulness of the system.

[0475] (Example 2)

[0476] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0477] Traditional agricultural management systems provide notifications based on the analysis of environmental information, but they fail to consider the user's emotions and psychological state, resulting in a lack of useful information for users experiencing stress. Furthermore, they are insufficient in terms of real-time data communication and continuous improvement based on user feedback.

[0478] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0479] In this invention, the server includes means for collecting environmental information, means equipped with an intelligent algorithm for analyzing the collected information, and means for customizing notification content with an emotion engine that evaluates the user's emotional state. This makes it possible to provide information that takes into account the user's psychological state. Furthermore, by further including high-speed information communication means for immediate information exchange and means for receiving evaluation data from the user and fine-tuning the intelligent algorithm, the quality of information provision can be continuously improved.

[0480] "Environmental information" is a general term for information related to crop growth and health in agricultural settings, such as temperature, humidity, soil pH, and moisture content.

[0481] An "intelligent algorithm" is an algorithm used for data analysis, utilizing machine learning and artificial intelligence technologies to extract useful information and patterns from data.

[0482] "Users" refer to agricultural workers and managers who use this system and are the entities that make decisions based on the information provided.

[0483] An "emotional engine" is an engine for analyzing and evaluating a user's emotional state, using technologies such as natural language processing and vital sign analysis to quantify stress, fatigue, and other emotional states.

[0484] "Customizing notification content" refers to the process of adjusting the format and expression of the information provided according to the user's emotional state.

[0485] Embodiments of the present invention relate to a management system in the agricultural field. Specific implementation methods are described below.

[0486] The system primarily consists of terminals, servers, and users. The terminals are installed on farms and collect environmental information using various sensors. These sensors include temperature sensors to measure air temperature, humidity sensors to detect humidity, and soil sensors to measure soil pH and moisture content. This data is transmitted to the server using network technologies such as Wi-Fi and LoRa.

[0487] The server receives transmitted environmental information and analyzes the data using intelligent algorithms. Specifically, it utilizes software such as Python and TensorFlow, and uses machine learning models to assess crop health and pest / disease risk. Simultaneously, the server uses an emotion engine to analyze vital data and user input information to assess the user's emotional state. This analysis also utilizes natural language processing techniques to quantify the user's stress and fatigue levels.

[0488] Based on the analysis results, the server generates notifications for the user and customizes them according to the emotion engine's analysis. For example, if the user is feeling stressed, the server will make the notification more user-friendly and add encouraging messages. For example, for a user managing crops while traveling, a possible notification might be, "The risk of pests and diseases is high, so pay attention to your crops. Take a break and refresh yourself."

[0489] Users can view notifications sent from the server via their devices. These notifications are displayed through a dedicated application on smartphones and tablets, supporting the user's actions. User feedback is sent to the server via the device. This feedback information is used by the server to continuously refine its intelligent algorithms and emotion engine, improving the accuracy of information delivery.

[0490] Examples of prompts for a generative AI model include the following:

[0491] "Based on the management status of the tomato field, please suggest what kind of notification should be sent if a user is experiencing stress."

[0492] Through this mechanism, the present invention enables effective information provision that takes into account the user's psychological state, thereby supporting improved efficiency in agricultural management.

[0493] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0494] Step 1:

[0495] The terminal collects environmental information using various sensors installed on the farm. Specifically, a temperature sensor measures the temperature, a humidity sensor measures the humidity, and a soil sensor measures the pH value and moisture content. This environmental information is obtained as input data. The terminal packages this data and prepares it for transmission to the server. The output is the data package of environmental information that is sent to the server.

[0496] Step 2:

[0497] The server receives environmental information sent from the terminal. The input data is a data package containing various environmental factors of the farm. The server analyzes this data using intelligent algorithms. For example, it uses Python and TensorFlow to run machine learning models and perform crop health assessments and predict pest and disease risks. The output is the crop status and risk assessment information as a result of the analysis.

[0498] Step 3:

[0499] The server receives vital data and user input transmitted from the terminal to evaluate the user's emotional state. The input data consists of individual emotional indicators such as heart rate and stress level. The emotion engine performs natural language processing and data analysis to quantify and diagnose the user's emotional state. The output is an evaluation report regarding the user's emotional state.

[0500] Step 4:

[0501] The server customizes the notification content based on the analysis results and sentiment evaluation from the previous step. The inputs are crop status evaluation information and the user's sentiment evaluation report. The server uses a generative AI model to generate friendly notification messages and advice tailored to the sentiment. The output is the optimized notification message.

[0502] Step 5:

[0503] The terminal displays notification messages received from the server to the user via a smartphone or tablet application. The input data is the notification message sent from the server. The output is an information notification screen that the user can view through the terminal. This allows the user to receive information necessary for efficient farm management in real time.

[0504] Step 6:

[0505] Users input feedback on notifications into a terminal application and send it to the server. The input data is user feedback information regarding the notification. The server receives this feedback and uses it to fine-tune the intelligent algorithms and emotion engine. The output is the improved algorithm settings. This allows the system to continuously improve and provide more accurate information.

[0506] (Application Example 2)

[0507] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0508] In factory and other work environments, providing information without considering the emotional state of workers can negatively impact work efficiency and safety. In particular, uniform notifications and information provision to workers experiencing stress or fatigue can actually increase their workload; therefore, a flexible approach tailored to the emotional state of workers is required.

[0509] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0510] In this invention, the server includes means for collecting environmental information, means equipped with a machine learning algorithm for analyzing the collected information, means for generating warnings based on the analysis results and providing information to the user, and means for customizing the information according to the user's emotional state using an emotion recognition engine. This enables flexible information provision according to the emotional state of the worker.

[0511] "Means of collecting environmental information" refers to devices and mechanisms that use sensors to acquire various data such as temperature, humidity, light intensity, and sound from specific environments such as factories and farms.

[0512] A "machine learning algorithm" is a type of artificial intelligence used to identify patterns based on collected data and perform analysis and predictions. It is a technology that can automatically improve its accuracy based on past data.

[0513] "Means for generating warnings and providing information to users" refers to a system that uses various forms of notification, such as visuals and audio, to alert or instruct users based on data analysis results.

[0514] An "emotion recognition engine" is software or algorithms that determine a user's emotions and psychological state from their biometric data and behavioral data, and then provide appropriate responses and information based on that state.

[0515] The system implementing this invention aims to recognize the emotions of workers in a factory and provide them with appropriate information. The system mainly consists of a server, peripheral terminals, and users.

[0516] The server collects data from sensors installed throughout the factory to gather environmental information. This information includes temperature, humidity, noise levels, and more. The collected data is transmitted to the server via high-speed data communication.

[0517] The transmitted data is analyzed on the server using machine learning algorithms that leverage programming languages ​​such as Python. Machine learning frameworks like TensorFlow and PyTorch are used for this analysis. The analysis results serve as the basis for generating subsequent notifications.

[0518] Simultaneously, an emotion recognition engine uses the worker's biometric data (e.g., heart rate data obtained from a smartwatch) to determine their current emotional state. This engine optimizes the method of information transmission based on the user's psychological state.

[0519] For example, if a worker is feeling stressed, the terminal will display a message such as "Let's take a short break" in a visually and audibly appealing format, based on analysis results from the server. This allows workers to continue their work safely and efficiently while reducing their burden.

[0520] An example of a prompt message to be fed into a generative AI model is: "Based on data received from a wearable device, analyze the worker's current emotional state and provide a stress-reducing message at the appropriate time."

[0521] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0522] Step 1:

[0523] The terminal collects environmental information from sensors within the factory. Input data includes temperature, humidity, and noise levels. The terminal temporarily stores this data and transmits it to the server using high-speed data communication.

[0524] Step 2:

[0525] The server uses machine learning algorithms to analyze the received environmental data. TensorFlow and PyTorch are used for pattern recognition and anomaly detection. The analysis output provides indicators of the working environment's state and potential risks.

[0526] Step 3:

[0527] Simultaneously, the server receives biometric data from the worker's smartwatch. This input includes heart rate and body movements. The server uses an emotion recognition engine to estimate the worker's emotional state from the input data. The output is an evaluation of the emotional state at that moment.

[0528] Step 4:

[0529] The server integrates the results of environmental data analysis and emotion recognition evaluation to generate appropriate notifications. This notification generation process includes evaluating data correlations and determining optimized messages to reduce the mental burden on workers.

[0530] Step 5:

[0531] The terminal receives notifications from the server and provides them to the worker in an appropriate manner. Using visual displays and audio guidance, it presents messages prompting the worker to take specific actions, such as "take a break." This allows the worker to receive feedback tailored to their own situation.

[0532] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0533] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0534] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0535] [Fourth Embodiment]

[0536] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0537] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0538] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0539] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0540] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0541] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0542] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0543] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0544] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0545] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0546] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0547] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0548] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0549] As an embodiment of the present invention, a system is described in which IoT sensors are used to collect data from the agricultural environment, and a server analyzes the collected data. A terminal communicates with the server, provides the analysis results to the user, and the user makes decisions regarding agricultural management based on that information.

[0550] First, the terminal collects environmental data such as temperature, humidity, soil pH, and sunlight from IoT sensors installed on the farm. This data is transmitted to a server in real time using 5G communication. The server uses artificial intelligence algorithms to analyze the received data and evaluate the growth status of crops and the risk of pests and diseases.

[0551] The analysis results are generated as notifications to the user regarding the optimal harvesting time and necessary agricultural tasks. The device displays these notifications to the user and provides specific advice to improve the efficiency of agricultural work. When the user performs tasks based on the notifications, the feedback is sent from the device to the server and used to improve the performance of the AI ​​algorithm.

[0552] As a concrete example, let's say a farm is growing tomatoes. The terminal acquires humidity and temperature data from sensors and sends it to a server. The server determines that the humidity is very high and the risk of disease is increasing. This information is notified to the user, who can quickly take action such as activating a dehumidifying fan or applying fungicide. Through such actions, efficient farm management becomes possible while maintaining the health of the crops.

[0553] The following describes the processing flow.

[0554] Step 1:

[0555] The terminal periodically acquires environmental data such as temperature, humidity, soil pH, and sunlight from IoT sensors installed on the farm. The acquired data is transmitted to a server using 5G communication.

[0556] Step 2:

[0557] The server executes an artificial intelligence algorithm based on the received environmental data. Through data analysis, it evaluates crop growth status and the risk of pest and disease outbreaks. The analysis results are recorded in a database to prepare for subsequent information provision.

[0558] Step 3:

[0559] Based on the analysis results, the server generates alerts and advice regarding agricultural practices. This includes, for example, early detection of pests and diseases, the amount of water needed, and the appropriate timing for fertilizer application.

[0560] Step 4:

[0561] The terminal receives notifications from the server and presents the information to the user visually. The notifications include concise instructions and detailed analysis results regarding agricultural management.

[0562] Step 5:

[0563] Users check notifications from their devices and perform necessary farming tasks. For example, they might activate a dehumidifying fan or spray mold inhibitor based on the notification.

[0564] Step 6:

[0565] The device collects feedback about the user's actions and sends it back to the server. This feedback includes information such as the content and timing of the actions.

[0566] Step 7:

[0567] The server receives feedback and fine-tunes its artificial intelligence algorithms. This improves the accuracy of subsequent analyses and notifications. This process is repeated to continuously support the efficiency of agriculture.

[0568] (Example 1)

[0569] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0570] In agriculture, environmental factors significantly impact crop growth and health, making it essential to collect and analyze environmental data in real time to implement appropriate agricultural management. However, conventional systems have faced challenges such as delays in data collection and analysis, and difficulty in effectively improving AI algorithms through feedback.

[0571] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0572] In this invention, the server includes means for collecting various environmental parameters from the agricultural environment, means for analyzing the collected data using an artificial intelligence algorithm to evaluate the health and risks of crops, means for generating farm work guidelines as notifications based on the analysis results and providing information to the user via a terminal, and means for receiving user work feedback sent from the terminal and improving the performance of the AI ​​algorithm. This enables highly accurate agricultural management through real-time data processing and effective improvement of the AI.

[0573] "Environmental parameters" are numerical information that directly affects crop growth conditions in an agricultural environment, such as temperature, humidity, soil pH, and sunlight.

[0574] An "artificial intelligence algorithm" is a procedure or method that uses machine learning and data analysis techniques to analyze collected data and automatically assess the health and risks of crops.

[0575] "Analysis results" refer to information about conclusions and recommended actions obtained by artificial intelligence algorithms, and are provided to users as notifications.

[0576] A "notification" is information generated based on analysis results, and it is a message that provides users with guidance and warnings for agricultural management.

[0577] "Feedback" refers to data about the farming tasks actually performed by users and the results thereof, and is used to improve the performance of AI algorithms.

[0578] "Mobile communication devices" are digital devices that users can carry with them, such as smartphones and tablets, and are used to receive notifications.

[0579] This invention is a system that combines IoT sensors and advanced data analysis technology in an agricultural environment to achieve efficient management of crops.

[0580] The terminal continuously collects environmental parameters such as temperature, humidity, soil pH, and sunlight using various sensors installed on the farm. This system can, for example, measure temperature and humidity using a DHT22 sensor and evaluate soil moisture content using a YL-69 sensor.

[0581] The terminal transmits collected environmental data to the server via high-speed wireless communication. The server receives this data in real time and analyzes it using artificial intelligence algorithms. The algorithms operate using AI frameworks such as TensorFlow and PyTorch, and evaluate the health of crops and the risk of pests and diseases from the data. Predictions are made during the analysis using machine learning models based on historical data.

[0582] The server generates agricultural management guidelines as notifications based on the analysis results and provides them to users via their devices. These notifications are displayed on mobile communication devices such as smartphones and tablets, helping users to properly plan and carry out their farm work.

[0583] Users perform farming tasks based on the notifications and send the results and feedback to the server via their devices. This allows the server to continuously adjust the AI ​​algorithms and improve the overall accuracy of the system.

[0584] As a concrete example, in a tomato farm, if humidity rises and the risk of disease increases, the server analyzes this information and sends a notification to the user instructing them to activate a dehumidifying fan. By following this instruction and performing dehumidification, the user can maintain the health of the crops while performing efficient farm work.

[0585] Example prompt: "Analyze data from farm sensors and assess crop health. If there are abnormalities in humidity and temperature, suggest a way to notify the user of appropriate actions."

[0586] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0587] Step 1:

[0588] The terminal acquires environmental parameters from IoT sensors installed on the farm. Specifically, the terminal collects temperature and humidity data from the DHT22 sensor and soil moisture data from the YL-69 sensor. This data is acquired as real-time, accurate input information that reflects the environmental conditions and is ready to be transmitted to the server.

[0589] Step 2:

[0590] The terminal transmits the collected environmental data to the server using 5G communication. This transmission process requires data to be sent without delay, allowing the server to begin analysis immediately. The information received by the server becomes output data in the form of real-time environmental parameters.

[0591] Step 3:

[0592] The server records the received environmental data in a database and performs analysis using artificial intelligence algorithms. Here, TensorFlow or PyTorch is used to perform data calculations to predict crop health and pest / disease risks from the data. The output derived from the data input is a risk assessment and recommended actions based on the analysis results.

[0593] Step 4:

[0594] The server uses the analysis results to generate notifications that provide guidance for agricultural work. These notifications include appropriate actions and countermeasures and are provided to the user via the terminal. The generated notifications output guidelines that serve as actual actions for the user.

[0595] Step 5:

[0596] Users check notifications sent via their devices and perform farm work based on the instructions. Specifically, based on the notification, users might activate a dehumidifying fan or spray fungicide on the farm. The user's actions serve as input for feedback to the system, recording the completed work.

[0597] Step 6:

[0598] Feedback from user actions is sent to the server via the terminal. The server uses this feedback to fine-tune the artificial intelligence algorithm and improve analysis accuracy. The output derived from the feedback results in more accurate risk predictions and recommended actions for future use.

[0599] (Application Example 1)

[0600] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0601] In factories and other facilities, optimizing equipment operation and maintenance schedules is crucial. However, traditionally, understanding equipment operation and streamlining preventative maintenance have been challenging. Furthermore, equipment failures can lead to decreased production efficiency and wasted energy, necessitating real-time monitoring and rapid response.

[0602] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0603] In this invention, the server includes a device for acquiring environmental data, a device equipped with a machine learning algorithm for analyzing the acquired information, and a device for monitoring the operating patterns of equipment and performing maintenance improvements. This enables efficient monitoring of the operating status of factory equipment and optimization of preventive maintenance.

[0604] "Environmental data" refers to information that indicates environmental conditions such as temperature, humidity, and light intensity, and is important information related to the operation of the equipment.

[0605] A "data acquisition device" refers to equipment that collects environmental data using sensors and other means, and transmits that data to a server.

[0606] A "machine learning algorithm for analysis" is a computational method for analyzing large amounts of data and finding meaningful patterns within it.

[0607] A "device for monitoring equipment operating patterns" is a device that continuously monitors the operating status of equipment and understands trends in its operation and performance.

[0608] A "device for maintenance improvement" is a device that develops an appropriate maintenance plan for equipment based on the analysis results of monitored operating patterns, thereby improving operational efficiency.

[0609] To implement this invention, first, environmental data such as temperature, humidity, and light intensity are acquired in the factory environment using IoT sensors. The acquired data is transmitted to a server in real time using the MQTT protocol. The server analyzes the data using machine learning algorithms with the TensorFlow library. Based on the analysis results, the operating patterns of the equipment are evaluated, and maintenance needs and an optimized operating schedule are generated. The results are notified to the user's smartphone or tablet via a user interface using React Native or Flutter. The user then takes appropriate maintenance actions based on the received information.

[0610] As a concrete example, when a sensor detects that a cooling system in a factory is overheating, the server predicts a malfunction and immediately notifies the user that maintenance is required. The user can check the notification on a smartphone app and schedule the cleaning of the cooling system's filters. This enables preventative maintenance and efficient factory operations.

[0611] An example of an input prompt for the generated AI model is: "Analyze the temperature data of factory equipment and propose an optimized approach for preventive maintenance in case of anomalies."

[0612] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0613] Step 1:

[0614] The terminal acquires environmental data such as temperature, humidity, and light intensity through IoT sensors within the factory. This data is acquired using a sensor interface, and initial data format conversion is performed on the terminal. The output is ready data for transmission to the server.

[0615] Step 2:

[0616] The terminal sends prepared environment data to the server using the MQTT protocol. It receives formatted data as input and relays that data to the server in real time. The output of this step is the data stream received by the server.

[0617] Step 3:

[0618] The server analyzes the received data stream using a machine learning algorithm based on TensorFlow. It takes the received data as input, performs data cleansing and transformation, and formats it suitable for analysis. As a result, it outputs the results of the equipment operation pattern analysis.

[0619] Step 4:

[0620] Based on the analysis results, the server uses a generating AI model to generate instructions for preventative maintenance and optimization of the operating schedule. In this step, the analysis results are taken as input, and the recommended actions from the generating AI are obtained as output. The output is a message containing the instructions.

[0621] Step 5:

[0622] The user's device receives instructions sent from the server and displays notifications through an application built with React Native or Flutter. It receives the instruction message as input and outputs that message on the screen in a user-friendly format.

[0623] Step 6:

[0624] The user performs actual maintenance and adjustment work based on the information they receive. Specifically, the user refers to the app to perform tasks such as cleaning the cooling system filters, and sends the details of these actions to the server as feedback through the app. This feedback data becomes the output.

[0625] Step 7:

[0626] The server receives feedback data from users and incorporates it into a dataset to fine-tune the machine learning algorithm. This improves the accuracy of the data algorithm and is used as input for new analysis processes.

[0627] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0628] In embodiments for carrying out the present invention, a system incorporating an emotion engine for recognizing user emotions and providing users with information useful for agricultural management is described. This system not only collects environmental data and provides analysis results, but also has the feature of customizing information based on the user's emotional state using the emotion engine.

[0629] First, the terminal collects environmental data from IoT sensors installed on the farm and transmits it to the server via high-speed data communication. The server analyzes the received data using an artificial intelligence algorithm to assess the health of the crops and the risk of pests and diseases. When generating notifications based on these analysis results, the emotion engine analyzes the user's emotional data and customizes the notification content and how it is delivered. For example, if it is determined that the user is feeling stressed, the notification will be made more friendly and will include advice to reduce stress.

[0630] As a concrete example, suppose a user is managing a tomato field. Environmental data is collected as usual, and the analysis results indicate a high risk of pest and disease outbreaks. On the other hand, the emotion engine determines that the user's emotional state is stressful, so in addition to the usual notification, the device presents the user with a message such as, "Take a break and relax." In this way, providing information according to emotions can reduce the user's psychological burden and support efficient agricultural management.

[0631] User feedback is collected by the terminal and sent to the server. The server uses this feedback to fine-tune the algorithm and further improve the quality of information provided. Through this continuous process, the present invention achieves improved efficiency in agricultural management.

[0632] The following describes the processing flow.

[0633] Step 1:

[0634] The terminal acquires environmental data such as temperature, humidity, soil pH, and sunlight from IoT sensors installed on the farm. This data is transmitted to the server in real time using 5G communication.

[0635] Step 2:

[0636] The server analyzes the received environmental data using artificial intelligence algorithms. Through this analysis, it assesses the health of the crops and the risk of pest and disease outbreaks, and stores the results in a database.

[0637] Step 3:

[0638] The server collects user emotion data using an emotion engine. This data is obtained, for example, from emotion analysis based on user facial recognition and analysis of voice tone.

[0639] Step 4:

[0640] The server generates optimized notifications based on analyzed environmental and sentiment data. These notifications may include specific farming task suggestions or encouraging messages tailored to the user's emotional state.

[0641] Step 5:

[0642] The device displays notifications sent from the server to the user. The notification content is adjusted according to the user's emotional state, thus reducing the user's psychological burden.

[0643] Step 6:

[0644] Users perform agricultural tasks based on notifications from their devices. User feedback is sent to the server via the device. This feedback includes information such as what tasks were performed and whether they were successful or not.

[0645] Step 7:

[0646] The server analyzes user feedback and fine-tunes its artificial intelligence algorithms. This improves the accuracy of subsequent data analysis and notification generation, thereby enhancing the overall usefulness of the system.

[0647] (Example 2)

[0648] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0649] Traditional agricultural management systems provide notifications based on the analysis of environmental information, but they fail to consider the user's emotions and psychological state, resulting in a lack of useful information for users experiencing stress. Furthermore, they are insufficient in terms of real-time data communication and continuous improvement based on user feedback.

[0650] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0651] In this invention, the server includes means for collecting environmental information, means equipped with an intelligent algorithm for analyzing the collected information, and means for customizing notification content with an emotion engine that evaluates the user's emotional state. This makes it possible to provide information that takes into account the user's psychological state. Furthermore, by further including high-speed information communication means for immediate information exchange and means for receiving evaluation data from the user and fine-tuning the intelligent algorithm, the quality of information provision can be continuously improved.

[0652] "Environmental information" is a general term for information related to crop growth and health in agricultural settings, such as temperature, humidity, soil pH, and moisture content.

[0653] An "intelligent algorithm" is an algorithm used for data analysis, utilizing machine learning and artificial intelligence technologies to extract useful information and patterns from data.

[0654] "Users" refer to agricultural workers and managers who use this system and are the entities that make decisions based on the information provided.

[0655] An "emotional engine" is an engine for analyzing and evaluating a user's emotional state, using technologies such as natural language processing and vital sign analysis to quantify stress, fatigue, and other emotional states.

[0656] "Customizing notification content" refers to the process of adjusting the format and expression of the information provided according to the user's emotional state.

[0657] Embodiments of the present invention relate to a management system in the agricultural field. Specific implementation methods are described below.

[0658] The system primarily consists of terminals, servers, and users. The terminals are installed on farms and collect environmental information using various sensors. These sensors include temperature sensors to measure air temperature, humidity sensors to detect humidity, and soil sensors to measure soil pH and moisture content. This data is transmitted to the server using network technologies such as Wi-Fi and LoRa.

[0659] The server receives transmitted environmental information and analyzes the data using intelligent algorithms. Specifically, it utilizes software such as Python and TensorFlow, and uses machine learning models to assess crop health and pest / disease risk. Simultaneously, the server uses an emotion engine to analyze vital data and user input information to assess the user's emotional state. This analysis also utilizes natural language processing techniques to quantify the user's stress and fatigue levels.

[0660] Based on the analysis results, the server generates notifications for the user and customizes them according to the emotion engine's analysis. For example, if the user is feeling stressed, the server will make the notification more user-friendly and add encouraging messages. For example, for a user managing crops while traveling, a possible notification might be, "The risk of pests and diseases is high, so pay attention to your crops. Take a break and refresh yourself."

[0661] Users can view notifications sent from the server via their devices. These notifications are displayed through a dedicated application on smartphones and tablets, supporting the user's actions. User feedback is sent to the server via the device. This feedback information is used by the server to continuously refine its intelligent algorithms and emotion engine, improving the accuracy of information delivery.

[0662] Examples of prompts for a generative AI model include the following:

[0663] "Based on the management status of the tomato field, please suggest what kind of notification should be sent if a user is experiencing stress."

[0664] Through this mechanism, the present invention enables effective information provision that takes into account the user's psychological state, thereby supporting improved efficiency in agricultural management.

[0665] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0666] Step 1:

[0667] The terminal collects environmental information using various sensors installed on the farm. Specifically, a temperature sensor measures the temperature, a humidity sensor measures the humidity, and a soil sensor measures the pH value and moisture content. This environmental information is obtained as input data. The terminal packages this data and prepares it for transmission to the server. The output is the data package of environmental information that is sent to the server.

[0668] Step 2:

[0669] The server receives environmental information sent from the terminal. The input data is a data package containing various environmental factors of the farm. The server analyzes this data using intelligent algorithms. For example, it uses Python and TensorFlow to run machine learning models and perform crop health assessments and predict pest and disease risks. The output is the crop status and risk assessment information as a result of the analysis.

[0670] Step 3:

[0671] The server receives vital data and user input transmitted from the terminal to evaluate the user's emotional state. The input data consists of individual emotional indicators such as heart rate and stress level. The emotion engine performs natural language processing and data analysis to quantify and diagnose the user's emotional state. The output is an evaluation report regarding the user's emotional state.

[0672] Step 4:

[0673] The server customizes the notification content based on the analysis results and sentiment evaluation from the previous step. The inputs are crop status evaluation information and the user's sentiment evaluation report. The server uses a generative AI model to generate friendly notification messages and advice tailored to the sentiment. The output is the optimized notification message.

[0674] Step 5:

[0675] The terminal displays notification messages received from the server to the user via a smartphone or tablet application. The input data is the notification message sent from the server. The output is an information notification screen that the user can view through the terminal. This allows the user to receive information necessary for efficient farm management in real time.

[0676] Step 6:

[0677] Users input feedback on notifications into a terminal application and send it to the server. The input data is user feedback information regarding the notification. The server receives this feedback and uses it to fine-tune the intelligent algorithms and emotion engine. The output is the improved algorithm settings. This allows the system to continuously improve and provide more accurate information.

[0678] (Application Example 2)

[0679] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0680] In factory and other work environments, providing information without considering the emotional state of workers can negatively impact work efficiency and safety. In particular, uniform notifications and information provision to workers experiencing stress or fatigue can actually increase their workload; therefore, a flexible approach tailored to the emotional state of workers is required.

[0681] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0682] In this invention, the server includes means for collecting environmental information, means equipped with a machine learning algorithm for analyzing the collected information, means for generating warnings based on the analysis results and providing information to the user, and means for customizing the information according to the user's emotional state using an emotion recognition engine. This enables flexible information provision according to the emotional state of the worker.

[0683] "Means of collecting environmental information" refers to devices and mechanisms that use sensors to acquire various data such as temperature, humidity, light intensity, and sound from specific environments such as factories and farms.

[0684] A "machine learning algorithm" is a type of artificial intelligence used to identify patterns based on collected data and perform analysis and predictions. It is a technology that can automatically improve its accuracy based on past data.

[0685] "Means for generating warnings and providing information to users" refers to a system that uses various forms of notification, such as visuals and audio, to alert or instruct users based on data analysis results.

[0686] An "emotion recognition engine" is software or algorithms that determine a user's emotions and psychological state from their biometric data and behavioral data, and then provide appropriate responses and information based on that state.

[0687] The system implementing this invention aims to recognize the emotions of workers in a factory and provide them with appropriate information. The system mainly consists of a server, peripheral terminals, and users.

[0688] The server collects data from sensors installed throughout the factory to gather environmental information. This information includes temperature, humidity, noise levels, and more. The collected data is transmitted to the server via high-speed data communication.

[0689] The transmitted data is analyzed on the server using machine learning algorithms that leverage programming languages ​​such as Python. Machine learning frameworks like TensorFlow and PyTorch are used for this analysis. The analysis results serve as the basis for generating subsequent notifications.

[0690] Simultaneously, an emotion recognition engine uses the worker's biometric data (e.g., heart rate data obtained from a smartwatch) to determine their current emotional state. This engine optimizes the method of information transmission based on the user's psychological state.

[0691] For example, if a worker is feeling stressed, the terminal will display a message such as "Let's take a short break" in a visually and audibly appealing format, based on analysis results from the server. This allows workers to continue their work safely and efficiently while reducing their burden.

[0692] An example of a prompt message to be fed into a generative AI model is: "Based on data received from a wearable device, analyze the worker's current emotional state and provide a stress-reducing message at the appropriate time."

[0693] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0694] Step 1:

[0695] The terminal collects environmental information from sensors within the factory. Input data includes temperature, humidity, and noise levels. The terminal temporarily stores this data and transmits it to the server using high-speed data communication.

[0696] Step 2:

[0697] The server uses machine learning algorithms to analyze the received environmental data. TensorFlow and PyTorch are used for pattern recognition and anomaly detection. The analysis output provides indicators of the working environment's state and potential risks.

[0698] Step 3:

[0699] Simultaneously, the server receives biometric data from the worker's smartwatch. This input includes heart rate and body movements. The server uses an emotion recognition engine to estimate the worker's emotional state from the input data. The output is an evaluation of the emotional state at that moment.

[0700] Step 4:

[0701] The server integrates the results of environmental data analysis and emotion recognition evaluation to generate appropriate notifications. This notification generation process includes evaluating data correlations and determining optimized messages to reduce the mental burden on workers.

[0702] Step 5:

[0703] The terminal receives notifications from the server and provides them to the worker in an appropriate manner. Using visual displays and audio guidance, it presents messages prompting the worker to take specific actions, such as "take a break." This allows the worker to receive feedback tailored to their own situation.

[0704] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0705] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0706] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0707] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0708] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0709] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0710] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0711] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0712] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0713] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0714] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0715] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0716] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0717] 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.

[0718] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0719] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0720] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0721] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0722] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0723] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0724] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0725] The following is further disclosed regarding the embodiments described above.

[0726] (Claim 1)

[0727] Means of collecting environmental data,

[0728] A means equipped with an artificial intelligence algorithm for analyzing the collected data,

[0729] A means of generating notifications based on analysis results and providing information to the user,

[0730] A system that includes this.

[0731] (Claim 2)

[0732] The system according to claim 1, characterized in that it uses high-speed data communication as a means of communication and exchanges data in real time.

[0733] (Claim 3)

[0734] The system according to claim 1, further comprising means for receiving user feedback data and fine-tuning the artificial intelligence algorithm.

[0735] "Example 1"

[0736] (Claim 1)

[0737] A means of collecting various environmental parameters from the agricultural environment,

[0738] A means of analyzing collected data using artificial intelligence algorithms to evaluate the health and risks of crops,

[0739] A means of generating agricultural work guidelines as notifications based on the analysis results and providing the information to the user via a terminal,

[0740] A means of receiving user work feedback sent from a terminal and improving the performance of the AI ​​algorithm,

[0741] A system that includes this.

[0742] (Claim 2)

[0743] The system according to claim 1, characterized by transmitting and receiving data in real time using high-speed wireless communication and performing data processing immediately.

[0744] (Claim 3)

[0745] The system according to claim 1, which displays the generated notification on the user's mobile communication device to help the user make an immediate decision.

[0746] "Application Example 1"

[0747] (Claim 1)

[0748] A device for acquiring environmental data,

[0749] A device equipped with a machine learning algorithm for analyzing acquired information,

[0750] A device that generates instructions based on analysis results and provides information to the user,

[0751] A device for monitoring the operating patterns of equipment and performing maintenance and improvements,

[0752] A system that includes this.

[0753] (Claim 2)

[0754] The system according to claim 1, characterized in that it performs immediate data exchange using high-speed data transmission means.

[0755] (Claim 3)

[0756] The system according to claim 1, further comprising a device for receiving user feedback information and adjusting the machine learning algorithm.

[0757] "Example 2 of combining an emotion engine"

[0758] (Claim 1)

[0759] Means of collecting environmental information,

[0760] A means equipped with an intelligent algorithm for analyzing the collected information,

[0761] A means of generating notifications based on analysis results and providing information to the user,

[0762] It includes an emotion engine that evaluates the user's emotional state, and a means to customize notification content.

[0763] A system that includes this.

[0764] (Claim 2)

[0765] The system according to claim 1, characterized in that it uses high-speed information communication as a means of communication and exchanges information immediately.

[0766] (Claim 3)

[0767] The system according to claim 1, further comprising means for receiving evaluation data from users and fine-tuning an intelligent algorithm.

[0768] "Application example 2 when combining with an emotional engine"

[0769] (Claim 1)

[0770] Means of collecting environmental information,

[0771] A means equipped with machine learning algorithms for analyzing collected information,

[0772] A means of generating warnings based on analysis results and providing information to users,

[0773] A means of customizing information according to the user's emotional state using an emotion recognition engine,

[0774] A system that includes this.

[0775] (Claim 2)

[0776] The system according to claim 1, characterized by using rapid data communication to exchange information in real time.

[0777] (Claim 3)

[0778] The system according to claim 1, further comprising means for receiving user feedback information and fine-tuning the machine learning algorithm. [Explanation of Symbols]

[0779] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of collecting environmental data, A means equipped with an artificial intelligence algorithm for analyzing the collected data, A means of generating notifications based on analysis results and providing information to the user, A system that includes this.

2. The system according to claim 1, characterized in that it uses high-speed data communication as a means of communication and exchanges data in real time.

3. The system according to claim 1, further comprising means for receiving user feedback data and fine-tuning the artificial intelligence algorithm.

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