System

Abstract

A system is provided.SOLUTION: A system including sensor means for collecting environmental data, transmission means for transmitting the data collected from the sensor means to a server via communication means, analysis means for analyzing the data and prompting an appropriate agricultural action, advice generation means for proposing an agricultural action based on the analysis, notification means for notifying a user terminal of the advice, and feedback means for transmitting feedback data obtained from the user terminal to the server.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Current agriculture faces many challenges in terms of efficiency and quality control. Weather fluctuations, the impact of pests and diseases, and a lack of knowledge sharing are particularly serious issues. These problems have led to declining agricultural productivity and concerns about sustainability. Existing technologies have limitations in addressing these challenges, and more advanced systems are needed. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. A system is provided that includes a sensor means for collecting environmental data, a transmission means for transmitting data collected from the sensor means to a server via a communication means, an analysis means for analyzing the data and encouraging appropriate agricultural actions, an advice generation means for proposing agricultural actions based on the analysis, a notification means for notifying a user terminal of the advice, and a feedback means for transmitting feedback data obtained from the user terminal to the server. In addition, the communication means is equipped with a means for utilizing a fifth-generation mobile communication system (5G), thereby achieving even faster and more stable data communication. The analysis means is equipped with an analysis algorithm using artificial intelligence, enabling the generation of highly accurate advice. These means enable agricultural efficiency and sustainable production.

[0006] "Sensor means" refers to a device that collects data on crops and the surrounding environment (temperature, humidity, soil pH, water content, light intensity, etc.).

[0007] The "transmitting means" is a communication device for transferring data collected by the sensor means to the server.

[0008] The "server" is a central processing unit that receives and manages data sent via the network, analyzes the data, and generates advice.

[0009] "Communication means" refers to technology for transmitting data bidirectionally between sensor means and servers, and between servers and user terminals, and includes 5G.

[0010] "Analysis means" refers to a processing method that performs calculations and analysis based on the data collected by the server to generate agricultural insights and recommendations.

[0011] The "advice generation means" is a system that creates agricultural action suggestions and advice on optimal cultivation methods based on the results of the analysis means.

[0012] The "notification means" is a method for transmitting the generated advice or recommendation to the user terminal.

[0013] The "feedback means" is an interface through which the user inputs the agricultural actions they have performed and the results of those actions, and sends the input to the server.

[0014] A "user terminal" is a device used by farmers to receive advice and send feedback, such as a smartphone or tablet.

[0015] An "analysis algorithm" is a set of program steps that uses artificial intelligence to assess agricultural conditions and make recommendations based on collected data.

[0016] "Feedback data" refers to data related to the methods and results of agricultural actions taken by users, and is information used to improve the accuracy of the server's analytical model. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] This invention is an agricultural support system that utilizes AI, IoT, and 5G technologies. This system collects data on crops and the surrounding environment, and proposes optimal agricultural actions to users based on the analysis results.

[0039] System configuration

[0040] The system consists of the following main components:

[0041] 1. Sensor means

[0042] It is an IoT sensor for collecting crop and environmental data (temperature, humidity, soil pH, water content, light intensity, etc.) in real time.

[0043] 2. Transmission Method

[0044] This is a communication device that transmits collected data to a server using 5G communication.

[0045] 3. Server

[0046] This is the central processing unit that receives, manages, and analyzes the transmitted data. The server stores the collected data and uses analytical algorithms to optimize agriculture.

[0047] 4. Analysis method

[0048] The server uses an AI-based analytical algorithm to analyze the collected data and evaluate the agricultural situation.

[0049] 5. Advice Generation Methods

[0050] Based on the analysis results, the system suggests agricultural actions and provides advice on optimal cultivation methods.

[0051] 6. Means of notification

[0052] This is a means for sending advice generated by the server to the user's terminal via 5G communication and notifying farmers.

[0053] 7. User Devices

[0054] The devices farmers use to receive advice and send feedback include smartphones and tablets.

[0055] 8. Feedback channels

[0056] This is an interface for users to input the agricultural actions they have performed and the results of those actions, and send them to the server.

[0057] Program processing

[0058] Data collection and transmission

[0059] The sensor means continuously collects environmental data from various sensors around the farm, such as soil pH, temperature, humidity, and water content.

[0060] The transmission means transmits the collected environmental data to a server in real time. 5G communication is used, enabling high-speed and stable data transmission.

[0061] Data analysis and advice generation

[0062] The server stores the newly received data in an analysis database.

[0063] The analytics tool uses AI models to analyze the data and assess the health of the crop and the agricultural actions needed, such as detecting increased risk of pests and diseases when humidity or temperature falls outside certain ranges.

[0064] The advice generator generates advice that suggests optimal agricultural actions based on the analysis results, such as "spray a specific pesticide in the right amount" or "add a specific fertilizer to adjust the soil pH."

[0065] Advice notifications and feedback collection

[0066] The notification means sends the generated advice to the user's device, and the user can check the notification on their smartphone or tablet.

[0067] The user terminal performs agricultural actions based on the notification, such as spraying specific pesticides or adding fertilizer.

[0068] The feedback means provides an interface for users to input the results of their agricultural actions and transmit them to the server, thereby allowing users to report the effectiveness of their actions.

[0069] Specific examples

[0070] For example, if crops are placed in a high-humidity environment during the rainy season, the sensor means will collect humidity data frequently. This data will then be immediately sent to the server using 5G communications. The server will analyze this data and detect that humidity may increase the risk of pest and disease outbreaks in the crops. Based on the analysis results, the advice generation means will suggest an appropriate pest control method (e.g., early application of a specific pesticide). A notification will be sent to the user's device, and the user will follow the instructions to quickly spray the pesticide, thereby preventing the outbreak of pests and diseases. The user will then input the results of their action into the feedback means and send it to the server. The server will use this feedback for analysis to improve the accuracy of advice from the next time onwards.

[0071] This will provide a coherent system that supports increased agricultural productivity and sustainability.

[0072] The processing flow will be explained below.

[0073] Step 1:

[0074] The sensor means collects real-time environmental data on the crops and their surroundings (temperature, humidity, soil pH, water content, light intensity, etc.). Specifically, each sensor measures at a set frequency and temporarily stores the data in its internal memory.

[0075] Step 2:

[0076] The transmitting means transmits the collected environmental data to the server using 5G communication. Specifically, after the sensor means collects a certain amount of data, it divides it into packets and transmits them to the server via the 5G network.

[0077] Step 3:

[0078] The server receives the data sent from the sensor means and stores it in an analysis database. Specifically, it checks the consistency of the received data and stores only accurate data in the database.

[0079] Step 4:

[0080] The analysis means inputs the received data into an AI model to analyze the condition of the crops and determine the optimal agricultural actions. Specifically, it detects fluctuation patterns in temperature and humidity and determines whether the crops are at risk.

[0081] Step 5:

[0082] The advice generator generates specific agricultural action suggestions based on the analysis results, for example, if the analysis results indicate soil acidification, it generates advice such as "add lime."

[0083] Step 6:

[0084] The notification means notifies the user terminal of the generated advice. Specifically, the advice content is sent as a push notification to a smartphone or tablet so that the user can immediately check it.

[0085] Step 7:

[0086] The user device receives and displays the notification, allowing the user to review the specific agricultural action recommendations.

[0087] Step 8:

[0088] The user performs agricultural actions based on the advice presented on the user terminal. For example, if the advice includes "add fertilizer," the user selects an appropriate fertilizer and applies the recommended amount to the farmland.

[0089] Step 9:

[0090] The feedback means provides an interface for inputting the results of the agricultural actions performed by the user, and the user inputs the details of the work that he or she actually performed and the results of the work into this interface.

[0091] Step 10:

[0092] The user terminal transmits the input feedback data to the server. Specifically, the feedback data is packetized and transmitted to the server via 5G communication.

[0093] Step 11:

[0094] The server analyzes the received feedback data and uses it to improve the accuracy of the analytical model. The server stores the feedback data in a database and uses it to train the AI ​​model, improving the accuracy of advice from the next time onwards.

[0095] The above steps will create a system that continuously monitors the health of crops and their growing environment and suggests optimal agricultural actions.

[0096] Example 1

[0097] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0098] Conventional agricultural support systems have had problems with the collection and analysis of environmental data and the proposal of appropriate agricultural actions not being done quickly enough and accurately. In particular, manual data collection and analysis takes time, often resulting in delays in appropriate responses. Another issue is that data feedback is not effectively utilized, making it difficult to improve the accuracy of future advice.

[0099] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0100] In this invention, the server includes a sensor device that collects environmental data, a transmission device that transmits the data collected from the sensor device to a central processing unit via a communication device, an analysis device that analyzes the data and encourages appropriate agricultural actions, an advice generation device that suggests agricultural actions based on the analysis, a notification device that notifies a user terminal of the advice, and a feedback device that transmits feedback data obtained from the user terminal to the central processing unit. This enables rapid and accurate collection and analysis of environmental data, rapid suggestion of appropriate agricultural actions, and effective use of feedback.

[0101] A "sensor device" is a device that collects environmental data such as temperature, humidity, soil pH, water content, and light intensity.

[0102] A "communication device" is a device for transmitting collected environmental data, and may include, for example, a fifth generation mobile communication system (5G).

[0103] The "central processing unit" is a device for analyzing and managing received environmental data.

[0104] The "transmitting device" is a device for transmitting data collected from the sensor device to the central processing unit via the communication device.

[0105] "Analysis Device" means a device used by the Central Processing Unit to perform the necessary analysis, such as analyzing data using AI models or machine learning algorithms.

[0106] The "advice generator" is a device for suggesting appropriate agricultural actions based on the analysis results.

[0107] The "notification device" is a device for notifying the generated advice to the user terminal.

[0108] A "feedback device" is a device for transmitting feedback data obtained from a user terminal to a central processing unit.

[0109] A "user terminal" is a device that allows a user to receive advice and send feedback, and includes a smartphone, tablet, or the like.

[0110] This invention is an agricultural support system that utilizes AI, IoT, and 5G technologies. This system collects data on agricultural crops and the surrounding environment, and proposes optimal agricultural actions to users based on the analysis results. The specific hardware and software configurations and their operation are described below.

[0111] System configuration

[0112] The system consists of the following main components:

[0113] 1. Sensor device

[0114] These IoT sensors collect crop and environmental data (temperature, humidity, soil pH, water content, light intensity, etc.) in real time, enabling detailed environmental information within the farm to be obtained. Specifically, they include soil humidity and temperature sensors.

[0115] 2. Communications Equipment

[0116] This is a communications device that transmits collected data to a central processing unit using 5G communications, enabling fast and reliable data transmission.

[0117] 3. Central Processing Unit

[0118] This is a central processing unit that receives, manages, and analyzes the transmitted data. Specifically, a database is placed on the server and used to store and analyze various data. The server stores the collected data and uses analytical algorithms to optimize agriculture.

[0119] 4. Analysis device

[0120] The server uses AI-based analytical algorithms to analyze the collected data and evaluate the agricultural situation. This allows for the determination of the health of crops and necessary agricultural actions based on environmental data. Specifically, the analysis is carried out using machine learning models and neural networks.

[0121] 5. Advice Generator

[0122] Based on the analysis results, the system proposes agricultural actions and provides advice on optimal cultivation methods, such as "spray specific pesticides in the right amounts" or "add specific fertilizer to adjust the soil pH."

[0123] 6. Notification device

[0124] This device sends and notifies the user of advice generated by the server via 5G communication, enabling real-time notifications. Users can receive advice on their smartphones, tablets, or other devices.

[0125] 7. Feedback Devices

[0126] This is a device for transmitting feedback data obtained from user terminals to the central processing unit. Users input the agricultural actions they performed and their results, and send this to the server.

[0127] Specific examples

[0128] For example, if crops are placed in a high-humidity environment during the rainy season, the sensor device will collect humidity data frequently. This data will then be immediately sent to a central processing unit using 5G communications. The server will then analyze the data and detect that humidity may increase the risk of pest infestation in the crops. Based on the analysis results, the advice generation device will suggest an appropriate pest control method (e.g., early application of a specific pesticide). A notification will be sent to the user's device, and the user will follow the instructions to quickly spray the pesticide, thereby preventing the occurrence of pests and diseases. The user will then input the results of their actions into a feedback device and send it to the server. The server will use this feedback for analysis to improve the accuracy of future advice.

[0129] Prompt Sentence Examples

[0130] An example of a prompt sentence to input to a generative AI model (e.g., ChatGPT (registered trademark)) is shown below.

[0131] Please explain the following agricultural support systems that utilize AI, IoT, and 5G technologies:

[0132] The sensor collects temperature and humidity data and transmits it to a server via 5G communication.

[0133] The server analyzes the received data and evaluates the health of the crops.

[0134] If necessary, it generates suggestions for agricultural actions (e.g., spraying pesticides) and notifies the user.

[0135] After the user performs an action, the result is given as feedback.

[0136] Please explain with specific examples.

[0137] The above is an embodiment of the invention.

[0138] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0139] Program processing

[0140] Step 1: Data collection

[0141] Subject: Sensor device

[0142] The sensor device collects environmental data such as temperature, humidity, soil pH, water content, and light intensity using various sensors placed around the farm.

[0143] Input: Environmental data on soil and air temperature, humidity, pH, and light intensity

[0144] Data processing and calculation: The sensor measurement values ​​are temporarily stored in the memory of the sensor device.

[0145] Output: Temporarily saved environmental data

[0146] Specific operation: The humidity sensor measures the humidity at 80% and records the data in memory.

[0147] Step 2: Send data

[0148] Subject: Communication device

[0149] The communication device transmits data collected from the sensor device to the central processing unit in real time, using 5G communication to achieve high-speed and stable data transmission.

[0150] Input: Temporarily saved environmental data (e.g. humidity 80%)

[0151] Data processing and calculation: Data is transmitted to the central processing unit using the 5G communication module.

[0152] Output: Environmental data received by the server

[0153] Specific operation: Humidity data (80%) is sent to the server via 5G communication.

[0154] Step 3: Save Data

[0155] Subject: Server

[0156] The server stores the received environmental data in an analysis database.

[0157] Input: Received environmental data (humidity 80%)

[0158] Data processing and calculation: Register environmental data in the database.

[0159] Output: Data stored in the database

[0160] Specific operation: The server saves the humidity data (80%) in the database.

[0161] Step 4: Data analysis

[0162] Subject: Server

[0163] The server uses AI models (e.g., machine learning algorithms) to analyze the data, which is then used by the analysis device to assess the health of the crops and the agricultural actions required.

[0164] Input: Environmental data stored in a database

[0165] Data processing and calculation: Data analysis is performed using AI models and machine learning algorithms.

[0166] Output: Analysis results (indicating that high humidity increases the risk of pests)

[0167] Specific operation: The server performs an analysis and determines that the humidity level of 80% is outside a specific range, increasing the risk of pests and diseases.

[0168] Step 5: Advice Generation

[0169] Subject: Server

[0170] The server generates appropriate agricultural actions based on the analysis results, and the advice generator creates specific advice and recommends it to the user.

[0171] Input: Analysis results (increase in pest risk)

[0172] Data processing and calculation: Based on the analysis results, appropriate agricultural actions (e.g., spraying pesticides) are generated.

[0173] Output: Generated advice

[0174] Specific operation: The server generates advice such as "Because the humidity is high, please spray 5 liters of a specific pesticide (brand name: XY)."

[0175] Step 6: Advice Notification

[0176] Subject: Server

[0177] The server passes the generated advice to the notification device, which then transmits the advice to the user device using 5G communications. The user device then displays the advice and notifies the user.

[0178] Input: Generated advice

[0179] Data processing and calculation: Advice is sent using 5G communication.

[0180] Output: Advice displayed on the user's terminal

[0181] Specific operation: The server sends advice to the smartphone, which then displays a notification saying, "Due to high humidity, please spray 5 liters of pesticide XY."

[0182] Step 7: Take Action

[0183] Subject: User

[0184] The user performs agricultural actions based on the notified advice.

[0185] Input: Received advice

[0186] Data processing and calculation: Implement specific agricultural actions based on the advice.

[0187] Output: Farming actions performed

[0188] Specific behavior: The user receives a notification on their smartphone and sprays 5 liters of pesticide XY.

[0189] Step 8: Gather feedback

[0190] Subject: User

[0191] The user inputs the agricultural actions they have performed and their results into the user terminal, and the feedback device transmits the input feedback to the server.

[0192] Input: The result of the agricultural action performed

[0193] Data processing and calculation: The results are entered into the user's terminal and sent to the server.

[0194] Output: Feedback data sent to the server

[0195] Specific operation: The user types "Pesticide XY was sprayed" into their smartphone and sends it.

[0196] Step 9: Use feedback

[0197] Subject: Server

[0198] The server adds the received feedback to the analysis data and uses it to improve the accuracy of advice from the next time onwards.

[0199] Input: Feedback data

[0200] Data processing and calculation: Feedback is reflected in the analysis and the database is updated.

[0201] Output: Improved advice for next time

[0202] Specific operation: The server uses the feedback that "after spraying pesticide XY, the humidity returned to the appropriate value" for analysis, and improves the accuracy of mold prevention advice from the next time onwards.

[0203] The above are the specific steps and details of the system's program processing.

[0204] (Application example 1)

[0205] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0206] Conventional factory automation systems have issues with real-time response and accuracy when it comes to detecting equipment anomalies and proposing maintenance. It's also difficult to quickly notify appropriate actions, making it impossible to maximize production efficiency and equipment lifespan. This leads to unnecessary downtime and excessive maintenance, which increases production costs and increases the burden on workers.

[0207] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0208] In this invention, the server includes a sensor means for collecting environmental data, a transmission means for transmitting the data collected from the sensor means to a central processing unit via a communication means, an analysis means for analyzing the data and promoting appropriate manufacturing processes and maintenance actions, an advice generation means for proposing manufacturing processes and maintenance actions based on the analysis, a notification means for notifying a user device of the advice, and a feedback means for transmitting feedback data obtained from the user device to the central processing unit. This enables real-time data collection within the factory and highly accurate analysis using AI to propose optimal actions. Furthermore, the use of 5G communications enables high-speed data transmission, allowing for prompt maintenance and process adjustments as needed. This minimizes equipment downtime and improves production efficiency.

[0209] "Environmental data" refers to data related to the machinery and working environment within the factory, such as temperature, humidity, and vibration.

[0210] "Sensor means" refers to devices and techniques for collecting environmental data in real time.

[0211] "Communication Means" refers to devices and technologies for transmitting data collected from the Sensor Means, including in particular those utilizing fifth generation mobile communication systems (5G).

[0212] "Transmitting Means" refers to devices and techniques for transmitting environmental data from the Sensor Means to the Central Processing Unit.

[0213] "Central Processing Unit" means a computer system that receives, stores, and analyzes collected data.

[0214] "Analysis Tools" refers to devices and algorithms used to analyze collected environmental data and identify appropriate manufacturing process or maintenance actions.

[0215] "Advice generation means" refers to devices and techniques that generate suggestions for manufacturing processes and maintenance actions based on the analysis results obtained by the analysis means.

[0216] "Notification means" refers to devices and techniques for transmitting and notifying the user device of the suggestions generated by the advice generation means.

[0217] A "user device" is a device on which a user receives notifications, and includes a smartphone or tablet.

[0218] "Feedback means" refers to devices and techniques for transmitting feedback data obtained from user devices to a central processing unit.

[0219] This invention is a system that collects data on equipment and the environment within a factory, analyzes that data, and proposes optimal manufacturing processes and maintenance actions. The system consists of the following main components:

[0220] System configuration

[0221] The system consists of the following main components:

[0222] 1. Sensor means

[0223] The sensor means is a device or technology for collecting environmental data such as temperature, humidity, and vibration in a factory in real time. This includes temperature sensors, humidity sensors, vibration sensors, etc.

[0224] 2. Transmission Method

[0225] The transmission means is a device and technology for transmitting data collected by the sensor means to the central processing unit, and uses 5G communication, enabling high-speed and stable data transmission.

[0226] 3. Central Processing Unit

[0227] The central processing unit is a computer system that receives, stores, and analyzes collected data. Specifically, it includes a database server and an AI analysis server.

[0228] 4. Analysis method

[0229] The analysis means is an analysis algorithm using artificial intelligence in the central processing unit, which analyzes the collected data and determines whether the manufacturing process or maintenance is necessary. For the analysis, Tensorflow (registered trademark) and Keras are used.

[0230] 5. Advice Generation Methods

[0231] The advice generator is a system that proposes optimal manufacturing processes and maintenance actions based on the results obtained from the analysis means, including appropriate maintenance timing and specific action plans.

[0232] 6. Means of notification

[0233] The notification means is a device or technology for transmitting the generated advice to a user device, such as a smartphone or tablet.

[0234] 7. Feedback channels

[0235] The feedback means is an interface for transmitting feedback data obtained from the user device to the central processing unit. By inputting the actions taken by the user and their results, the central processing unit uses this information for further analysis and optimization.

[0236] Program processing

[0237] Data collection and transmission

[0238] The sensor means continuously collects environmental data from each sensor inside the factory, such as temperature, humidity, vibration, etc. The transmission means transmits the collected environmental data to the central processing unit using 5G communication.

[0239] Data analysis and advice generation

[0240] The central processing unit stores newly received data in an analysis database. The analysis means analyzes the data using an AI model and evaluates the health of the equipment and the necessary manufacturing and maintenance actions. Specifically, if vibrations exceed a certain threshold, it detects the high possibility that an abnormality has occurred. The advice generation means generates advice that suggests the optimal manufacturing and maintenance actions based on the analysis results.

[0241] Advice notifications and feedback collection

[0242] The notification means sends the generated advice to the user device and notifies it. The user can check the notification on a smartphone or tablet. The user then performs manufacturing or maintenance actions based on the notification. Specifically, this could include performing machine maintenance or adjusting process settings. The feedback means provides an interface that allows the user to input the results of the actions they have taken and send them to the central processing unit. This allows the effectiveness of the actions to be reported. The central processing unit uses this feedback for analysis and improves the accuracy of advice from the next time onwards.

[0243] Specific examples

[0244] For example, if a robot arm detects abnormal vibrations during production in a factory, the sensor means immediately collects and transmits the data. The central processing unit analyzes this data and, if it determines that machine maintenance is necessary, the advice generation means immediately suggests that "maintenance of this robot arm is necessary." The user receives the notification and promptly performs maintenance work according to the instructions. The results are entered into the user device and transmitted to the central processing unit as feedback data. This improves the accuracy of abnormality detection and maintenance actions from the next time onwards.

[0245] Example prompt for a generative AI model:

[0246] Design a system that collects and analyzes sensor data, specifically temperature, humidity, and vibration data, to propose optimal maintenance methods for equipment.

[0247] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0248] Step 1:

[0249] The sensor means collects environmental data such as temperature, humidity, and vibration in the factory in real time.

[0250] Specifically, each sensor measures data at a specified interval and temporarily stores the data in memory. The input is environmental data, and the output is the collected environmental data.

[0251] Step 2:

[0252] The transmitting means transmits the collected environmental data to the central processing unit using 5G communication.

[0253] Specifically, the data stored in the sensor's memory is converted into packets and sent to a central processing unit via a 5G modem. The input is the collected environmental data, and the output is the data sent to the central processing unit.

[0254] Step 3:

[0255] The central processing unit stores the newly received data in a database.

[0256] Specifically, the received data is written to an analysis database and a timestamp is added. The input is the data received via 5G communication, and the output is the data stored in the database.

[0257] Step 4:

[0258] The analytics tool uses AI models to analyze the data and assess the health of the equipment and any necessary production or maintenance actions.

[0259] Specifically, using a machine learning framework such as TensorFlow, collected data is input into a trained model to detect anomalies and identify necessary actions. The input is the data stored in the database, and the output is the analysis results.

[0260] Step 5:

[0261] The advice generation means generates advice that proposes optimal manufacturing and maintenance actions based on the analysis results.

[0262] Specifically, specific advice such as "This robot arm needs maintenance" is generated based on the analysis results. This identifies the necessary maintenance work and adjustments to the manufacturing process. The input is the analysis results, and the output is the generated advice.

[0263] Step 6:

[0264] The notification means transmits the generated advice to the user device to notify it.

[0265] Specifically, a messaging protocol is used via a notification server to notify the generated advice to the user's smartphone or tablet. The input is the generated advice, and the output is the notification to the user device.

[0266] Step 7:

[0267] Users can take production and maintenance actions based on notifications.

[0268] Specifically, the user checks the notification content and performs machine maintenance or process adjustments according to the instructions. The input is the notification received by the user device, and the output is the action taken.

[0269] Step 8:

[0270] A feedback means provides an interface for inputting and transmitting results of actions taken by the user to the central processing unit.

[0271] Specifically, the results of the actions taken by the user are input into the application, and the data is sent back to the central processing unit. The input is the result of the action taken, and the output is the feedback data sent to the central processing unit.

[0272] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0273] This invention is an agricultural support system that utilizes AI, IoT, 5G technology, and an emotion engine. It not only collects data on crops and the surrounding environment and suggests optimal agricultural actions to users based on the analysis results, but also recognizes the user's emotions and adjusts the content and presentation of advice to provide more effective agricultural support.

[0274] System configuration

[0275] The system consists of the following main components:

[0276] 1. Sensor means

[0277] It is an IoT sensor for collecting crop and environmental data (temperature, humidity, soil pH, water content, light intensity, etc.) in real time.

[0278] 2. Transmission Method

[0279] This is a communication device that transmits collected data to a server using 5G communication.

[0280] 3. Server

[0281] This is the central processing unit that receives, manages, and analyzes the transmitted data. The server stores the collected data and uses analytical algorithms to optimize agriculture.

[0282] 4. Analysis method

[0283] The server uses an AI-based analytical algorithm to analyze the collected data and evaluate the agricultural situation.

[0284] 5. Advice Generation Methods

[0285] Based on the analysis results, the system suggests agricultural actions and provides advice on optimal cultivation methods.

[0286] 6. Means of notification

[0287] This is a means for notifying the user terminal of the advice generated by the server and conveying it to farmers.

[0288] 7. User Devices

[0289] The devices farmers use to receive advice and send feedback include smartphones and tablets.

[0290] 8. Feedback channels

[0291] This is an interface for users to input the agricultural actions they have performed and the results of those actions, and send them to the server.

[0292] 9. Emotion Engine

[0293] This is a device that analyzes emotions from the user's voice and facial expressions and feeds the results back to the server.

[0294] System action

[0295] Data collection and transmission

[0296] The sensor means continuously collects environmental data from various sensors around the farm, such as soil pH, temperature, humidity, and water content.

[0297] The transmission means transmits the collected environmental data to the server using 5G communication, which enables high-speed and stable data transmission.

[0298] Data analysis and advice generation

[0299] The server stores the newly received data in an analysis database.

[0300] The analytics tool uses AI models to analyze the data and assess the health of the crops and the agricultural actions they need to take, such as detecting patterns of temperature and humidity fluctuations and determining whether the crops are at risk.

[0301] The advice generator generates advice that suggests optimal agricultural actions based on the analysis results, such as "spray a specific pesticide in the right amount" or "add a specific fertilizer to adjust the soil pH."

[0302] Advice Notification and Emotion Recognition

[0303] The notification means notifies the user terminal of the generated advice. Specifically, the advice content is sent as a push notification to a smartphone or tablet so that the user can immediately check it.

[0304] The emotion engine analyzes the user's voice and facial expressions and feeds back their emotional state to the server. For example, if the user is in a stressful state, that information is sent to the server.

[0305] Emotion-based advice adjustment

[0306] The server receives feedback from the emotion engine and adjusts the content and expression of advice in the advice generation means. For example, if the user is under stress, advice containing encouraging words is generated to increase the user's motivation.

[0307] Engaging with users and gathering feedback

[0308] The user device receives and displays the notification, allowing the user to review the specific agricultural action recommendations.

[0309] The user performs agricultural actions based on the advice presented on the user terminal. For example, if the advice includes "add fertilizer," the user selects an appropriate fertilizer and applies the recommended amount to the farmland.

[0310] The feedback means provides an interface for inputting the results of the agricultural actions performed by the user, and the user inputs the details of the work that he or she actually performed and the results of the work into this interface.

[0311] The user terminal transmits the input feedback data to the server. Specifically, the feedback data is packetized and transmitted to the server via 5G communication.

[0312] The server analyzes the received feedback data and uses it to improve the accuracy of the analytical model. The server stores the feedback data in a database and uses it to train the AI ​​model, improving the accuracy of advice from the next time onwards.

[0313] Specific examples

[0314] For example, if crops are placed in a high-humidity environment during the rainy season, the sensor means will collect humidity data frequently. This data will then be immediately sent to the server using 5G communications. The server will analyze this data and detect that humidity may increase the risk of pest infestation. Based on the analysis results, the advice generation means will suggest an appropriate pest control method (e.g., early application of a specific pesticide). A notification will be sent to the user's device, and the user will quickly follow the instructions to spray the pesticide. In addition, the emotion engine will analyze the user's voice and facial expressions. If the server detects that the user is under stress, the server will generate advice that includes encouraging words. The user then inputs the results of their actions into the feedback means and sends it to the server. The server will use this feedback for analysis to improve the accuracy of future advice.

[0315] This not only provides a consistent system that supports agricultural productivity and sustainability, but also responds to the user's emotional state, resulting in more effective agricultural support.

[0316] The processing flow will be explained below.

[0317] Step 1:

[0318] The sensor means collects real-time environmental data on the crops and their surroundings (temperature, humidity, soil pH, water content, light intensity, etc.). Each sensor periodically obtains its own measured value and temporarily stores the data in its internal memory.

[0319] Step 2:

[0320] The transmission means packetizes the collected environmental data and transmits it to the server using 5G communication, allowing the environmental data to be transmitted to the server at high speed and with high stability.

[0321] Step 3:

[0322] The server receives the environmental data sent from the sensor means and first checks its consistency. After the data consistency is confirmed, it stores it in a database and prepares it for analysis.

[0323] Step 4:

[0324] The analysis means stores the received data in an analytical database and inputs the data into an AI model, which compares it with past data to assess the current environmental conditions and the health of the crops. For example, it analyzes patterns of temperature and humidity fluctuations to predict whether the crops are at risk of pests or diseases.

[0325] Step 5:

[0326] The advice generation means generates specific agricultural action proposals based on the analysis results. For example, if humidity is high and the risk of a specific pest is high, the system generates specific advice such as "spray a specific pesticide in the appropriate amount."

[0327] Step 6:

[0328] The emotion engine recognizes emotions from the user's voice and facial expressions and sends the data to the server. The emotion engine determines the user's emotional state, such as stress or satisfaction, from their reaction.

[0329] Step 7:

[0330] The server analyzes the emotion data received from the emotion engine, and if the user is feeling stressed, feeds back that information to the advice generating means.

[0331] Step 8:

[0332] The advice generation means adjusts the content and expression of the advice based on the analysis results of the emotion engine. For example, if the user is in a high-stress state, the advice generation means generates advice that includes kinder expressions and encouraging words.

[0333] Step 9:

[0334] The notification means sends the generated advice to the user terminal, and the advice content is displayed on the user's smartphone or tablet via push notification.

[0335] Step 10:

[0336] The user terminal displays the received advice, and the user confirms it and plans agricultural actions based on the advice.

[0337] Step 11:

[0338] The user follows the advice displayed on the user terminal and performs specific agricultural actions. For example, if the advice is to "spray a specific pesticide in the appropriate amount," the user will spray the indicated amount of pesticide on the field.

[0339] Step 12:

[0340] The feedback means provides an interface for inputting the results of the agricultural actions performed by the user. The user inputs the details of the work actually performed and the results, and saves them in the terminal.

[0341] Step 13:

[0342] The user terminal packetizes the input feedback data and transmits it again to the server via 5G communication.

[0343] Step 14:

[0344] The server analyzes the received feedback data and uses it to improve the accuracy of the analytical model. The feedback data is stored in a database and used to train the AI ​​model, improving the accuracy of advice from the next time onwards.

[0345] This will enable a more effective agricultural support system that not only continuously monitors the health and growth environment of crops and suggests optimal agricultural actions, but also responds to the user's emotional state.

[0346] Example 2

[0347] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0348] In modern agriculture, the collection and analysis of environmental data to propose appropriate agricultural actions is extremely important. However, conventional systems do not adequately analyze the collected data or generate advice, and in particular lack support that takes into account the user's emotional state. This reduces user motivation and makes it difficult to provide effective agricultural support. Furthermore, delays in real-time data transmission and analysis can lead to reduced productivity and increased agricultural risks. Effective methods to resolve these issues are needed.

[0349] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a sensor means for collecting environmental data, a transmission means for transmitting data collected from the sensor means to the server via a communication means, an analysis means for analyzing the data and encouraging appropriate agricultural actions, an advice generation means for suggesting agricultural actions based on the analysis, a notification means for notifying the user terminal of the advice, an emotion analysis means for analyzing the emotional state of the user, an emotion adaptation means for adjusting the content and expression of the advice based on the results of the emotion analysis means, and a feedback means for transmitting feedback data obtained from the user terminal to the server. This enables effective agricultural support that takes the user's emotional state into consideration, and by realizing data transmission and analysis in real time, agricultural productivity and efficiency are significantly improved.

[0350] A "sensor means" is a device for collecting environmental data.

[0351] The "transmitting means" is a device for transmitting data collected from the sensor means to the server via the communication means.

[0352] The "analysis means" is a device that analyzes the received data and encourages appropriate agricultural actions.

[0353] The "advice generator" is a device for suggesting agricultural actions based on the results of the analysis means.

[0354] The "notification means" is a device for notifying the user terminal of the generated advice.

[0355] The "emotion analysis means" is a device for analyzing the user's emotional state.

[0356] The "emotion adaptation means" is a device for adjusting the content and expression of advice based on the results of the emotion analysis means.

[0357] The "feedback means" is a device for transmitting feedback data obtained from a user terminal to a server.

[0358] "Communication medium" is the technology used to send and receive data.

[0359] A "user terminal" is a device through which a user receives advice and sends feedback.

[0360] Basic system configuration

[0361] The system utilizes AI, IoT, 5G technology, and an emotion engine to collect data on crops and the surrounding environment, and then suggests optimal agricultural actions to users based on the analysis results.It also analyzes the user's emotional state and adjusts the content and presentation of advice to provide effective agricultural support.

[0362] Specifically, the following hardware and software are used.

[0363] Main hardware used:

[0364] IoT sensors: temperature sensors, humidity sensors, soil pH sensors, moisture sensors, light intensity sensors, etc.

[0365] Communications equipment: 5G-compatible modems and routers.

[0366] Server: The central processing unit responsible for collecting, analyzing, storing data and generating advice.

[0367] User device: A device such as a smartphone or tablet that allows a user to receive advice and send feedback.

[0368] Emotion engine: Camera and microphone for analyzing the user's voice and facial expressions.

[0369] Main software used:

[0370] AI model: The machine learning algorithm used to analyze data and generate advice.

[0371] Database software: NoSQL database, e.g. MongoDB.

[0372] Analysis algorithms: Software that analyzes environmental data such as temperature, humidity, soil pH, water content, and light intensity to assess crop health and any necessary agricultural actions.

[0373] Notification software: A mobile application for sending push notifications.

[0374] System operation example

[0375] For example, if crops are placed in a high-humidity environment during the rainy season, the sensor will collect humidity data frequently. The device will then transmit the data obtained from the humidity sensor to a server via 5G communication. The server will then receive the data and store it in a database.

[0376] The server uses an AI analysis model to analyze humidity data and detect whether humidity can increase the risk of pests and diseases in crops. Based on the analysis results, the advice generation means generates advice suggesting appropriate pest control methods (e.g., early application of a specific pesticide).

[0377] The generated advice is sent as a push notification to the user's smartphone or tablet via the notification means. The user receives the advice and sprays the pesticide as instructed. The emotion engine analyzes the user's voice and facial expression, and if it determines that the user is under stress, the server generates advice including words of encouragement.

[0378] Example prompt sentence:

[0379] "We've detected that the user is under stress. Please generate friendly, encouraging advice."

[0380] The user then sends the results of their agricultural actions to the server via a feedback mechanism. The server analyzes this feedback data, stores it in a database, and uses it as training data to improve the accuracy of advice in future.

[0381] effect

[0382] This system enables real-time, highly accurate data analysis and provides personalized agricultural support that takes into account the user's emotional state, thereby improving agricultural productivity and efficiency and enabling sustainable agricultural practices.

[0383] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0384] Step 1: Data collection

[0385] The terminal (sensor means) collects environmental data within the farm in real time. The sensor means uses temperature sensors, humidity sensors, soil pH sensors, moisture sensors, light intensity sensors, etc. to obtain data on temperature, humidity, soil pH, moisture content, light intensity, etc. For example, the temperature sensor measures 25°C, and the humidity sensor measures 80%.

[0386] Input: Environmental data

[0387] Output: Raw data from the sensor

[0388] Step 2: Send data

[0389] The environmental data collected by the device is sent to a server using 5G communication. Specifically, the data obtained from each sensor is packaged in JSON format and sent over the 5G network. For example, data such as {"temperature": 25, "humidity": 80} is sent to the server.

[0390] Input: Raw data from the sensor

[0391] Output: JSON data sent to the server via the communication method

[0392] Step 3: Data reception and storage

[0393] The server receives the data sent from the device and stores it in a database for analysis. For example, the server's API receives JSON data and executes a process to store it in a NoSQL database.

[0394] Input: JSON data sent via communication means

[0395] Output: Environmental data stored in a database

[0396] Step 4: Data analysis

[0397] The server analyzes the data using an AI analysis model. For example, it determines that the risk of pests and diseases increases when humidity levels remain high. The server compares this data with past data and makes a risk assessment. Specifically, it analyzes temperature and humidity patterns to assess the health of the crops.

[0398] Input: Environmental data stored in a database

[0399] Output: Analysis results (risk of pests, etc.)

[0400] Step 5: Advice Generation

[0401] The server generates advice based on the analysis results. For example, it generates specific instructions such as "Because humidity is high, spray a specific pesticide now." The advice generation means proposes optimal agricultural actions based on the analysis results.

[0402] Input: Analysis results

[0403] Output: Specific advice

[0404] Step 6: Advice Notification

[0405] The server sends the generated advice to the user's device. The advice is sent to the user in real time using push notifications. For example, a notification saying "Please spray pesticides now" is displayed on the smartphone.

[0406] Input: Specific advice

[0407] Output: Notification to user terminal

[0408] Step 7: User sentiment analysis

[0409] The emotion engine analyzes the user's voice and facial expression and transmits the user's emotional state to the server. For example, the emotion engine determines that the user is feeling stressed based on the user's tone of voice and facial expression.

[0410] Input: Audio and visual data

[0411] Output: Parsed emotion data

[0412] Step 8: Adjusting Advice Based on Emotions

[0413] The server adjusts the content and expression of the advice based on the results of emotion analysis. For example, for a user who is under stress, it adds an encouraging message such as, "Thank you for your hard work. Please take a short break before proceeding to the next step."

[0414] Input: Parsed emotion data

[0415] Output: Adjusted advice

[0416] Step 9: User Actions and Feedback Collection

[0417] The user receives the notification and takes action based on the advice, for example, spraying a specific pesticide as instructed. The feedback means provides an interface that records the actions taken by the user, and the user inputs the results, such as "the pesticide has been sprayed."

[0418] Input: User action result

[0419] Output: Feedback data

[0420] Step 10: Sending and using feedback data

[0421] The user device sends the recorded feedback data to a server via 5G communication. The server receives the feedback data and uses it for analysis. The server uses the feedback data to train the AI ​​model and improve the accuracy of advice from the next time onwards.

[0422] Input: Feedback data sent from the user device

[0423] Output: Updated analytical model

[0424] By repeatedly performing these steps, the system not only continues to support the user's agricultural activities, but also provides personalized support that is attuned to the user's emotional state.

[0425] (Application example 2)

[0426] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0427] Conventional security systems collect environmental data and detect anomalies, but lack the ability to adjust alerts based on the emotional state of security guards. This makes it difficult for stressed security guards to respond quickly and accurately. The present invention aims to solve this problem by providing a system that realizes effective security responses that take emotional states into account.

[0428] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes sensor means for collecting environmental data, transmission means for transmitting data collected from the sensor means to the server via communication means, analysis means for analyzing the data and encouraging appropriate action, advice generation means for suggesting an action based on the analysis, notification means for notifying the user terminal of the advice, an emotion engine for acquiring user emotion data, advice adjustment means for adjusting the content of advice based on the emotion data, and feedback means for transmitting feedback data obtained from the user terminal to the server. This enables effective alert notification that takes into account the emotional state of the security guard.

[0429] "Environmental Data" refers to information about the environment, such as ambient temperature, humidity, movement, and sound.

[0430] "Sensor means" refers to a device or group of devices that collect environmental data in real time.

[0431] "Transmission means" refers to a device or function that transmits collected data to a server via a communication means.

[0432] "Analysis means" refers to the function of using the server's algorithms and programs to analyze collected data and determine abnormalities and necessary actions.

[0433] The "advice generation means" refers to a function that generates specific actions and suggestions for the user based on the results obtained by the analysis means.

[0434] The "notification means" refers to a function or device for transmitting the generated advice or warning to the user terminal.

[0435] "User terminal" refers to a device that can receive and operate information, such as a smart device or HMD used by security guards.

[0436] An "emotion engine" refers to a function or device that analyzes the user's emotional state from their voice and facial expressions, and acquires and transmits that data.

[0437] "Advice adjustment means" refers to a function that adjusts the content and expression of advice based on emotional data obtained from the emotion engine.

[0438] "Feedback means" refers to an interface or function for recording the actions performed by the user and the results thereof, and transmitting them to the server.

[0439] System Overview

[0440] This invention is a security system designed to support security guards. This system combines IoT sensors, 5G communications, AI analysis, and an emotion engine to easily realize anomaly monitoring and emotion recognition. This section explains the details of each component and how to combine them.

[0441] System configuration

[0442] Sensor Means

[0443] The system includes multiple IoT sensors (e.g., temperature, humidity, motion, and sound sensors) to collect environmental data. These sensors are installed in different locations within the facility and collect data in real time. The collected environmental data includes temperature, humidity, motion, and sound.

[0444] Transmission method

[0445] The environmental data collected by the sensors is sent to a server using 5G communication, which enables high-speed and stable data transmission.

[0446] server

[0447] The server is a central processing unit that receives, manages, and analyzes the environmental data sent to it. The environmental data is stored in a database within the server, and an analysis algorithm using an AI model (for example, TensorFlow) evaluates the data as an analysis method.

[0448] Analysis means

[0449] The analysis means analyzes the collected data to detect anomalies, for example, if a motion sensor detects suspicious activity within the facility, the data is flagged as an anomaly.

[0450] Advice Generation Method

[0451] Based on the analysis results, advice is generated that suggests appropriate actions to take. For example, when an abnormality is detected, an instruction such as "urgent action is required" is generated.

[0452] Notification means

[0453] The generated advice and warnings are sent to the user's device (smart glasses, head-mounted display, etc.) via a notification means, allowing security guards to receive prompt alerts.

[0454] Emotion Engine

[0455] The emotion engine is a device that analyzes emotions from the user's voice and facial expressions and sends the data to a server. For example, if the user is in a stressful state, the data is sent to the server.

[0456] Advice adjustment measures

[0457] The server adjusts the advice content based on the emotion data obtained from the emotion engine. For example, if a user is under stress, the server generates advice that includes encouraging words, providing advice in a way that is optimal for the user.

[0458] Feedback Methods

[0459] The actions taken by the user and their results are sent to the server via a feedback mechanism, which allows the server to use the accumulated feedback data for analysis and to improve the accuracy of the AI ​​model.

[0460] Specific examples

[0461] For example, if a motion sensor installed in a facility detects abnormal activity, the data is immediately sent to a server via 5G communication. The server analyzes the data and detects the abnormality, generating an alert stating that "emergency action is required." This alert is then sent to the user's device, such as smart glasses. At the same time, the emotion engine analyzes the facial expressions and voice of the security guard, and if it detects that the guard is under stress, the advice adjustment means generates advice including an encouraging message such as "Please stay calm." The user then takes action in accordance with the instructions and sends the results to the server using the feedback means. The server uses this feedback data in future analyses to improve the accuracy of the entire system.

[0462] Prompt Sentence Examples

[0463] "Please provide data to train an AI model for anomaly detection for a security monitoring system. Please output the data in the following format:

[0464] timestamp

[0465] Sensor ID

[0466] Sensor Value

[0467] Normal / abnormal label

[0468] As described above, the detailed description shows how components in a security system work together to function efficiently.

[0469] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0470] Step 1:

[0471] Sensors collect environmental data. Sensors (e.g., temperature, humidity, motion, and sound sensors) capture data in real time and store the data in internal memory. The input is the sensor reading (temperature, humidity, motion, sound, etc.) and the output is the collected environmental data.

[0472] Step 2:

[0473] The transmitting means transmits the collected environmental data to a server. The data collected from the sensor is transmitted to the server using 5G communication. The input is the environmental data collected by the sensor, and the output is the data transmitted to the server.

[0474] Step 3:

[0475] The server stores the received data in an analysis database. As soon as the server receives the data, it adds it to the database and uses it as basic data for analysis. The input is the environmental data sent from the transmission means, and the output is the data stored in the database.

[0476] Step 4:

[0477] The server's analytical means analyzes the data and detects anomalies. An AI model (using, for example, TensorFlow) evaluates the environmental data and detects abnormal behavior or conditions. The input is the environmental data stored in the database, and the output is the analysis result (normal / abnormal determination).

[0478] Step 5:

[0479] The advice generator proposes actions based on the analysis results. If an anomaly is detected, it generates appropriate countermeasures (e.g., emergency response instructions). The input is the server's analysis results, and the output is the proposed actions or warnings.

[0480] Step 6:

[0481] The notification means sends the advice to the user's terminal. The generated advice or warning is notified to the user's terminal, such as smart glasses or a head-mounted display. The input is the suggestion from the advice generation means, and the output is the advice / warning displayed on the user's terminal.

[0482] Step 7:

[0483] The emotion engine analyzes the user's emotional data. It acquires the user's voice and facial expression and evaluates their state using an emotion analysis algorithm. The input is the user's voice and facial expression data, and the output is the analyzed emotional data.

[0484] Step 8:

[0485] The advice adjustment means adjusts the advice based on the emotion data. It receives the emotion analysis results and optimizes the content and expression of the advice. The input is the emotion data from the emotion engine and the initial advice content, and the output is the adjusted advice.

[0486] Step 9:

[0487] The adjusted advice is displayed on the user's device. The adjusted content is displayed on the device, and the user confirms it and takes action. The input is the adjusted advice, and the output is the user's action.

[0488] Step 10:

[0489] The feedback means acquires the result of the user's action and transmits it to the server. The user provides feedback on the action and its result using an input device. The input is the result of the user's action, and the output is the feedback data transmitted to the server.

[0490] Step 11:

[0491] The server analyzes the feedback data and uses it for future analyses. The feedback data is stored in a database and reflected in the learning of the AI ​​model. The input is the sent feedback data, and the output is an updated analysis algorithm.

[0492] Through the above processing steps, the security system of the present invention functions effectively and can provide alert notifications and feedback that take into account the emotional state of the security guard.

[0493] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0494] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0495] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0496] [Second embodiment]

[0497] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0498] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0499] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0500] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0501] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0502] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0503] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0504] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0505] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0506] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0507] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0508] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0509] This invention is an agricultural support system that utilizes AI, IoT, and 5G technologies. This system collects data on crops and the surrounding environment, and proposes optimal agricultural actions to users based on the analysis results.

[0510] System configuration

[0511] The system consists of the following main components:

[0512] 1. Sensor means

[0513] It is an IoT sensor for collecting crop and environmental data (temperature, humidity, soil pH, water content, light intensity, etc.) in real time.

[0514] 2. Transmission Method

[0515] This is a communication device that transmits collected data to a server using 5G communication.

[0516] 3. Server

[0517] This is the central processing unit that receives, manages, and analyzes the transmitted data. The server stores the collected data and uses analytical algorithms to optimize agriculture.

[0518] 4. Analysis method

[0519] The server uses an AI-based analytical algorithm to analyze the collected data and evaluate the agricultural situation.

[0520] 5. Advice Generation Methods

[0521] Based on the analysis results, the system suggests agricultural actions and provides advice on optimal cultivation methods.

[0522] 6. Means of notification

[0523] This is a means for sending advice generated by the server to the user's terminal via 5G communication and notifying farmers.

[0524] 7. User Devices

[0525] The devices farmers use to receive advice and send feedback include smartphones and tablets.

[0526] 8. Feedback channels

[0527] This is an interface for users to input the agricultural actions they have performed and the results of those actions, and send them to the server.

[0528] Program processing

[0529] Data collection and transmission

[0530] The sensor means continuously collects environmental data from various sensors around the farm, such as soil pH, temperature, humidity, and water content.

[0531] The transmission means transmits the collected environmental data to a server in real time. 5G communication is used, enabling high-speed and stable data transmission.

[0532] Data analysis and advice generation

[0533] The server stores the newly received data in an analysis database.

[0534] The analytics tool uses AI models to analyze the data and assess the health of the crop and the agricultural actions needed, such as detecting increased risk of pests and diseases when humidity or temperature falls outside certain ranges.

[0535] The advice generator generates advice that suggests optimal agricultural actions based on the analysis results, such as "spray a specific pesticide in the right amount" or "add a specific fertilizer to adjust the soil pH."

[0536] Advice notifications and feedback collection

[0537] The notification means sends the generated advice to the user's device, and the user can check the notification on their smartphone or tablet.

[0538] The user terminal performs agricultural actions based on the notification, such as spraying specific pesticides or adding fertilizer.

[0539] The feedback means provides an interface for users to input the results of their agricultural actions and transmit them to the server, thereby allowing users to report the effectiveness of their actions.

[0540] Specific examples

[0541] For example, if crops are placed in a high-humidity environment during the rainy season, the sensor means will collect humidity data frequently. This data will then be immediately sent to the server using 5G communications. The server will analyze this data and detect that humidity may increase the risk of pest and disease outbreaks in the crops. Based on the analysis results, the advice generation means will suggest an appropriate pest control method (e.g., early application of a specific pesticide). A notification will be sent to the user's device, and the user will follow the instructions to quickly spray the pesticide, thereby preventing the outbreak of pests and diseases. The user will then input the results of their action into the feedback means and send it to the server. The server will use this feedback for analysis to improve the accuracy of advice from the next time onwards.

[0542] This will provide a coherent system that supports increased agricultural productivity and sustainability.

[0543] The processing flow will be explained below.

[0544] Step 1:

[0545] The sensor means collects real-time environmental data on the crops and their surroundings (temperature, humidity, soil pH, water content, light intensity, etc.). Specifically, each sensor measures at a set frequency and temporarily stores the data in its internal memory.

[0546] Step 2:

[0547] The transmitting means transmits the collected environmental data to the server using 5G communication. Specifically, after the sensor means collects a certain amount of data, it divides it into packets and transmits them to the server via the 5G network.

[0548] Step 3:

[0549] The server receives the data sent from the sensor means and stores it in an analysis database. Specifically, it checks the consistency of the received data and stores only accurate data in the database.

[0550] Step 4:

[0551] The analysis means inputs the received data into an AI model to analyze the condition of the crops and determine the optimal agricultural actions. Specifically, it detects fluctuation patterns in temperature and humidity and determines whether the crops are at risk.

[0552] Step 5:

[0553] The advice generator generates specific agricultural action suggestions based on the analysis results, for example, if the analysis results indicate soil acidification, it generates advice such as "add lime."

[0554] Step 6:

[0555] The notification means notifies the user terminal of the generated advice. Specifically, the advice content is sent as a push notification to a smartphone or tablet so that the user can immediately check it.

[0556] Step 7:

[0557] The user device receives and displays the notification, allowing the user to review the specific agricultural action recommendations.

[0558] Step 8:

[0559] The user performs agricultural actions based on the advice presented on the user terminal. For example, if the advice includes "add fertilizer," the user selects an appropriate fertilizer and applies the recommended amount to the farmland.

[0560] Step 9:

[0561] The feedback means provides an interface for inputting the results of the agricultural actions performed by the user, and the user inputs the details of the work that he or she actually performed and the results of the work into this interface.

[0562] Step 10:

[0563] The user terminal transmits the input feedback data to the server. Specifically, the feedback data is packetized and transmitted to the server via 5G communication.

[0564] Step 11:

[0565] The server analyzes the received feedback data and uses it to improve the accuracy of the analytical model. The server stores the feedback data in a database and uses it to train the AI ​​model, improving the accuracy of advice from the next time onwards.

[0566] The above steps will create a system that continuously monitors the health of crops and their growing environment and suggests optimal agricultural actions.

[0567] Example 1

[0568] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0569] Conventional agricultural support systems have had problems with the collection and analysis of environmental data and the proposal of appropriate agricultural actions not being done quickly enough and accurately. In particular, manual data collection and analysis takes time, often resulting in delays in appropriate responses. Another issue is that data feedback is not effectively utilized, making it difficult to improve the accuracy of future advice.

[0570] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0571] In this invention, the server includes a sensor device that collects environmental data, a transmission device that transmits the data collected from the sensor device to a central processing unit via a communication device, an analysis device that analyzes the data and encourages appropriate agricultural actions, an advice generation device that suggests agricultural actions based on the analysis, a notification device that notifies a user terminal of the advice, and a feedback device that transmits feedback data obtained from the user terminal to the central processing unit. This enables rapid and accurate collection and analysis of environmental data, rapid suggestion of appropriate agricultural actions, and effective use of feedback.

[0572] A "sensor device" is a device that collects environmental data such as temperature, humidity, soil pH, water content, and light intensity.

[0573] A "communication device" is a device for transmitting collected environmental data, and may include, for example, a fifth generation mobile communication system (5G).

[0574] The "central processing unit" is a device for analyzing and managing received environmental data.

[0575] The "transmitting device" is a device for transmitting data collected from the sensor device to the central processing unit via the communication device.

[0576] "Analysis Device" means a device used by the Central Processing Unit to perform the necessary analysis, such as analyzing data using AI models or machine learning algorithms.

[0577] The "advice generator" is a device for suggesting appropriate agricultural actions based on the analysis results.

[0578] The "notification device" is a device for notifying the generated advice to the user terminal.

[0579] A "feedback device" is a device for transmitting feedback data obtained from a user terminal to a central processing unit.

[0580] A "user terminal" is a device that allows a user to receive advice and send feedback, and includes a smartphone, tablet, or the like.

[0581] This invention is an agricultural support system that utilizes AI, IoT, and 5G technologies. This system collects data on agricultural crops and the surrounding environment, and proposes optimal agricultural actions to users based on the analysis results. The specific hardware and software configurations and their operation are described below.

[0582] System configuration

[0583] The system consists of the following main components:

[0584] 1. Sensor device

[0585] These IoT sensors collect crop and environmental data (temperature, humidity, soil pH, water content, light intensity, etc.) in real time, enabling detailed environmental information within the farm to be obtained. Specifically, they include soil humidity and temperature sensors.

[0586] 2. Communications Equipment

[0587] This is a communications device that transmits collected data to a central processing unit using 5G communications, enabling fast and reliable data transmission.

[0588] 3. Central Processing Unit

[0589] This is a central processing unit that receives, manages, and analyzes the transmitted data. Specifically, a database is placed on the server and used to store and analyze various data. The server stores the collected data and uses analytical algorithms to optimize agriculture.

[0590] 4. Analysis device

[0591] The server uses AI-based analytical algorithms to analyze the collected data and evaluate the agricultural situation. This allows for the determination of the health of crops and necessary agricultural actions based on environmental data. Specifically, the analysis is carried out using machine learning models and neural networks.

[0592] 5. Advice Generator

[0593] Based on the analysis results, the system proposes agricultural actions and provides advice on optimal cultivation methods, such as "spray specific pesticides in the right amounts" or "add specific fertilizer to adjust the soil pH."

[0594] 6. Notification device

[0595] This device sends and notifies the user of advice generated by the server via 5G communication, enabling real-time notifications. Users can receive advice on their smartphones, tablets, or other devices.

[0596] 7. Feedback Devices

[0597] This is a device for transmitting feedback data obtained from user terminals to the central processing unit. Users input the agricultural actions they performed and their results, and send this to the server.

[0598] Specific examples

[0599] For example, if crops are placed in a high-humidity environment during the rainy season, the sensor device will collect humidity data frequently. This data will then be immediately sent to a central processing unit using 5G communications. The server will then analyze the data and detect that humidity may increase the risk of pest infestation in the crops. Based on the analysis results, the advice generation device will suggest an appropriate pest control method (e.g., early application of a specific pesticide). A notification will be sent to the user's device, and the user will follow the instructions to quickly spray the pesticide, thereby preventing the occurrence of pests and diseases. The user will then input the results of their actions into a feedback device and send it to the server. The server will use this feedback for analysis to improve the accuracy of future advice.

[0600] Prompt Sentence Examples

[0601] Below is an example of a prompt sentence to input to a generative AI model (e.g., ChatGPT).

[0602] Please explain the following agricultural support systems that utilize AI, IoT, and 5G technologies:

[0603] The sensor collects temperature and humidity data and transmits it to a server via 5G communication.

[0604] The server analyzes the received data and evaluates the health of the crops.

[0605] If necessary, it generates suggestions for agricultural actions (e.g., spraying pesticides) and notifies the user.

[0606] After the user performs an action, the result is given as feedback.

[0607] Please explain with specific examples.

[0608] The above is an embodiment of the invention.

[0609] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0610] Program processing

[0611] Step 1: Data collection

[0612] Subject: Sensor device

[0613] The sensor device collects environmental data such as temperature, humidity, soil pH, water content, and light intensity using various sensors placed around the farm.

[0614] Input: Environmental data on soil and air temperature, humidity, pH, and light intensity

[0615] Data processing and calculation: The sensor measurement values ​​are temporarily stored in the memory of the sensor device.

[0616] Output: Temporarily saved environmental data

[0617] Specific operation: The humidity sensor measures the humidity at 80% and records the data in memory.

[0618] Step 2: Send data

[0619] Subject: Communication device

[0620] The communication device transmits data collected from the sensor device to the central processing unit in real time, using 5G communication to achieve high-speed and stable data transmission.

[0621] Input: Temporarily saved environmental data (e.g. humidity 80%)

[0622] Data processing and calculation: Data is transmitted to the central processing unit using the 5G communication module.

[0623] Output: Environmental data received by the server

[0624] Specific operation: Humidity data (80%) is sent to the server via 5G communication.

[0625] Step 3: Save Data

[0626] Subject: Server

[0627] The server stores the received environmental data in an analysis database.

[0628] Input: Received environmental data (humidity 80%)

[0629] Data processing and calculation: Register environmental data in the database.

[0630] Output: Data stored in the database

[0631] Specific operation: The server saves the humidity data (80%) in the database.

[0632] Step 4: Data analysis

[0633] Subject: Server

[0634] The server uses AI models (e.g., machine learning algorithms) to analyze the data, which is then used by the analysis device to assess the health of the crops and the agricultural actions required.

[0635] Input: Environmental data stored in a database

[0636] Data processing and calculation: Data analysis is performed using AI models and machine learning algorithms.

[0637] Output: Analysis results (indicating that high humidity increases the risk of pests)

[0638] Specific operation: The server performs an analysis and determines that the humidity level of 80% is outside a specific range, increasing the risk of pests and diseases.

[0639] Step 5: Advice Generation

[0640] Subject: Server

[0641] The server generates appropriate agricultural actions based on the analysis results, and the advice generator creates specific advice and recommends it to the user.

[0642] Input: Analysis results (increase in pest risk)

[0643] Data processing and calculation: Based on the analysis results, appropriate agricultural actions (e.g., spraying pesticides) are generated.

[0644] Output: Generated advice

[0645] Specific operation: The server generates advice such as "Because the humidity is high, please spray 5 liters of a specific pesticide (brand name: XY)."

[0646] Step 6: Advice Notification

[0647] Subject: Server

[0648] The server passes the generated advice to the notification device, which then transmits the advice to the user device using 5G communications. The user device then displays the advice and notifies the user.

[0649] Input: Generated advice

[0650] Data processing and calculation: Advice is sent using 5G communication.

[0651] Output: Advice displayed on the user's terminal

[0652] Specific operation: The server sends advice to the smartphone, which then displays a notification saying, "Due to high humidity, please spray 5 liters of pesticide XY."

[0653] Step 7: Take Action

[0654] Subject: User

[0655] The user performs agricultural actions based on the notified advice.

[0656] Input: Received advice

[0657] Data processing and calculation: Implement specific agricultural actions based on the advice.

[0658] Output: Farming actions performed

[0659] Specific behavior: The user receives a notification on their smartphone and sprays 5 liters of pesticide XY.

[0660] Step 8: Gather feedback

[0661] Subject: User

[0662] The user inputs the agricultural actions they have performed and their results into the user terminal, and the feedback device transmits the input feedback to the server.

[0663] Input: The result of the agricultural action performed

[0664] Data processing and calculation: The results are entered into the user's terminal and sent to the server.

[0665] Output: Feedback data sent to the server

[0666] Specific operation: The user types "Pesticide XY was sprayed" into their smartphone and sends it.

[0667] Step 9: Use feedback

[0668] Subject: Server

[0669] The server adds the received feedback to the analysis data and uses it to improve the accuracy of advice from the next time onwards.

[0670] Input: Feedback data

[0671] Data processing and calculation: Feedback is reflected in the analysis and the database is updated.

[0672] Output: Improved advice for next time

[0673] Specific operation: The server uses the feedback that "after spraying pesticide XY, the humidity returned to the appropriate value" for analysis, and improves the accuracy of mold prevention advice from the next time onwards.

[0674] The above are the specific steps and details of the system's program processing.

[0675] (Application example 1)

[0676] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0677] Conventional factory automation systems have issues with real-time response and accuracy when it comes to detecting equipment anomalies and proposing maintenance. It's also difficult to quickly notify appropriate actions, making it impossible to maximize production efficiency and equipment lifespan. This leads to unnecessary downtime and excessive maintenance, which increases production costs and increases the burden on workers.

[0678] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0679] In this invention, the server includes a sensor means for collecting environmental data, a transmission means for transmitting the data collected from the sensor means to a central processing unit via a communication means, an analysis means for analyzing the data and promoting appropriate manufacturing processes and maintenance actions, an advice generation means for proposing manufacturing processes and maintenance actions based on the analysis, a notification means for notifying a user device of the advice, and a feedback means for transmitting feedback data obtained from the user device to the central processing unit. This enables real-time data collection within the factory and highly accurate analysis using AI to propose optimal actions. Furthermore, the use of 5G communications enables high-speed data transmission, allowing for prompt maintenance and process adjustments as needed. This minimizes equipment downtime and improves production efficiency.

[0680] "Environmental data" refers to data related to the machinery and working environment within the factory, such as temperature, humidity, and vibration.

[0681] "Sensor means" refers to devices and techniques for collecting environmental data in real time.

[0682] "Communication Means" refers to devices and technologies for transmitting data collected from the Sensor Means, including in particular those utilizing fifth generation mobile communication systems (5G).

[0683] "Transmitting Means" refers to devices and techniques for transmitting environmental data from the Sensor Means to the Central Processing Unit.

[0684] "Central Processing Unit" means a computer system that receives, stores, and analyzes collected data.

[0685] "Analysis Tools" refers to devices and algorithms used to analyze collected environmental data and identify appropriate manufacturing process or maintenance actions.

[0686] "Advice generation means" refers to devices and techniques that generate suggestions for manufacturing processes and maintenance actions based on the analysis results obtained by the analysis means.

[0687] "Notification means" refers to devices and techniques for transmitting and notifying the user device of the suggestions generated by the advice generation means.

[0688] A "user device" is a device on which a user receives notifications, and includes a smartphone or tablet.

[0689] "Feedback means" refers to devices and techniques for transmitting feedback data obtained from user devices to a central processing unit.

[0690] This invention is a system that collects data on equipment and the environment within a factory, analyzes that data, and proposes optimal manufacturing processes and maintenance actions. The system consists of the following main components:

[0691] System configuration

[0692] The system consists of the following main components:

[0693] 1. Sensor means

[0694] The sensor means is a device or technology for collecting environmental data such as temperature, humidity, and vibration in a factory in real time. This includes temperature sensors, humidity sensors, vibration sensors, etc.

[0695] 2. Transmission Method

[0696] The transmission means is a device and technology for transmitting data collected by the sensor means to the central processing unit, and uses 5G communication, enabling high-speed and stable data transmission.

[0697] 3. Central Processing Unit

[0698] The central processing unit is a computer system that receives, stores, and analyzes collected data. Specifically, it includes a database server and an AI analysis server.

[0699] 4. Analysis method

[0700] The analysis method is an analytical algorithm using artificial intelligence in the central processing unit, which analyzes the collected data and determines whether the manufacturing process or maintenance is necessary. TensorFlow and Keras are used for the analysis.

[0701] 5. Advice Generation Methods

[0702] The advice generator is a system that proposes optimal manufacturing processes and maintenance actions based on the results obtained from the analysis means, including appropriate maintenance timing and specific action plans.

[0703] 6. Means of notification

[0704] The notification means is a device or technology for transmitting the generated advice to a user device, such as a smartphone or tablet.

[0705] 7. Feedback channels

[0706] The feedback means is an interface for transmitting feedback data obtained from the user device to the central processing unit. By inputting the actions taken by the user and their results, the central processing unit uses this information for further analysis and optimization.

[0707] Program processing

[0708] Data collection and transmission

[0709] The sensor means continuously collects environmental data from each sensor inside the factory, such as temperature, humidity, vibration, etc. The transmission means transmits the collected environmental data to the central processing unit using 5G communication.

[0710] Data analysis and advice generation

[0711] The central processing unit stores newly received data in an analysis database. The analysis means analyzes the data using an AI model and evaluates the health of the equipment and the necessary manufacturing and maintenance actions. Specifically, if vibrations exceed a certain threshold, it detects the high possibility that an abnormality has occurred. The advice generation means generates advice that suggests the optimal manufacturing and maintenance actions based on the analysis results.

[0712] Advice notifications and feedback collection

[0713] The notification means sends the generated advice to the user device and notifies it. The user can check the notification on a smartphone or tablet. The user then performs manufacturing or maintenance actions based on the notification. Specifically, this could include performing machine maintenance or adjusting process settings. The feedback means provides an interface that allows the user to input the results of the actions they have taken and send them to the central processing unit. This allows the effectiveness of the actions to be reported. The central processing unit uses this feedback for analysis and improves the accuracy of advice from the next time onwards.

[0714] Specific examples

[0715] For example, if a robot arm detects abnormal vibrations during production in a factory, the sensor means immediately collects and transmits the data. The central processing unit analyzes this data and, if it determines that machine maintenance is necessary, the advice generation means immediately suggests that "maintenance of this robot arm is necessary." The user receives the notification and promptly performs maintenance work according to the instructions. The results are entered into the user device and transmitted to the central processing unit as feedback data. This improves the accuracy of abnormality detection and maintenance actions from the next time onwards.

[0716] Example prompt for a generative AI model:

[0717] Design a system that collects and analyzes sensor data, specifically temperature, humidity, and vibration data, to propose optimal maintenance methods for equipment.

[0718] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0719] Step 1:

[0720] The sensor means collects environmental data such as temperature, humidity, and vibration in the factory in real time.

[0721] Specifically, each sensor measures data at a specified interval and temporarily stores the data in memory. The input is environmental data, and the output is the collected environmental data.

[0722] Step 2:

[0723] The transmitting means transmits the collected environmental data to the central processing unit using 5G communication.

[0724] Specifically, the data stored in the sensor's memory is converted into packets and sent to a central processing unit via a 5G modem. The input is the collected environmental data, and the output is the data sent to the central processing unit.

[0725] Step 3:

[0726] The central processing unit stores the newly received data in a database.

[0727] Specifically, the received data is written to an analysis database and a timestamp is added. The input is the data received via 5G communication, and the output is the data stored in the database.

[0728] Step 4:

[0729] The analytics tool uses AI models to analyze the data and assess the health of the equipment and any necessary production or maintenance actions.

[0730] Specifically, using a machine learning framework such as TensorFlow, collected data is input into a trained model to detect anomalies and identify necessary actions. The input is the data stored in the database, and the output is the analysis results.

[0731] Step 5:

[0732] The advice generation means generates advice that proposes optimal manufacturing and maintenance actions based on the analysis results.

[0733] Specifically, specific advice such as "This robot arm needs maintenance" is generated based on the analysis results. This identifies the necessary maintenance work and adjustments to the manufacturing process. The input is the analysis results, and the output is the generated advice.

[0734] Step 6:

[0735] The notification means transmits the generated advice to the user device to notify it.

[0736] Specifically, a messaging protocol is used via a notification server to notify the generated advice to the user's smartphone or tablet. The input is the generated advice, and the output is the notification to the user device.

[0737] Step 7:

[0738] Users can take production and maintenance actions based on notifications.

[0739] Specifically, the user checks the notification content and performs machine maintenance or process adjustments according to the instructions. The input is the notification received by the user device, and the output is the action taken.

[0740] Step 8:

[0741] A feedback means provides an interface for inputting and transmitting results of actions taken by the user to the central processing unit.

[0742] Specifically, the results of the actions taken by the user are input into the application, and the data is sent back to the central processing unit. The input is the result of the action taken, and the output is the feedback data sent to the central processing unit.

[0743] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0744] This invention is an agricultural support system that utilizes AI, IoT, 5G technology, and an emotion engine. It not only collects data on crops and the surrounding environment and suggests optimal agricultural actions to users based on the analysis results, but also recognizes the user's emotions and adjusts the content and presentation of advice to provide more effective agricultural support.

[0745] System configuration

[0746] The system consists of the following main components:

[0747] 1. Sensor means

[0748] It is an IoT sensor for collecting crop and environmental data (temperature, humidity, soil pH, water content, light intensity, etc.) in real time.

[0749] 2. Transmission Method

[0750] This is a communication device that transmits collected data to a server using 5G communication.

[0751] 3. Server

[0752] This is the central processing unit that receives, manages, and analyzes the transmitted data. The server stores the collected data and uses analytical algorithms to optimize agriculture.

[0753] 4. Analysis method

[0754] The server uses an AI-based analytical algorithm to analyze the collected data and evaluate the agricultural situation.

[0755] 5. Advice Generation Methods

[0756] Based on the analysis results, the system suggests agricultural actions and provides advice on optimal cultivation methods.

[0757] 6. Means of notification

[0758] This is a means for notifying the user terminal of the advice generated by the server and conveying it to farmers.

[0759] 7. User Devices

[0760] The devices farmers use to receive advice and send feedback include smartphones and tablets.

[0761] 8. Feedback channels

[0762] This is an interface for users to input the agricultural actions they have performed and the results of those actions, and send them to the server.

[0763] 9. Emotion Engine

[0764] This is a device that analyzes emotions from the user's voice and facial expressions and feeds the results back to the server.

[0765] System action

[0766] Data collection and transmission

[0767] The sensor means continuously collects environmental data from various sensors around the farm, such as soil pH, temperature, humidity, and water content.

[0768] The transmission means transmits the collected environmental data to the server using 5G communication, which enables high-speed and stable data transmission.

[0769] Data analysis and advice generation

[0770] The server stores the newly received data in an analysis database.

[0771] The analytics tool uses AI models to analyze the data and assess the health of the crops and the agricultural actions they need to take, such as detecting patterns of temperature and humidity fluctuations and determining whether the crops are at risk.

[0772] The advice generator generates advice that suggests optimal agricultural actions based on the analysis results, such as "spray a specific pesticide in the right amount" or "add a specific fertilizer to adjust the soil pH."

[0773] Advice Notification and Emotion Recognition

[0774] The notification means notifies the user terminal of the generated advice. Specifically, the advice content is sent as a push notification to a smartphone or tablet so that the user can immediately check it.

[0775] The emotion engine analyzes the user's voice and facial expressions and feeds back their emotional state to the server. For example, if the user is in a stressful state, that information is sent to the server.

[0776] Emotion-based advice adjustment

[0777] The server receives feedback from the emotion engine and adjusts the content and expression of advice in the advice generation means. For example, if the user is under stress, advice containing encouraging words is generated to increase the user's motivation.

[0778] Engaging with users and gathering feedback

[0779] The user device receives and displays the notification, allowing the user to review the specific agricultural action recommendations.

[0780] The user performs agricultural actions based on the advice presented on the user terminal. For example, if the advice includes "add fertilizer," the user selects an appropriate fertilizer and applies the recommended amount to the farmland.

[0781] The feedback means provides an interface for inputting the results of the agricultural actions performed by the user, and the user inputs the details of the work that he or she actually performed and the results of the work into this interface.

[0782] The user terminal transmits the input feedback data to the server. Specifically, the feedback data is packetized and transmitted to the server via 5G communication.

[0783] The server analyzes the received feedback data and uses it to improve the accuracy of the analytical model. The server stores the feedback data in a database and uses it to train the AI ​​model, improving the accuracy of advice from the next time onwards.

[0784] Specific examples

[0785] For example, if crops are placed in a high-humidity environment during the rainy season, the sensor means will collect humidity data frequently. This data will then be immediately sent to the server using 5G communications. The server will analyze this data and detect that humidity may increase the risk of pest infestation. Based on the analysis results, the advice generation means will suggest an appropriate pest control method (e.g., early application of a specific pesticide). A notification will be sent to the user's device, and the user will quickly follow the instructions to spray the pesticide. In addition, the emotion engine will analyze the user's voice and facial expressions. If the server detects that the user is under stress, the server will generate advice that includes encouraging words. The user then inputs the results of their actions into the feedback means and sends it to the server. The server will use this feedback for analysis to improve the accuracy of future advice.

[0786] This not only provides a consistent system that supports agricultural productivity and sustainability, but also responds to the user's emotional state, resulting in more effective agricultural support.

[0787] The processing flow will be explained below.

[0788] Step 1:

[0789] The sensor means collects real-time environmental data on the crops and their surroundings (temperature, humidity, soil pH, water content, light intensity, etc.). Each sensor periodically obtains its own measured value and temporarily stores the data in its internal memory.

[0790] Step 2:

[0791] The transmission means packetizes the collected environmental data and transmits it to the server using 5G communication, allowing the environmental data to be transmitted to the server at high speed and with high stability.

[0792] Step 3:

[0793] The server receives the environmental data sent from the sensor means and first checks its consistency. After the data consistency is confirmed, it stores it in a database and prepares it for analysis.

[0794] Step 4:

[0795] The analysis means stores the received data in an analytical database and inputs the data into an AI model, which compares it with past data to assess the current environmental conditions and the health of the crops. For example, it analyzes patterns of temperature and humidity fluctuations to predict whether the crops are at risk of pests or diseases.

[0796] Step 5:

[0797] The advice generation means generates specific agricultural action proposals based on the analysis results. For example, if humidity is high and the risk of a specific pest is high, the system generates specific advice such as "spray a specific pesticide in the appropriate amount."

[0798] Step 6:

[0799] The emotion engine recognizes emotions from the user's voice and facial expressions and sends the data to the server. The emotion engine determines the user's emotional state, such as stress or satisfaction, from their reaction.

[0800] Step 7:

[0801] The server analyzes the emotion data received from the emotion engine, and if the user is feeling stressed, feeds back that information to the advice generating means.

[0802] Step 8:

[0803] The advice generation means adjusts the content and expression of the advice based on the analysis results of the emotion engine. For example, if the user is in a high-stress state, the advice generation means generates advice that includes kinder expressions and encouraging words.

[0804] Step 9:

[0805] The notification means sends the generated advice to the user terminal, and the advice content is displayed on the user's smartphone or tablet via push notification.

[0806] Step 10:

[0807] The user terminal displays the received advice, and the user confirms it and plans agricultural actions based on the advice.

[0808] Step 11:

[0809] The user follows the advice displayed on the user terminal and performs specific agricultural actions. For example, if the advice is to "spray a specific pesticide in the appropriate amount," the user will spray the indicated amount of pesticide on the field.

[0810] Step 12:

[0811] The feedback means provides an interface for inputting the results of the agricultural actions performed by the user. The user inputs the details of the work actually performed and the results, and saves them in the terminal.

[0812] Step 13:

[0813] The user terminal packetizes the input feedback data and transmits it again to the server via 5G communication.

[0814] Step 14:

[0815] The server analyzes the received feedback data and uses it to improve the accuracy of the analytical model. The feedback data is stored in a database and used to train the AI ​​model, improving the accuracy of advice from the next time onwards.

[0816] This will enable a more effective agricultural support system that not only continuously monitors the health and growth environment of crops and suggests optimal agricultural actions, but also responds to the user's emotional state.

[0817] Example 2

[0818] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0819] In modern agriculture, the collection and analysis of environmental data to propose appropriate agricultural actions is extremely important. However, conventional systems do not adequately analyze the collected data or generate advice, and in particular lack support that takes into account the user's emotional state. This reduces user motivation and makes it difficult to provide effective agricultural support. Furthermore, delays in real-time data transmission and analysis can lead to reduced productivity and increased agricultural risks. Effective methods to resolve these issues are needed.

[0820] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a sensor means for collecting environmental data, a transmission means for transmitting data collected from the sensor means to the server via a communication means, an analysis means for analyzing the data and encouraging appropriate agricultural actions, an advice generation means for suggesting agricultural actions based on the analysis, a notification means for notifying the user terminal of the advice, an emotion analysis means for analyzing the emotional state of the user, an emotion adaptation means for adjusting the content and expression of the advice based on the results of the emotion analysis means, and a feedback means for transmitting feedback data obtained from the user terminal to the server. This enables effective agricultural support that takes the user's emotional state into consideration, and by realizing data transmission and analysis in real time, agricultural productivity and efficiency are significantly improved.

[0821] A "sensor means" is a device for collecting environmental data.

[0822] The "transmitting means" is a device for transmitting data collected from the sensor means to the server via the communication means.

[0823] The "analysis means" is a device that analyzes the received data and encourages appropriate agricultural actions.

[0824] The "advice generator" is a device for suggesting agricultural actions based on the results of the analysis means.

[0825] The "notification means" is a device for notifying the user terminal of the generated advice.

[0826] The "emotion analysis means" is a device for analyzing the user's emotional state.

[0827] The "emotion adaptation means" is a device for adjusting the content and expression of advice based on the results of the emotion analysis means.

[0828] The "feedback means" is a device for transmitting feedback data obtained from a user terminal to a server.

[0829] "Communication medium" is the technology used to send and receive data.

[0830] A "user terminal" is a device through which a user receives advice and sends feedback.

[0831] Basic system configuration

[0832] The system utilizes AI, IoT, 5G technology, and an emotion engine to collect data on crops and the surrounding environment, and then suggests optimal agricultural actions to users based on the analysis results.It also analyzes the user's emotional state and adjusts the content and presentation of advice to provide effective agricultural support.

[0833] Specifically, the following hardware and software are used.

[0834] Main hardware used:

[0835] IoT sensors: temperature sensors, humidity sensors, soil pH sensors, moisture sensors, light intensity sensors, etc.

[0836] Communications equipment: 5G-compatible modems and routers.

[0837] Server: The central processing unit responsible for collecting, analyzing, storing data and generating advice.

[0838] User device: A device such as a smartphone or tablet that allows a user to receive advice and send feedback.

[0839] Emotion engine: Camera and microphone for analyzing the user's voice and facial expressions.

[0840] Main software used:

[0841] AI model: The machine learning algorithm used to analyze data and generate advice.

[0842] Database software: NoSQL database, e.g. MongoDB.

[0843] Analysis algorithms: Software that analyzes environmental data such as temperature, humidity, soil pH, water content, and light intensity to assess crop health and any necessary agricultural actions.

[0844] Notification software: A mobile application for sending push notifications.

[0845] System operation example

[0846] For example, if crops are placed in a high-humidity environment during the rainy season, the sensor will collect humidity data frequently. The device will then transmit the data obtained from the humidity sensor to a server via 5G communication. The server will then receive the data and store it in a database.

[0847] The server uses an AI analysis model to analyze humidity data and detect whether humidity can increase the risk of pests and diseases in crops. Based on the analysis results, the advice generation means generates advice suggesting appropriate pest control methods (e.g., early application of a specific pesticide).

[0848] The generated advice is sent as a push notification to the user's smartphone or tablet via the notification means. The user receives the advice and sprays the pesticide as instructed. The emotion engine analyzes the user's voice and facial expression, and if it determines that the user is under stress, the server generates advice including words of encouragement.

[0849] Example prompt sentence:

[0850] "We've detected that the user is under stress. Please generate friendly, encouraging advice."

[0851] The user then sends the results of their agricultural actions to the server via a feedback mechanism. The server analyzes this feedback data, stores it in a database, and uses it as training data to improve the accuracy of advice in future.

[0852] effect

[0853] This system enables real-time, highly accurate data analysis and provides personalized agricultural support that takes into account the user's emotional state, thereby improving agricultural productivity and efficiency and enabling sustainable agricultural practices.

[0854] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0855] Step 1: Data collection

[0856] The terminal (sensor means) collects environmental data within the farm in real time. The sensor means uses temperature sensors, humidity sensors, soil pH sensors, moisture sensors, light intensity sensors, etc. to obtain data on temperature, humidity, soil pH, moisture content, light intensity, etc. For example, the temperature sensor measures 25°C, and the humidity sensor measures 80%.

[0857] Input: Environmental data

[0858] Output: Raw data from the sensor

[0859] Step 2: Send data

[0860] The environmental data collected by the device is sent to a server using 5G communication. Specifically, the data obtained from each sensor is packaged in JSON format and sent over the 5G network. For example, data such as {"temperature": 25, "humidity": 80} is sent to the server.

[0861] Input: Raw data from the sensor

[0862] Output: JSON data sent to the server via the communication method

[0863] Step 3: Data reception and storage

[0864] The server receives the data sent from the device and stores it in a database for analysis. For example, the server's API receives JSON data and executes a process to store it in a NoSQL database.

[0865] Input: JSON data sent via communication means

[0866] Output: Environmental data stored in a database

[0867] Step 4: Data analysis

[0868] The server analyzes the data using an AI analysis model. For example, it determines that the risk of pests and diseases increases when humidity levels remain high. The server compares this data with past data and makes a risk assessment. Specifically, it analyzes temperature and humidity patterns to assess the health of the crops.

[0869] Input: Environmental data stored in a database

[0870] Output: Analysis results (risk of pests, etc.)

[0871] Step 5: Advice Generation

[0872] The server generates advice based on the analysis results. For example, it generates specific instructions such as "Because humidity is high, spray a specific pesticide now." The advice generation means proposes optimal agricultural actions based on the analysis results.

[0873] Input: Analysis results

[0874] Output: Specific advice

[0875] Step 6: Advice Notification

[0876] The server sends the generated advice to the user's device. The advice is sent to the user in real time using push notifications. For example, a notification saying "Please spray pesticides now" is displayed on the smartphone.

[0877] Input: Specific advice

[0878] Output: Notification to user terminal

[0879] Step 7: User sentiment analysis

[0880] The emotion engine analyzes the user's voice and facial expression and transmits the user's emotional state to the server. For example, the emotion engine determines that the user is feeling stressed based on the user's tone of voice and facial expression.

[0881] Input: Audio and visual data

[0882] Output: Parsed emotion data

[0883] Step 8: Adjusting Advice Based on Emotions

[0884] The server adjusts the content and expression of the advice based on the results of emotion analysis. For example, for a user who is under stress, it adds an encouraging message such as, "Thank you for your hard work. Please take a short break before proceeding to the next step."

[0885] Input: Parsed emotion data

[0886] Output: Adjusted advice

[0887] Step 9: User Actions and Feedback Collection

[0888] The user receives the notification and takes action based on the advice, for example, spraying a specific pesticide as instructed. The feedback means provides an interface that records the actions taken by the user, and the user inputs the results, such as "the pesticide has been sprayed."

[0889] Input: User action result

[0890] Output: Feedback data

[0891] Step 10: Sending and using feedback data

[0892] The user device sends the recorded feedback data to a server via 5G communication. The server receives the feedback data and uses it for analysis. The server uses the feedback data to train the AI ​​model and improve the accuracy of advice from the next time onwards.

[0893] Input: Feedback data sent from the user device

[0894] Output: Updated analytical model

[0895] By repeatedly performing these steps, the system not only continues to support the user's agricultural activities, but also provides personalized support that is attuned to the user's emotional state.

[0896] (Application example 2)

[0897] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0898] Conventional security systems collect environmental data and detect anomalies, but lack the ability to adjust alerts based on the emotional state of security guards. This makes it difficult for stressed security guards to respond quickly and accurately. The present invention aims to solve this problem by providing a system that realizes effective security responses that take emotional states into account.

[0899] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes sensor means for collecting environmental data, transmission means for transmitting data collected from the sensor means to the server via communication means, analysis means for analyzing the data and encouraging appropriate action, advice generation means for suggesting an action based on the analysis, notification means for notifying the user terminal of the advice, an emotion engine for acquiring user emotion data, advice adjustment means for adjusting the content of advice based on the emotion data, and feedback means for transmitting feedback data obtained from the user terminal to the server. This enables effective alert notification that takes into account the emotional state of the security guard.

[0900] "Environmental Data" refers to information about the environment, such as ambient temperature, humidity, movement, and sound.

[0901] "Sensor means" refers to a device or group of devices that collect environmental data in real time.

[0902] "Transmission means" refers to a device or function that transmits collected data to a server via a communication means.

[0903] "Analysis means" refers to the function of using the server's algorithms and programs to analyze collected data and determine abnormalities and necessary actions.

[0904] The "advice generation means" refers to a function that generates specific actions and suggestions for the user based on the results obtained by the analysis means.

[0905] The "notification means" refers to a function or device for transmitting the generated advice or warning to the user terminal.

[0906] "User terminal" refers to a device that can receive and operate information, such as a smart device or HMD used by security guards.

[0907] An "emotion engine" refers to a function or device that analyzes the user's emotional state from their voice and facial expressions, and acquires and transmits that data.

[0908] "Advice adjustment means" refers to a function that adjusts the content and expression of advice based on emotional data obtained from the emotion engine.

[0909] "Feedback means" refers to an interface or function for recording the actions performed by the user and the results thereof, and transmitting them to the server.

[0910] System Overview

[0911] This invention is a security system designed to support security guards. This system combines IoT sensors, 5G communications, AI analysis, and an emotion engine to easily realize anomaly monitoring and emotion recognition. This section explains the details of each component and how to combine them.

[0912] System configuration

[0913] Sensor Means

[0914] The system includes multiple IoT sensors (e.g., temperature, humidity, motion, and sound sensors) to collect environmental data. These sensors are installed in different locations within the facility and collect data in real time. The collected environmental data includes temperature, humidity, motion, and sound.

[0915] Transmission method

[0916] The environmental data collected by the sensors is sent to a server using 5G communication, which enables high-speed and stable data transmission.

[0917] server

[0918] The server is a central processing unit that receives, manages, and analyzes the environmental data sent to it. The environmental data is stored in a database within the server, and an analysis algorithm using an AI model (for example, TensorFlow) evaluates the data as an analysis method.

[0919] Analysis means

[0920] The analysis means analyzes the collected data to detect anomalies, for example, if a motion sensor detects suspicious activity within the facility, the data is flagged as an anomaly.

[0921] Advice Generation Method

[0922] Based on the analysis results, advice is generated that suggests appropriate actions to take. For example, when an abnormality is detected, an instruction such as "urgent action is required" is generated.

[0923] Notification means

[0924] The generated advice and warnings are sent to the user's device (smart glasses, head-mounted display, etc.) via a notification means, allowing security guards to receive prompt alerts.

[0925] Emotion Engine

[0926] The emotion engine is a device that analyzes emotions from the user's voice and facial expressions and sends the data to a server. For example, if the user is in a stressful state, the data is sent to the server.

[0927] Advice adjustment measures

[0928] The server adjusts the advice content based on the emotion data obtained from the emotion engine. For example, if a user is under stress, the server generates advice that includes encouraging words, providing advice in a way that is optimal for the user.

[0929] Feedback Methods

[0930] The actions taken by the user and their results are sent to the server via a feedback mechanism, which allows the server to use the accumulated feedback data for analysis and to improve the accuracy of the AI ​​model.

[0931] Specific examples

[0932] For example, if a motion sensor installed in a facility detects abnormal activity, the data is immediately sent to a server via 5G communication. The server analyzes the data and detects the abnormality, generating an alert stating that "emergency action is required." This alert is then sent to the user's device, such as smart glasses. At the same time, the emotion engine analyzes the facial expressions and voice of the security guard, and if it detects that the guard is under stress, the advice adjustment means generates advice including an encouraging message such as "Please stay calm." The user then takes action in accordance with the instructions and sends the results to the server using the feedback means. The server uses this feedback data in future analyses to improve the accuracy of the entire system.

[0933] Prompt Sentence Examples

[0934] "Please provide data to train an AI model for anomaly detection for a security monitoring system. Please output the data in the following format:

[0935] timestamp

[0936] Sensor ID

[0937] Sensor Value

[0938] Normal / abnormal label

[0939] As described above, the detailed description shows how components in a security system work together to function efficiently.

[0940] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0941] Step 1:

[0942] Sensors collect environmental data. Sensors (e.g., temperature, humidity, motion, and sound sensors) capture data in real time and store the data in internal memory. The input is the sensor reading (temperature, humidity, motion, sound, etc.) and the output is the collected environmental data.

[0943] Step 2:

[0944] The transmitting means transmits the collected environmental data to a server. The data collected from the sensor is transmitted to the server using 5G communication. The input is the environmental data collected by the sensor, and the output is the data transmitted to the server.

[0945] Step 3:

[0946] The server stores the received data in an analysis database. As soon as the server receives the data, it adds it to the database and uses it as basic data for analysis. The input is the environmental data sent from the transmission means, and the output is the data stored in the database.

[0947] Step 4:

[0948] The server's analytical means analyzes the data and detects anomalies. An AI model (using, for example, TensorFlow) evaluates the environmental data and detects abnormal behavior or conditions. The input is the environmental data stored in the database, and the output is the analysis result (normal / abnormal determination).

[0949] Step 5:

[0950] The advice generator proposes actions based on the analysis results. If an anomaly is detected, it generates appropriate countermeasures (e.g., emergency response instructions). The input is the server's analysis results, and the output is the proposed actions or warnings.

[0951] Step 6:

[0952] The notification means sends the advice to the user's terminal. The generated advice or warning is notified to the user's terminal, such as smart glasses or a head-mounted display. The input is the suggestion from the advice generation means, and the output is the advice / warning displayed on the user's terminal.

[0953] Step 7:

[0954] The emotion engine analyzes the user's emotional data. It acquires the user's voice and facial expression and evaluates their state using an emotion analysis algorithm. The input is the user's voice and facial expression data, and the output is the analyzed emotional data.

[0955] Step 8:

[0956] The advice adjustment means adjusts the advice based on the emotion data. It receives the emotion analysis results and optimizes the content and expression of the advice. The input is the emotion data from the emotion engine and the initial advice content, and the output is the adjusted advice.

[0957] Step 9:

[0958] The adjusted advice is displayed on the user's device. The adjusted content is displayed on the device, and the user confirms it and takes action. The input is the adjusted advice, and the output is the user's action.

[0959] Step 10:

[0960] The feedback means acquires the result of the user's action and transmits it to the server. The user provides feedback on the action and its result using an input device. The input is the result of the user's action, and the output is the feedback data transmitted to the server.

[0961] Step 11:

[0962] The server analyzes the feedback data and uses it for future analyses. The feedback data is stored in a database and reflected in the learning of the AI ​​model. The input is the sent feedback data, and the output is an updated analysis algorithm.

[0963] Through the above processing steps, the security system of the present invention functions effectively and can provide alert notifications and feedback that take into account the emotional state of the security guard.

[0964] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0965] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0966] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0967] [Third embodiment]

[0968] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0969] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0970] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0971] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0972] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0973] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0974] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0975] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0976] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0977] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0978] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0979] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0980] This invention is an agricultural support system that utilizes AI, IoT, and 5G technologies. This system collects data on crops and the surrounding environment, and proposes optimal agricultural actions to users based on the analysis results.

[0981] System configuration

[0982] The system consists of the following main components:

[0983] 1. Sensor means

[0984] It is an IoT sensor for collecting crop and environmental data (temperature, humidity, soil pH, water content, light intensity, etc.) in real time.

[0985] 2. Transmission Method

[0986] This is a communication device that transmits collected data to a server using 5G communication.

[0987] 3. Server

[0988] This is the central processing unit that receives, manages, and analyzes the transmitted data. The server stores the collected data and uses analytical algorithms to optimize agriculture.

[0989] 4. Analysis method

[0990] The server uses an AI-based analytical algorithm to analyze the collected data and evaluate the agricultural situation.

[0991] 5. Advice Generation Methods

[0992] Based on the analysis results, the system suggests agricultural actions and provides advice on optimal cultivation methods.

[0993] 6. Means of notification

[0994] This is a means for sending advice generated by the server to the user's terminal via 5G communication and notifying farmers.

[0995] 7. User Devices

[0996] The devices farmers use to receive advice and send feedback include smartphones and tablets.

[0997] 8. Feedback channels

[0998] This is an interface for users to input the agricultural actions they have performed and the results of those actions, and send them to the server.

[0999] Program processing

[1000] Data collection and transmission

[1001] The sensor means continuously collects environmental data from various sensors around the farm, such as soil pH, temperature, humidity, and water content.

[1002] The transmission means transmits the collected environmental data to a server in real time. 5G communication is used, enabling high-speed and stable data transmission.

[1003] Data analysis and advice generation

[1004] The server stores the newly received data in an analysis database.

[1005] The analytics tool uses AI models to analyze the data and assess the health of the crop and the agricultural actions needed, such as detecting increased risk of pests and diseases when humidity or temperature falls outside certain ranges.

[1006] The advice generator generates advice that suggests optimal agricultural actions based on the analysis results, such as "spray a specific pesticide in the right amount" or "add a specific fertilizer to adjust the soil pH."

[1007] Advice notifications and feedback collection

[1008] The notification means sends the generated advice to the user's device, and the user can check the notification on their smartphone or tablet.

[1009] The user terminal performs agricultural actions based on the notification, such as spraying specific pesticides or adding fertilizer.

[1010] The feedback means provides an interface for users to input the results of their agricultural actions and transmit them to the server, thereby allowing users to report the effectiveness of their actions.

[1011] Specific examples

[1012] For example, if crops are placed in a high-humidity environment during the rainy season, the sensor means will collect humidity data frequently. This data will then be immediately sent to the server using 5G communications. The server will analyze this data and detect that humidity may increase the risk of pest and disease outbreaks in the crops. Based on the analysis results, the advice generation means will suggest an appropriate pest control method (e.g., early application of a specific pesticide). A notification will be sent to the user's device, and the user will follow the instructions to quickly spray the pesticide, thereby preventing the outbreak of pests and diseases. The user will then input the results of their action into the feedback means and send it to the server. The server will use this feedback for analysis to improve the accuracy of advice from the next time onwards.

[1013] This will provide a coherent system that supports increased agricultural productivity and sustainability.

[1014] The processing flow will be explained below.

[1015] Step 1:

[1016] The sensor means collects real-time environmental data on the crops and their surroundings (temperature, humidity, soil pH, water content, light intensity, etc.). Specifically, each sensor measures at a set frequency and temporarily stores the data in its internal memory.

[1017] Step 2:

[1018] The transmitting means transmits the collected environmental data to the server using 5G communication. Specifically, after the sensor means collects a certain amount of data, it divides it into packets and transmits them to the server via the 5G network.

[1019] Step 3:

[1020] The server receives the data sent from the sensor means and stores it in an analysis database. Specifically, it checks the consistency of the received data and stores only accurate data in the database.

[1021] Step 4:

[1022] The analysis means inputs the received data into an AI model to analyze the condition of the crops and determine the optimal agricultural actions. Specifically, it detects fluctuation patterns in temperature and humidity and determines whether the crops are at risk.

[1023] Step 5:

[1024] The advice generator generates specific agricultural action suggestions based on the analysis results, for example, if the analysis results indicate soil acidification, it generates advice such as "add lime."

[1025] Step 6:

[1026] The notification means notifies the user terminal of the generated advice. Specifically, the advice content is sent as a push notification to a smartphone or tablet so that the user can immediately check it.

[1027] Step 7:

[1028] The user device receives and displays the notification, allowing the user to review the specific agricultural action recommendations.

[1029] Step 8:

[1030] The user performs agricultural actions based on the advice presented on the user terminal. For example, if the advice includes "add fertilizer," the user selects an appropriate fertilizer and applies the recommended amount to the farmland.

[1031] Step 9:

[1032] The feedback means provides an interface for inputting the results of the agricultural actions performed by the user, and the user inputs the details of the work that he or she actually performed and the results of the work into this interface.

[1033] Step 10:

[1034] The user terminal transmits the input feedback data to the server. Specifically, the feedback data is packetized and transmitted to the server via 5G communication.

[1035] Step 11:

[1036] The server analyzes the received feedback data and uses it to improve the accuracy of the analytical model. The server stores the feedback data in a database and uses it to train the AI ​​model, improving the accuracy of advice from the next time onwards.

[1037] The above steps will create a system that continuously monitors the health of crops and their growing environment and suggests optimal agricultural actions.

[1038] Example 1

[1039] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1040] Conventional agricultural support systems have had problems with the collection and analysis of environmental data and the proposal of appropriate agricultural actions not being done quickly enough and accurately. In particular, manual data collection and analysis takes time, often resulting in delays in appropriate responses. Another issue is that data feedback is not effectively utilized, making it difficult to improve the accuracy of future advice.

[1041] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1042] In this invention, the server includes a sensor device that collects environmental data, a transmission device that transmits the data collected from the sensor device to a central processing unit via a communication device, an analysis device that analyzes the data and encourages appropriate agricultural actions, an advice generation device that suggests agricultural actions based on the analysis, a notification device that notifies a user terminal of the advice, and a feedback device that transmits feedback data obtained from the user terminal to the central processing unit. This enables rapid and accurate collection and analysis of environmental data, rapid suggestion of appropriate agricultural actions, and effective use of feedback.

[1043] A "sensor device" is a device that collects environmental data such as temperature, humidity, soil pH, water content, and light intensity.

[1044] A "communication device" is a device for transmitting collected environmental data, and may include, for example, a fifth generation mobile communication system (5G).

[1045] The "central processing unit" is a device for analyzing and managing received environmental data.

[1046] The "transmitting device" is a device for transmitting data collected from the sensor device to the central processing unit via the communication device.

[1047] "Analysis Device" means a device used by the Central Processing Unit to perform the necessary analysis, such as analyzing data using AI models or machine learning algorithms.

[1048] The "advice generator" is a device for suggesting appropriate agricultural actions based on the analysis results.

[1049] The "notification device" is a device for notifying the generated advice to the user terminal.

[1050] A "feedback device" is a device for transmitting feedback data obtained from a user terminal to a central processing unit.

[1051] A "user terminal" is a device that allows a user to receive advice and send feedback, and includes a smartphone, tablet, or the like.

[1052] This invention is an agricultural support system that utilizes AI, IoT, and 5G technologies. This system collects data on agricultural crops and the surrounding environment, and proposes optimal agricultural actions to users based on the analysis results. The specific hardware and software configurations and their operation are described below.

[1053] System configuration

[1054] The system consists of the following main components:

[1055] 1. Sensor device

[1056] These IoT sensors collect crop and environmental data (temperature, humidity, soil pH, water content, light intensity, etc.) in real time, enabling detailed environmental information within the farm to be obtained. Specifically, they include soil humidity and temperature sensors.

[1057] 2. Communications Equipment

[1058] This is a communications device that transmits collected data to a central processing unit using 5G communications, enabling fast and reliable data transmission.

[1059] 3. Central Processing Unit

[1060] This is a central processing unit that receives, manages, and analyzes the transmitted data. Specifically, a database is placed on the server and used to store and analyze various data. The server stores the collected data and uses analytical algorithms to optimize agriculture.

[1061] 4. Analysis device

[1062] The server uses AI-based analytical algorithms to analyze the collected data and evaluate the agricultural situation. This allows for the determination of the health of crops and necessary agricultural actions based on environmental data. Specifically, the analysis is carried out using machine learning models and neural networks.

[1063] 5. Advice Generator

[1064] Based on the analysis results, the system proposes agricultural actions and provides advice on optimal cultivation methods, such as "spray specific pesticides in the right amounts" or "add specific fertilizer to adjust the soil pH."

[1065] 6. Notification device

[1066] This device sends and notifies the user of advice generated by the server via 5G communication, enabling real-time notifications. Users can receive advice on their smartphones, tablets, or other devices.

[1067] 7. Feedback Devices

[1068] This is a device for transmitting feedback data obtained from user terminals to the central processing unit. Users input the agricultural actions they performed and their results, and send this to the server.

[1069] Specific examples

[1070] For example, if crops are placed in a high-humidity environment during the rainy season, the sensor device will collect humidity data frequently. This data will then be immediately sent to a central processing unit using 5G communications. The server will then analyze the data and detect that humidity may increase the risk of pest infestation in the crops. Based on the analysis results, the advice generation device will suggest an appropriate pest control method (e.g., early application of a specific pesticide). A notification will be sent to the user's device, and the user will follow the instructions to quickly spray the pesticide, thereby preventing the occurrence of pests and diseases. The user will then input the results of their actions into a feedback device and send it to the server. The server will use this feedback for analysis to improve the accuracy of future advice.

[1071] Prompt Sentence Examples

[1072] Below is an example of a prompt sentence to input to a generative AI model (e.g., ChatGPT).

[1073] Please explain the following agricultural support systems that utilize AI, IoT, and 5G technologies:

[1074] The sensor collects temperature and humidity data and transmits it to a server via 5G communication.

[1075] The server analyzes the received data and evaluates the health of the crops.

[1076] If necessary, it generates suggestions for agricultural actions (e.g., spraying pesticides) and notifies the user.

[1077] After the user performs an action, the result is given as feedback.

[1078] Please explain with specific examples.

[1079] The above is an embodiment of the invention.

[1080] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1081] Program processing

[1082] Step 1: Data collection

[1083] Subject: Sensor device

[1084] The sensor device collects environmental data such as temperature, humidity, soil pH, water content, and light intensity using various sensors placed around the farm.

[1085] Input: Environmental data on soil and air temperature, humidity, pH, and light intensity

[1086] Data processing and calculation: The sensor measurement values ​​are temporarily stored in the memory of the sensor device.

[1087] Output: Temporarily saved environmental data

[1088] Specific operation: The humidity sensor measures the humidity at 80% and records the data in memory.

[1089] Step 2: Send data

[1090] Subject: Communication device

[1091] The communication device transmits data collected from the sensor device to the central processing unit in real time, using 5G communication to achieve high-speed and stable data transmission.

[1092] Input: Temporarily saved environmental data (e.g. humidity 80%)

[1093] Data processing and calculation: Data is transmitted to the central processing unit using the 5G communication module.

[1094] Output: Environmental data received by the server

[1095] Specific operation: Humidity data (80%) is sent to the server via 5G communication.

[1096] Step 3: Save Data

[1097] Subject: Server

[1098] The server stores the received environmental data in an analysis database.

[1099] Input: Received environmental data (humidity 80%)

[1100] Data processing and calculation: Register environmental data in the database.

[1101] Output: Data stored in the database

[1102] Specific operation: The server saves the humidity data (80%) in the database.

[1103] Step 4: Data analysis

[1104] Subject: Server

[1105] The server uses AI models (e.g., machine learning algorithms) to analyze the data, which is then used by the analysis device to assess the health of the crops and the agricultural actions required.

[1106] Input: Environmental data stored in a database

[1107] Data processing and calculation: Data analysis is performed using AI models and machine learning algorithms.

[1108] Output: Analysis results (indicating that high humidity increases the risk of pests)

[1109] Specific operation: The server performs an analysis and determines that the humidity level of 80% is outside a specific range, increasing the risk of pests and diseases.

[1110] Step 5: Advice Generation

[1111] Subject: Server

[1112] The server generates appropriate agricultural actions based on the analysis results, and the advice generator creates specific advice and recommends it to the user.

[1113] Input: Analysis results (increase in pest risk)

[1114] Data processing and calculation: Based on the analysis results, appropriate agricultural actions (e.g., spraying pesticides) are generated.

[1115] Output: Generated advice

[1116] Specific operation: The server generates advice such as "Because the humidity is high, please spray 5 liters of a specific pesticide (brand name: XY)."

[1117] Step 6: Advice Notification

[1118] Subject: Server

[1119] The server passes the generated advice to the notification device, which then transmits the advice to the user device using 5G communications. The user device then displays the advice and notifies the user.

[1120] Input: Generated advice

[1121] Data processing and calculation: Advice is sent using 5G communication.

[1122] Output: Advice displayed on the user's terminal

[1123] Specific operation: The server sends advice to the smartphone, which then displays a notification saying, "Due to high humidity, please spray 5 liters of pesticide XY."

[1124] Step 7: Take Action

[1125] Subject: User

[1126] The user performs agricultural actions based on the notified advice.

[1127] Input: Received advice

[1128] Data processing and calculation: Implement specific agricultural actions based on the advice.

[1129] Output: Farming actions performed

[1130] Specific behavior: The user receives a notification on their smartphone and sprays 5 liters of pesticide XY.

[1131] Step 8: Gather feedback

[1132] Subject: User

[1133] The user inputs the agricultural actions they have performed and their results into the user terminal, and the feedback device transmits the input feedback to the server.

[1134] Input: The result of the agricultural action performed

[1135] Data processing and calculation: The results are entered into the user's terminal and sent to the server.

[1136] Output: Feedback data sent to the server

[1137] Specific operation: The user types "Pesticide XY was sprayed" into their smartphone and sends it.

[1138] Step 9: Use feedback

[1139] Subject: Server

[1140] The server adds the received feedback to the analysis data and uses it to improve the accuracy of advice from the next time onwards.

[1141] Input: Feedback data

[1142] Data processing and calculation: Feedback is reflected in the analysis and the database is updated.

[1143] Output: Improved advice for next time

[1144] Specific operation: The server uses the feedback that "after spraying pesticide XY, the humidity returned to the appropriate value" for analysis, and improves the accuracy of mold prevention advice from the next time onwards.

[1145] The above are the specific steps and details of the system's program processing.

[1146] (Application example 1)

[1147] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1148] Conventional factory automation systems have issues with real-time response and accuracy when it comes to detecting equipment anomalies and proposing maintenance. It's also difficult to quickly notify appropriate actions, making it impossible to maximize production efficiency and equipment lifespan. This leads to unnecessary downtime and excessive maintenance, which increases production costs and increases the burden on workers.

[1149] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1150] In this invention, the server includes a sensor means for collecting environmental data, a transmission means for transmitting the data collected from the sensor means to a central processing unit via a communication means, an analysis means for analyzing the data and promoting appropriate manufacturing processes and maintenance actions, an advice generation means for proposing manufacturing processes and maintenance actions based on the analysis, a notification means for notifying a user device of the advice, and a feedback means for transmitting feedback data obtained from the user device to the central processing unit. This enables real-time data collection within the factory and highly accurate analysis using AI to propose optimal actions. Furthermore, the use of 5G communications enables high-speed data transmission, allowing for prompt maintenance and process adjustments as needed. This minimizes equipment downtime and improves production efficiency.

[1151] "Environmental data" refers to data related to the machinery and working environment within the factory, such as temperature, humidity, and vibration.

[1152] "Sensor means" refers to devices and techniques for collecting environmental data in real time.

[1153] "Communication Means" refers to devices and technologies for transmitting data collected from the Sensor Means, including in particular those utilizing fifth generation mobile communication systems (5G).

[1154] "Transmitting Means" refers to devices and techniques for transmitting environmental data from the Sensor Means to the Central Processing Unit.

[1155] "Central Processing Unit" means a computer system that receives, stores, and analyzes collected data.

[1156] "Analysis Tools" refers to devices and algorithms used to analyze collected environmental data and identify appropriate manufacturing process or maintenance actions.

[1157] "Advice generation means" refers to devices and techniques that generate suggestions for manufacturing processes and maintenance actions based on the analysis results obtained by the analysis means.

[1158] "Notification means" refers to devices and techniques for transmitting and notifying the user device of the suggestions generated by the advice generation means.

[1159] A "user device" is a device on which a user receives notifications, and includes a smartphone or tablet.

[1160] "Feedback means" refers to devices and techniques for transmitting feedback data obtained from user devices to a central processing unit.

[1161] This invention is a system that collects data on equipment and the environment within a factory, analyzes that data, and proposes optimal manufacturing processes and maintenance actions. The system consists of the following main components:

[1162] System configuration

[1163] The system consists of the following main components:

[1164] 1. Sensor means

[1165] The sensor means is a device or technology for collecting environmental data such as temperature, humidity, and vibration in a factory in real time. This includes temperature sensors, humidity sensors, vibration sensors, etc.

[1166] 2. Transmission Method

[1167] The transmission means is a device and technology for transmitting data collected by the sensor means to the central processing unit, and uses 5G communication, enabling high-speed and stable data transmission.

[1168] 3. Central Processing Unit

[1169] The central processing unit is a computer system that receives, stores, and analyzes collected data. Specifically, it includes a database server and an AI analysis server.

[1170] 4. Analysis method

[1171] The analysis method is an analytical algorithm using artificial intelligence in the central processing unit, which analyzes the collected data and determines whether the manufacturing process or maintenance is necessary. TensorFlow and Keras are used for the analysis.

[1172] 5. Advice Generation Methods

[1173] The advice generator is a system that proposes optimal manufacturing processes and maintenance actions based on the results obtained from the analysis means, including appropriate maintenance timing and specific action plans.

[1174] 6. Means of notification

[1175] The notification means is a device or technology for transmitting the generated advice to a user device, such as a smartphone or tablet.

[1176] 7. Feedback channels

[1177] The feedback means is an interface for transmitting feedback data obtained from the user device to the central processing unit. By inputting the actions taken by the user and their results, the central processing unit uses this information for further analysis and optimization.

[1178] Program processing

[1179] Data collection and transmission

[1180] The sensor means continuously collects environmental data from each sensor inside the factory, such as temperature, humidity, vibration, etc. The transmission means transmits the collected environmental data to the central processing unit using 5G communication.

[1181] Data analysis and advice generation

[1182] The central processing unit stores newly received data in an analysis database. The analysis means analyzes the data using an AI model and evaluates the health of the equipment and the necessary manufacturing and maintenance actions. Specifically, if vibrations exceed a certain threshold, it detects the high possibility that an abnormality has occurred. The advice generation means generates advice that suggests the optimal manufacturing and maintenance actions based on the analysis results.

[1183] Advice notifications and feedback collection

[1184] The notification means sends the generated advice to the user device and notifies it. The user can check the notification on a smartphone or tablet. The user then performs manufacturing or maintenance actions based on the notification. Specifically, this could include performing machine maintenance or adjusting process settings. The feedback means provides an interface that allows the user to input the results of the actions they have taken and send them to the central processing unit. This allows the effectiveness of the actions to be reported. The central processing unit uses this feedback for analysis and improves the accuracy of advice from the next time onwards.

[1185] Specific examples

[1186] For example, if a robot arm detects abnormal vibrations during production in a factory, the sensor means immediately collects and transmits the data. The central processing unit analyzes this data and, if it determines that machine maintenance is necessary, the advice generation means immediately suggests that "maintenance of this robot arm is necessary." The user receives the notification and promptly performs maintenance work according to the instructions. The results are entered into the user device and transmitted to the central processing unit as feedback data. This improves the accuracy of abnormality detection and maintenance actions from the next time onwards.

[1187] Example prompt for a generative AI model:

[1188] Design a system that collects and analyzes sensor data, specifically temperature, humidity, and vibration data, to propose optimal maintenance methods for equipment.

[1189] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1190] Step 1:

[1191] The sensor means collects environmental data such as temperature, humidity, and vibration in the factory in real time.

[1192] Specifically, each sensor measures data at a specified interval and temporarily stores the data in memory. The input is environmental data, and the output is the collected environmental data.

[1193] Step 2:

[1194] The transmitting means transmits the collected environmental data to the central processing unit using 5G communication.

[1195] Specifically, the data stored in the sensor's memory is converted into packets and sent to a central processing unit via a 5G modem. The input is the collected environmental data, and the output is the data sent to the central processing unit.

[1196] Step 3:

[1197] The central processing unit stores the newly received data in a database.

[1198] Specifically, the received data is written to an analysis database and a timestamp is added. The input is the data received via 5G communication, and the output is the data stored in the database.

[1199] Step 4:

[1200] The analytics tool uses AI models to analyze the data and assess the health of the equipment and any necessary production or maintenance actions.

[1201] Specifically, using a machine learning framework such as TensorFlow, collected data is input into a trained model to detect anomalies and identify necessary actions. The input is the data stored in the database, and the output is the analysis results.

[1202] Step 5:

[1203] The advice generation means generates advice that proposes optimal manufacturing and maintenance actions based on the analysis results.

[1204] Specifically, specific advice such as "This robot arm needs maintenance" is generated based on the analysis results. This identifies the necessary maintenance work and adjustments to the manufacturing process. The input is the analysis results, and the output is the generated advice.

[1205] Step 6:

[1206] The notification means transmits the generated advice to the user device to notify it.

[1207] Specifically, a messaging protocol is used via a notification server to notify the generated advice to the user's smartphone or tablet. The input is the generated advice, and the output is the notification to the user device.

[1208] Step 7:

[1209] Users can take production and maintenance actions based on notifications.

[1210] Specifically, the user checks the notification content and performs machine maintenance or process adjustments according to the instructions. The input is the notification received by the user device, and the output is the action taken.

[1211] Step 8:

[1212] A feedback means provides an interface for inputting and transmitting results of actions taken by the user to the central processing unit.

[1213] Specifically, the results of the actions taken by the user are input into the application, and the data is sent back to the central processing unit. The input is the result of the action taken, and the output is the feedback data sent to the central processing unit.

[1214] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1215] This invention is an agricultural support system that utilizes AI, IoT, 5G technology, and an emotion engine. It not only collects data on crops and the surrounding environment and suggests optimal agricultural actions to users based on the analysis results, but also recognizes the user's emotions and adjusts the content and presentation of advice to provide more effective agricultural support.

[1216] System configuration

[1217] The system consists of the following main components:

[1218] 1. Sensor means

[1219] It is an IoT sensor for collecting crop and environmental data (temperature, humidity, soil pH, water content, light intensity, etc.) in real time.

[1220] 2. Transmission Method

[1221] This is a communication device that transmits collected data to a server using 5G communication.

[1222] 3. Server

[1223] This is the central processing unit that receives, manages, and analyzes the transmitted data. The server stores the collected data and uses analytical algorithms to optimize agriculture.

[1224] 4. Analysis method

[1225] The server uses an AI-based analytical algorithm to analyze the collected data and evaluate the agricultural situation.

[1226] 5. Advice Generation Methods

[1227] Based on the analysis results, the system suggests agricultural actions and provides advice on optimal cultivation methods.

[1228] 6. Means of notification

[1229] This is a means for notifying the user terminal of the advice generated by the server and conveying it to farmers.

[1230] 7. User Devices

[1231] The devices farmers use to receive advice and send feedback include smartphones and tablets.

[1232] 8. Feedback channels

[1233] This is an interface for users to input the agricultural actions they have performed and the results of those actions, and send them to the server.

[1234] 9. Emotion Engine

[1235] This is a device that analyzes emotions from the user's voice and facial expressions and feeds the results back to the server.

[1236] System action

[1237] Data collection and transmission

[1238] The sensor means continuously collects environmental data from various sensors around the farm, such as soil pH, temperature, humidity, and water content.

[1239] The transmission means transmits the collected environmental data to the server using 5G communication, which enables high-speed and stable data transmission.

[1240] Data analysis and advice generation

[1241] The server stores the newly received data in an analysis database.

[1242] The analytics tool uses AI models to analyze the data and assess the health of the crops and the agricultural actions they need to take, such as detecting patterns of temperature and humidity fluctuations and determining whether the crops are at risk.

[1243] The advice generator generates advice that suggests optimal agricultural actions based on the analysis results, such as "spray a specific pesticide in the right amount" or "add a specific fertilizer to adjust the soil pH."

[1244] Advice Notification and Emotion Recognition

[1245] The notification means notifies the user terminal of the generated advice. Specifically, the advice content is sent as a push notification to a smartphone or tablet so that the user can immediately check it.

[1246] The emotion engine analyzes the user's voice and facial expressions and feeds back their emotional state to the server. For example, if the user is in a stressful state, that information is sent to the server.

[1247] Emotion-based advice adjustment

[1248] The server receives feedback from the emotion engine and adjusts the content and expression of advice in the advice generation means. For example, if the user is under stress, advice containing encouraging words is generated to increase the user's motivation.

[1249] Engaging with users and gathering feedback

[1250] The user device receives and displays the notification, allowing the user to review the specific agricultural action recommendations.

[1251] The user performs agricultural actions based on the advice presented on the user terminal. For example, if the advice includes "add fertilizer," the user selects an appropriate fertilizer and applies the recommended amount to the farmland.

[1252] The feedback means provides an interface for inputting the results of the agricultural actions performed by the user, and the user inputs the details of the work that he or she actually performed and the results of the work into this interface.

[1253] The user terminal transmits the input feedback data to the server. Specifically, the feedback data is packetized and transmitted to the server via 5G communication.

[1254] The server analyzes the received feedback data and uses it to improve the accuracy of the analytical model. The server stores the feedback data in a database and uses it to train the AI ​​model, improving the accuracy of advice from the next time onwards.

[1255] Specific examples

[1256] For example, if crops are placed in a high-humidity environment during the rainy season, the sensor means will collect humidity data frequently. This data will then be immediately sent to the server using 5G communications. The server will analyze this data and detect that humidity may increase the risk of pest infestation. Based on the analysis results, the advice generation means will suggest an appropriate pest control method (e.g., early application of a specific pesticide). A notification will be sent to the user's device, and the user will quickly follow the instructions to spray the pesticide. In addition, the emotion engine will analyze the user's voice and facial expressions. If the server detects that the user is under stress, the server will generate advice that includes encouraging words. The user then inputs the results of their actions into the feedback means and sends it to the server. The server will use this feedback for analysis to improve the accuracy of future advice.

[1257] This not only provides a consistent system that supports agricultural productivity and sustainability, but also responds to the user's emotional state, resulting in more effective agricultural support.

[1258] The processing flow will be explained below.

[1259] Step 1:

[1260] The sensor means collects real-time environmental data on the crops and their surroundings (temperature, humidity, soil pH, water content, light intensity, etc.). Each sensor periodically obtains its own measured value and temporarily stores the data in its internal memory.

[1261] Step 2:

[1262] The transmission means packetizes the collected environmental data and transmits it to the server using 5G communication, allowing the environmental data to be transmitted to the server at high speed and with high stability.

[1263] Step 3:

[1264] The server receives the environmental data sent from the sensor means and first checks its consistency. After the data consistency is confirmed, it stores it in a database and prepares it for analysis.

[1265] Step 4:

[1266] The analysis means stores the received data in an analytical database and inputs the data into an AI model, which compares it with past data to assess the current environmental conditions and the health of the crops. For example, it analyzes patterns of temperature and humidity fluctuations to predict whether the crops are at risk of pests or diseases.

[1267] Step 5:

[1268] The advice generation means generates specific agricultural action proposals based on the analysis results. For example, if humidity is high and the risk of a specific pest is high, the system generates specific advice such as "spray a specific pesticide in the appropriate amount."

[1269] Step 6:

[1270] The emotion engine recognizes emotions from the user's voice and facial expressions and sends the data to the server. The emotion engine determines the user's emotional state, such as stress or satisfaction, from their reaction.

[1271] Step 7:

[1272] The server analyzes the emotion data received from the emotion engine, and if the user is feeling stressed, feeds back that information to the advice generating means.

[1273] Step 8:

[1274] The advice generation means adjusts the content and expression of the advice based on the analysis results of the emotion engine. For example, if the user is in a high-stress state, the advice generation means generates advice that includes kinder expressions and encouraging words.

[1275] Step 9:

[1276] The notification means sends the generated advice to the user terminal, and the advice content is displayed on the user's smartphone or tablet via push notification.

[1277] Step 10:

[1278] The user terminal displays the received advice, and the user confirms it and plans agricultural actions based on the advice.

[1279] Step 11:

[1280] The user follows the advice displayed on the user terminal and performs specific agricultural actions. For example, if the advice is to "spray a specific pesticide in the appropriate amount," the user will spray the indicated amount of pesticide on the field.

[1281] Step 12:

[1282] The feedback means provides an interface for inputting the results of the agricultural actions performed by the user. The user inputs the details of the work actually performed and the results, and saves them in the terminal.

[1283] Step 13:

[1284] The user terminal packetizes the input feedback data and transmits it again to the server via 5G communication.

[1285] Step 14:

[1286] The server analyzes the received feedback data and uses it to improve the accuracy of the analytical model. The feedback data is stored in a database and used to train the AI ​​model, improving the accuracy of advice from the next time onwards.

[1287] This will enable a more effective agricultural support system that not only continuously monitors the health and growth environment of crops and suggests optimal agricultural actions, but also responds to the user's emotional state.

[1288] Example 2

[1289] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1290] In modern agriculture, the collection and analysis of environmental data to propose appropriate agricultural actions is extremely important. However, conventional systems do not adequately analyze the collected data or generate advice, and in particular lack support that takes into account the user's emotional state. This reduces user motivation and makes it difficult to provide effective agricultural support. Furthermore, delays in real-time data transmission and analysis can lead to reduced productivity and increased agricultural risks. Effective methods to resolve these issues are needed.

[1291] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a sensor means for collecting environmental data, a transmission means for transmitting data collected from the sensor means to the server via a communication means, an analysis means for analyzing the data and encouraging appropriate agricultural actions, an advice generation means for suggesting agricultural actions based on the analysis, a notification means for notifying the user terminal of the advice, an emotion analysis means for analyzing the emotional state of the user, an emotion adaptation means for adjusting the content and expression of the advice based on the results of the emotion analysis means, and a feedback means for transmitting feedback data obtained from the user terminal to the server. This enables effective agricultural support that takes the user's emotional state into consideration, and by realizing data transmission and analysis in real time, agricultural productivity and efficiency are significantly improved.

[1292] A "sensor means" is a device for collecting environmental data.

[1293] The "transmitting means" is a device for transmitting data collected from the sensor means to the server via the communication means.

[1294] The "analysis means" is a device that analyzes the received data and encourages appropriate agricultural actions.

[1295] The "advice generator" is a device for suggesting agricultural actions based on the results of the analysis means.

[1296] The "notification means" is a device for notifying the user terminal of the generated advice.

[1297] The "emotion analysis means" is a device for analyzing the user's emotional state.

[1298] The "emotion adaptation means" is a device for adjusting the content and expression of advice based on the results of the emotion analysis means.

[1299] The "feedback means" is a device for transmitting feedback data obtained from a user terminal to a server.

[1300] "Communication medium" is the technology used to send and receive data.

[1301] A "user terminal" is a device through which a user receives advice and sends feedback.

[1302] Basic system configuration

[1303] The system utilizes AI, IoT, 5G technology, and an emotion engine to collect data on crops and the surrounding environment, and then suggests optimal agricultural actions to users based on the analysis results.It also analyzes the user's emotional state and adjusts the content and presentation of advice to provide effective agricultural support.

[1304] Specifically, the following hardware and software are used.

[1305] Main hardware used:

[1306] IoT sensors: temperature sensors, humidity sensors, soil pH sensors, moisture sensors, light intensity sensors, etc.

[1307] Communications equipment: 5G-compatible modems and routers.

[1308] Server: The central processing unit responsible for collecting, analyzing, storing data and generating advice.

[1309] User device: A device such as a smartphone or tablet that allows a user to receive advice and send feedback.

[1310] Emotion engine: Camera and microphone for analyzing the user's voice and facial expressions.

[1311] Main software used:

[1312] AI model: The machine learning algorithm used to analyze data and generate advice.

[1313] Database software: NoSQL database, e.g. MongoDB.

[1314] Analysis algorithms: Software that analyzes environmental data such as temperature, humidity, soil pH, water content, and light intensity to assess crop health and any necessary agricultural actions.

[1315] Notification software: A mobile application for sending push notifications.

[1316] System operation example

[1317] For example, if crops are placed in a high-humidity environment during the rainy season, the sensor will collect humidity data frequently. The device will then transmit the data obtained from the humidity sensor to a server via 5G communication. The server will then receive the data and store it in a database.

[1318] The server uses an AI analysis model to analyze humidity data and detect whether humidity can increase the risk of pests and diseases in crops. Based on the analysis results, the advice generation means generates advice suggesting appropriate pest control methods (e.g., early application of a specific pesticide).

[1319] The generated advice is sent as a push notification to the user's smartphone or tablet via the notification means. The user receives the advice and sprays the pesticide as instructed. The emotion engine analyzes the user's voice and facial expression, and if it determines that the user is under stress, the server generates advice including words of encouragement.

[1320] Example prompt sentence:

[1321] "We've detected that the user is under stress. Please generate friendly, encouraging advice."

[1322] The user then sends the results of their agricultural actions to the server via a feedback mechanism. The server analyzes this feedback data, stores it in a database, and uses it as training data to improve the accuracy of advice in future.

[1323] effect

[1324] This system enables real-time, highly accurate data analysis and provides personalized agricultural support that takes into account the user's emotional state, thereby improving agricultural productivity and efficiency and enabling sustainable agricultural practices.

[1325] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1326] Step 1: Data collection

[1327] The terminal (sensor means) collects environmental data within the farm in real time. The sensor means uses temperature sensors, humidity sensors, soil pH sensors, moisture sensors, light intensity sensors, etc. to obtain data on temperature, humidity, soil pH, moisture content, light intensity, etc. For example, the temperature sensor measures 25°C, and the humidity sensor measures 80%.

[1328] Input: Environmental data

[1329] Output: Raw data from the sensor

[1330] Step 2: Send data

[1331] The environmental data collected by the device is sent to a server using 5G communication. Specifically, the data obtained from each sensor is packaged in JSON format and sent over the 5G network. For example, data such as {"temperature": 25, "humidity": 80} is sent to the server.

[1332] Input: Raw data from the sensor

[1333] Output: JSON data sent to the server via the communication method

[1334] Step 3: Data reception and storage

[1335] The server receives the data sent from the device and stores it in a database for analysis. For example, the server's API receives JSON data and executes a process to store it in a NoSQL database.

[1336] Input: JSON data sent via communication means

[1337] Output: Environmental data stored in a database

[1338] Step 4: Data analysis

[1339] The server analyzes the data using an AI analysis model. For example, it determines that the risk of pests and diseases increases when humidity levels remain high. The server compares this data with past data and makes a risk assessment. Specifically, it analyzes temperature and humidity patterns to assess the health of the crops.

[1340] Input: Environmental data stored in a database

[1341] Output: Analysis results (risk of pests, etc.)

[1342] Step 5: Advice Generation

[1343] The server generates advice based on the analysis results. For example, it generates specific instructions such as "Because humidity is high, spray a specific pesticide now." The advice generation means proposes optimal agricultural actions based on the analysis results.

[1344] Input: Analysis results

[1345] Output: Specific advice

[1346] Step 6: Advice Notification

[1347] The server sends the generated advice to the user's device. The advice is sent to the user in real time using push notifications. For example, a notification saying "Please spray pesticides now" is displayed on the smartphone.

[1348] Input: Specific advice

[1349] Output: Notification to user terminal

[1350] Step 7: User sentiment analysis

[1351] The emotion engine analyzes the user's voice and facial expression and transmits the user's emotional state to the server. For example, the emotion engine determines that the user is feeling stressed based on the user's tone of voice and facial expression.

[1352] Input: Audio and visual data

[1353] Output: Parsed emotion data

[1354] Step 8: Adjusting Advice Based on Emotions

[1355] The server adjusts the content and expression of the advice based on the results of emotion analysis. For example, for a user who is under stress, it adds an encouraging message such as, "Thank you for your hard work. Please take a short break before proceeding to the next step."

[1356] Input: Parsed emotion data

[1357] Output: Adjusted advice

[1358] Step 9: User Actions and Feedback Collection

[1359] The user receives the notification and takes action based on the advice, for example, spraying a specific pesticide as instructed. The feedback means provides an interface that records the actions taken by the user, and the user inputs the results, such as "the pesticide has been sprayed."

[1360] Input: User action result

[1361] Output: Feedback data

[1362] Step 10: Sending and using feedback data

[1363] The user device sends the recorded feedback data to a server via 5G communication. The server receives the feedback data and uses it for analysis. The server uses the feedback data to train the AI ​​model and improve the accuracy of advice from the next time onwards.

[1364] Input: Feedback data sent from the user device

[1365] Output: Updated analytical model

[1366] By repeatedly performing these steps, the system not only continues to support the user's agricultural activities, but also provides personalized support that is attuned to the user's emotional state.

[1367] (Application example 2)

[1368] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1369] Conventional security systems collect environmental data and detect anomalies, but lack the ability to adjust alerts based on the emotional state of security guards. This makes it difficult for stressed security guards to respond quickly and accurately. The present invention aims to solve this problem by providing a system that realizes effective security responses that take emotional states into account.

[1370] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes sensor means for collecting environmental data, transmission means for transmitting data collected from the sensor means to the server via communication means, analysis means for analyzing the data and encouraging appropriate action, advice generation means for suggesting an action based on the analysis, notification means for notifying the user terminal of the advice, an emotion engine for acquiring user emotion data, advice adjustment means for adjusting the content of advice based on the emotion data, and feedback means for transmitting feedback data obtained from the user terminal to the server. This enables effective alert notification that takes into account the emotional state of the security guard.

[1371] "Environmental Data" refers to information about the environment, such as ambient temperature, humidity, movement, and sound.

[1372] "Sensor means" refers to a device or group of devices that collect environmental data in real time.

[1373] "Transmission means" refers to a device or function that transmits collected data to a server via a communication means.

[1374] "Analysis means" refers to the function of using the server's algorithms and programs to analyze collected data and determine abnormalities and necessary actions.

[1375] The "advice generation means" refers to a function that generates specific actions and suggestions for the user based on the results obtained by the analysis means.

[1376] The "notification means" refers to a function or device for transmitting the generated advice or warning to the user terminal.

[1377] "User terminal" refers to a device that can receive and operate information, such as a smart device or HMD used by security guards.

[1378] An "emotion engine" refers to a function or device that analyzes the user's emotional state from their voice and facial expressions, and acquires and transmits that data.

[1379] "Advice adjustment means" refers to a function that adjusts the content and expression of advice based on emotional data obtained from the emotion engine.

[1380] "Feedback means" refers to an interface or function for recording the actions performed by the user and the results thereof, and transmitting them to the server.

[1381] System Overview

[1382] This invention is a security system designed to support security guards. This system combines IoT sensors, 5G communications, AI analysis, and an emotion engine to easily realize anomaly monitoring and emotion recognition. This section explains the details of each component and how to combine them.

[1383] System configuration

[1384] Sensor Means

[1385] The system includes multiple IoT sensors (e.g., temperature, humidity, motion, and sound sensors) to collect environmental data. These sensors are installed in different locations within the facility and collect data in real time. The collected environmental data includes temperature, humidity, motion, and sound.

[1386] Transmission method

[1387] The environmental data collected by the sensors is sent to a server using 5G communication, which enables high-speed and stable data transmission.

[1388] server

[1389] The server is a central processing unit that receives, manages, and analyzes the environmental data sent to it. The environmental data is stored in a database within the server, and an analysis algorithm using an AI model (for example, TensorFlow) evaluates the data as an analysis method.

[1390] Analysis means

[1391] The analysis means analyzes the collected data to detect anomalies, for example, if a motion sensor detects suspicious activity within the facility, the data is flagged as an anomaly.

[1392] Advice Generation Method

[1393] Based on the analysis results, advice is generated that suggests appropriate actions to take. For example, when an abnormality is detected, an instruction such as "urgent action is required" is generated.

[1394] Notification means

[1395] The generated advice and warnings are sent to the user's device (smart glasses, head-mounted display, etc.) via a notification means, allowing security guards to receive prompt alerts.

[1396] Emotion Engine

[1397] The emotion engine is a device that analyzes emotions from the user's voice and facial expressions and sends the data to a server. For example, if the user is in a stressful state, the data is sent to the server.

[1398] Advice adjustment measures

[1399] The server adjusts the advice content based on the emotion data obtained from the emotion engine. For example, if a user is under stress, the server generates advice that includes encouraging words, providing advice in a way that is optimal for the user.

[1400] Feedback Methods

[1401] The actions taken by the user and their results are sent to the server via a feedback mechanism, which allows the server to use the accumulated feedback data for analysis and to improve the accuracy of the AI ​​model.

[1402] Specific examples

[1403] For example, if a motion sensor installed in a facility detects abnormal activity, the data is immediately sent to a server via 5G communication. The server analyzes the data and detects the abnormality, generating an alert stating that "emergency action is required." This alert is then sent to the user's device, such as smart glasses. At the same time, the emotion engine analyzes the facial expressions and voice of the security guard, and if it detects that the guard is under stress, the advice adjustment means generates advice including an encouraging message such as "Please stay calm." The user then takes action in accordance with the instructions and sends the results to the server using the feedback means. The server uses this feedback data in future analyses to improve the accuracy of the entire system.

[1404] Prompt Sentence Examples

[1405] "Please provide data to train an AI model for anomaly detection for a security monitoring system. Please output the data in the following format:

[1406] timestamp

[1407] Sensor ID

[1408] Sensor Value

[1409] Normal / abnormal label

[1410] As described above, the detailed description shows how components in a security system work together to function efficiently.

[1411] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1412] Step 1:

[1413] Sensors collect environmental data. Sensors (e.g., temperature, humidity, motion, and sound sensors) capture data in real time and store the data in internal memory. The input is the sensor reading (temperature, humidity, motion, sound, etc.) and the output is the collected environmental data.

[1414] Step 2:

[1415] The transmitting means transmits the collected environmental data to a server. The data collected from the sensor is transmitted to the server using 5G communication. The input is the environmental data collected by the sensor, and the output is the data transmitted to the server.

[1416] Step 3:

[1417] The server stores the received data in an analysis database. As soon as the server receives the data, it adds it to the database and uses it as basic data for analysis. The input is the environmental data sent from the transmission means, and the output is the data stored in the database.

[1418] Step 4:

[1419] The server's analytical means analyzes the data and detects anomalies. An AI model (using, for example, TensorFlow) evaluates the environmental data and detects abnormal behavior or conditions. The input is the environmental data stored in the database, and the output is the analysis result (normal / abnormal determination).

[1420] Step 5:

[1421] The advice generator proposes actions based on the analysis results. If an anomaly is detected, it generates appropriate countermeasures (e.g., emergency response instructions). The input is the server's analysis results, and the output is the proposed actions or warnings.

[1422] Step 6:

[1423] The notification means sends the advice to the user's terminal. The generated advice or warning is notified to the user's terminal, such as smart glasses or a head-mounted display. The input is the suggestion from the advice generation means, and the output is the advice / warning displayed on the user's terminal.

[1424] Step 7:

[1425] The emotion engine analyzes the user's emotional data. It acquires the user's voice and facial expression and evaluates their state using an emotion analysis algorithm. The input is the user's voice and facial expression data, and the output is the analyzed emotional data.

[1426] Step 8:

[1427] The advice adjustment means adjusts the advice based on the emotion data. It receives the emotion analysis results and optimizes the content and expression of the advice. The input is the emotion data from the emotion engine and the initial advice content, and the output is the adjusted advice.

[1428] Step 9:

[1429] The adjusted advice is displayed on the user's device. The adjusted content is displayed on the device, and the user confirms it and takes action. The input is the adjusted advice, and the output is the user's action.

[1430] Step 10:

[1431] The feedback means acquires the result of the user's action and transmits it to the server. The user provides feedback on the action and its result using an input device. The input is the result of the user's action, and the output is the feedback data transmitted to the server.

[1432] Step 11:

[1433] The server analyzes the feedback data and uses it for future analyses. The feedback data is stored in a database and reflected in the learning of the AI ​​model. The input is the sent feedback data, and the output is an updated analysis algorithm.

[1434] Through the above processing steps, the security system of the present invention functions effectively and can provide alert notifications and feedback that take into account the emotional state of the security guard.

[1435] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1436] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1437] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1438] [Fourth embodiment]

[1439] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1440] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1441] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1442] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1443] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1444] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1445] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1446] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1447] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1448] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1449] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1450] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1451] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1452] This invention is an agricultural support system that utilizes AI, IoT, and 5G technologies. This system collects data on crops and the surrounding environment, and proposes optimal agricultural actions to users based on the analysis results.

[1453] System configuration

[1454] The system consists of the following main components:

[1455] 1. Sensor means

[1456] It is an IoT sensor for collecting crop and environmental data (temperature, humidity, soil pH, water content, light intensity, etc.) in real time.

[1457] 2. Transmission Method

[1458] This is a communication device that transmits collected data to a server using 5G communication.

[1459] 3. Server

[1460] This is the central processing unit that receives, manages, and analyzes the transmitted data. The server stores the collected data and uses analytical algorithms to optimize agriculture.

[1461] 4. Analysis method

[1462] The server uses an AI-based analytical algorithm to analyze the collected data and evaluate the agricultural situation.

[1463] 5. Advice Generation Methods

[1464] Based on the analysis results, the system suggests agricultural actions and provides advice on optimal cultivation methods.

[1465] 6. Means of notification

[1466] This is a means for sending advice generated by the server to the user's terminal via 5G communication and notifying farmers.

[1467] 7. User Devices

[1468] The devices farmers use to receive advice and send feedback include smartphones and tablets.

[1469] 8. Feedback channels

[1470] This is an interface for users to input the agricultural actions they have performed and the results of those actions, and send them to the server.

[1471] Program processing

[1472] Data collection and transmission

[1473] The sensor means continuously collects environmental data from various sensors around the farm, such as soil pH, temperature, humidity, and water content.

[1474] The transmission means transmits the collected environmental data to a server in real time. 5G communication is used, enabling high-speed and stable data transmission.

[1475] Data analysis and advice generation

[1476] The server stores the newly received data in an analysis database.

[1477] The analytics tool uses AI models to analyze the data and assess the health of the crop and the agricultural actions needed, such as detecting increased risk of pests and diseases when humidity or temperature falls outside certain ranges.

[1478] The advice generator generates advice that suggests optimal agricultural actions based on the analysis results, such as "spray a specific pesticide in the right amount" or "add a specific fertilizer to adjust the soil pH."

[1479] Advice notifications and feedback collection

[1480] The notification means sends the generated advice to the user's device, and the user can check the notification on their smartphone or tablet.

[1481] The user terminal performs agricultural actions based on the notification, such as spraying specific pesticides or adding fertilizer.

[1482] The feedback means provides an interface for users to input the results of their agricultural actions and transmit them to the server, thereby allowing users to report the effectiveness of their actions.

[1483] Specific examples

[1484] For example, if crops are placed in a high-humidity environment during the rainy season, the sensor means will collect humidity data frequently. This data will then be immediately sent to the server using 5G communications. The server will analyze this data and detect that humidity may increase the risk of pest and disease outbreaks in the crops. Based on the analysis results, the advice generation means will suggest an appropriate pest control method (e.g., early application of a specific pesticide). A notification will be sent to the user's device, and the user will follow the instructions to quickly spray the pesticide, thereby preventing the outbreak of pests and diseases. The user will then input the results of their action into the feedback means and send it to the server. The server will use this feedback for analysis to improve the accuracy of advice from the next time onwards.

[1485] This will provide a coherent system that supports increased agricultural productivity and sustainability.

[1486] The processing flow will be explained below.

[1487] Step 1:

[1488] The sensor means collects real-time environmental data on the crops and their surroundings (temperature, humidity, soil pH, water content, light intensity, etc.). Specifically, each sensor measures at a set frequency and temporarily stores the data in its internal memory.

[1489] Step 2:

[1490] The transmitting means transmits the collected environmental data to the server using 5G communication. Specifically, after the sensor means collects a certain amount of data, it divides it into packets and transmits them to the server via the 5G network.

[1491] Step 3:

[1492] The server receives the data sent from the sensor means and stores it in an analysis database. Specifically, it checks the consistency of the received data and stores only accurate data in the database.

[1493] Step 4:

[1494] The analysis means inputs the received data into an AI model to analyze the condition of the crops and determine the optimal agricultural actions. Specifically, it detects fluctuation patterns in temperature and humidity and determines whether the crops are at risk.

[1495] Step 5:

[1496] The advice generator generates specific agricultural action suggestions based on the analysis results, for example, if the analysis results indicate soil acidification, it generates advice such as "add lime."

[1497] Step 6:

[1498] The notification means notifies the user terminal of the generated advice. Specifically, the advice content is sent as a push notification to a smartphone or tablet so that the user can immediately check it.

[1499] Step 7:

[1500] The user device receives and displays the notification, allowing the user to review the specific agricultural action recommendations.

[1501] Step 8:

[1502] The user performs agricultural actions based on the advice presented on the user terminal. For example, if the advice includes "add fertilizer," the user selects an appropriate fertilizer and applies the recommended amount to the farmland.

[1503] Step 9:

[1504] The feedback means provides an interface for inputting the results of the agricultural actions performed by the user, and the user inputs the details of the work that he or she actually performed and the results of the work into this interface.

[1505] Step 10:

[1506] The user terminal transmits the input feedback data to the server. Specifically, the feedback data is packetized and transmitted to the server via 5G communication.

[1507] Step 11:

[1508] The server analyzes the received feedback data and uses it to improve the accuracy of the analytical model. The server stores the feedback data in a database and uses it to train the AI ​​model, improving the accuracy of advice from the next time onwards.

[1509] The above steps will create a system that continuously monitors the health of crops and their growing environment and suggests optimal agricultural actions.

[1510] Example 1

[1511] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1512] Conventional agricultural support systems have had problems with the collection and analysis of environmental data and the proposal of appropriate agricultural actions not being done quickly enough and accurately. In particular, manual data collection and analysis takes time, often resulting in delays in appropriate responses. Another issue is that data feedback is not effectively utilized, making it difficult to improve the accuracy of future advice.

[1513] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1514] In this invention, the server includes a sensor device that collects environmental data, a transmission device that transmits the data collected from the sensor device to a central processing unit via a communication device, an analysis device that analyzes the data and encourages appropriate agricultural actions, an advice generation device that suggests agricultural actions based on the analysis, a notification device that notifies a user terminal of the advice, and a feedback device that transmits feedback data obtained from the user terminal to the central processing unit. This enables rapid and accurate collection and analysis of environmental data, rapid suggestion of appropriate agricultural actions, and effective use of feedback.

[1515] A "sensor device" is a device that collects environmental data such as temperature, humidity, soil pH, water content, and light intensity.

[1516] A "communication device" is a device for transmitting collected environmental data, and may include, for example, a fifth generation mobile communication system (5G).

[1517] The "central processing unit" is a device for analyzing and managing received environmental data.

[1518] The "transmitting device" is a device for transmitting data collected from the sensor device to the central processing unit via the communication device.

[1519] "Analysis Device" means a device used by the Central Processing Unit to perform the necessary analysis, such as analyzing data using AI models or machine learning algorithms.

[1520] The "advice generator" is a device for suggesting appropriate agricultural actions based on the analysis results.

[1521] The "notification device" is a device for notifying the generated advice to the user terminal.

[1522] A "feedback device" is a device for transmitting feedback data obtained from a user terminal to a central processing unit.

[1523] A "user terminal" is a device that allows a user to receive advice and send feedback, and includes a smartphone, tablet, or the like.

[1524] This invention is an agricultural support system that utilizes AI, IoT, and 5G technologies. This system collects data on agricultural crops and the surrounding environment, and proposes optimal agricultural actions to users based on the analysis results. The specific hardware and software configurations and their operation are described below.

[1525] System configuration

[1526] The system consists of the following main components:

[1527] 1. Sensor device

[1528] These IoT sensors collect crop and environmental data (temperature, humidity, soil pH, water content, light intensity, etc.) in real time, enabling detailed environmental information within the farm to be obtained. Specifically, they include soil humidity and temperature sensors.

[1529] 2. Communications Equipment

[1530] This is a communications device that transmits collected data to a central processing unit using 5G communications, enabling fast and reliable data transmission.

[1531] 3. Central Processing Unit

[1532] This is a central processing unit that receives, manages, and analyzes the transmitted data. Specifically, a database is placed on the server and used to store and analyze various data. The server stores the collected data and uses analytical algorithms to optimize agriculture.

[1533] 4. Analysis device

[1534] The server uses AI-based analytical algorithms to analyze the collected data and evaluate the agricultural situation. This allows for the determination of the health of crops and necessary agricultural actions based on environmental data. Specifically, the analysis is carried out using machine learning models and neural networks.

[1535] 5. Advice Generator

[1536] Based on the analysis results, the system proposes agricultural actions and provides advice on optimal cultivation methods, such as "spray specific pesticides in the right amounts" or "add specific fertilizer to adjust the soil pH."

[1537] 6. Notification device

[1538] This device sends and notifies the user of advice generated by the server via 5G communication, enabling real-time notifications. Users can receive advice on their smartphones, tablets, or other devices.

[1539] 7. Feedback Devices

[1540] This is a device for transmitting feedback data obtained from user terminals to the central processing unit. Users input the agricultural actions they performed and their results, and send this to the server.

[1541] Specific examples

[1542] For example, if crops are placed in a high-humidity environment during the rainy season, the sensor device will collect humidity data frequently. This data will then be immediately sent to a central processing unit using 5G communications. The server will then analyze the data and detect that humidity may increase the risk of pest infestation in the crops. Based on the analysis results, the advice generation device will suggest an appropriate pest control method (e.g., early application of a specific pesticide). A notification will be sent to the user's device, and the user will follow the instructions to quickly spray the pesticide, thereby preventing the occurrence of pests and diseases. The user will then input the results of their actions into a feedback device and send it to the server. The server will use this feedback for analysis to improve the accuracy of future advice.

[1543] Prompt Sentence Examples

[1544] Below is an example of a prompt sentence to input to a generative AI model (e.g., ChatGPT).

[1545] Please explain the following agricultural support systems that utilize AI, IoT, and 5G technologies:

[1546] The sensor collects temperature and humidity data and transmits it to a server via 5G communication.

[1547] The server analyzes the received data and evaluates the health of the crops.

[1548] If necessary, it generates suggestions for agricultural actions (e.g., spraying pesticides) and notifies the user.

[1549] After the user performs an action, the result is given as feedback.

[1550] Please explain with specific examples.

[1551] The above is an embodiment of the invention.

[1552] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1553] Program processing

[1554] Step 1: Data collection

[1555] Subject: Sensor device

[1556] The sensor device collects environmental data such as temperature, humidity, soil pH, water content, and light intensity using various sensors placed around the farm.

[1557] Input: Environmental data on soil and air temperature, humidity, pH, and light intensity

[1558] Data processing and calculation: The sensor measurement values ​​are temporarily stored in the memory of the sensor device.

[1559] Output: Temporarily saved environmental data

[1560] Specific operation: The humidity sensor measures the humidity at 80% and records the data in memory.

[1561] Step 2: Send data

[1562] Subject: Communication device

[1563] The communication device transmits data collected from the sensor device to the central processing unit in real time, using 5G communication to achieve high-speed and stable data transmission.

[1564] Input: Temporarily saved environmental data (e.g. humidity 80%)

[1565] Data processing and calculation: Data is transmitted to the central processing unit using the 5G communication module.

[1566] Output: Environmental data received by the server

[1567] Specific operation: Humidity data (80%) is sent to the server via 5G communication.

[1568] Step 3: Save Data

[1569] Subject: Server

[1570] The server stores the received environmental data in an analysis database.

[1571] Input: Received environmental data (humidity 80%)

[1572] Data processing and calculation: Register environmental data in the database.

[1573] Output: Data stored in the database

[1574] Specific operation: The server saves the humidity data (80%) in the database.

[1575] Step 4: Data analysis

[1576] Subject: Server

[1577] The server uses AI models (e.g., machine learning algorithms) to analyze the data, which is then used by the analysis device to assess the health of the crops and the agricultural actions required.

[1578] Input: Environmental data stored in a database

[1579] Data processing and calculation: Data analysis is performed using AI models and machine learning algorithms.

[1580] Output: Analysis results (indicating that high humidity increases the risk of pests)

[1581] Specific operation: The server performs an analysis and determines that the humidity level of 80% is outside a specific range, increasing the risk of pests and diseases.

[1582] Step 5: Advice Generation

[1583] Subject: Server

[1584] The server generates appropriate agricultural actions based on the analysis results, and the advice generator creates specific advice and recommends it to the user.

[1585] Input: Analysis results (increase in pest risk)

[1586] Data processing and calculation: Based on the analysis results, appropriate agricultural actions (e.g., spraying pesticides) are generated.

[1587] Output: Generated advice

[1588] Specific operation: The server generates advice such as "Because the humidity is high, please spray 5 liters of a specific pesticide (brand name: XY)."

[1589] Step 6: Advice Notification

[1590] Subject: Server

[1591] The server passes the generated advice to the notification device, which then transmits the advice to the user device using 5G communications. The user device then displays the advice and notifies the user.

[1592] Input: Generated advice

[1593] Data processing and calculation: Advice is sent using 5G communication.

[1594] Output: Advice displayed on the user's terminal

[1595] Specific operation: The server sends advice to the smartphone, which then displays a notification saying, "Due to high humidity, please spray 5 liters of pesticide XY."

[1596] Step 7: Take Action

[1597] Subject: User

[1598] The user performs agricultural actions based on the notified advice.

[1599] Input: Received advice

[1600] Data processing and calculation: Implement specific agricultural actions based on the advice.

[1601] Output: Farming actions performed

[1602] Specific behavior: The user receives a notification on their smartphone and sprays 5 liters of pesticide XY.

[1603] Step 8: Gather feedback

[1604] Subject: User

[1605] The user inputs the agricultural actions they have performed and their results into the user terminal, and the feedback device transmits the input feedback to the server.

[1606] Input: The result of the agricultural action performed

[1607] Data processing and calculation: The results are entered into the user's terminal and sent to the server.

[1608] Output: Feedback data sent to the server

[1609] Specific operation: The user types "Pesticide XY was sprayed" into their smartphone and sends it.

[1610] Step 9: Use feedback

[1611] Subject: Server

[1612] The server adds the received feedback to the analysis data and uses it to improve the accuracy of advice from the next time onwards.

[1613] Input: Feedback data

[1614] Data processing and calculation: Feedback is reflected in the analysis and the database is updated.

[1615] Output: Improved advice for next time

[1616] Specific operation: The server uses the feedback that "after spraying pesticide XY, the humidity returned to the appropriate value" for analysis, and improves the accuracy of mold prevention advice from the next time onwards.

[1617] The above are the specific steps and details of the system's program processing.

[1618] (Application example 1)

[1619] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1620] Conventional factory automation systems have issues with real-time response and accuracy when it comes to detecting equipment anomalies and proposing maintenance. It's also difficult to quickly notify appropriate actions, making it impossible to maximize production efficiency and equipment lifespan. This leads to unnecessary downtime and excessive maintenance, which increases production costs and increases the burden on workers.

[1621] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1622] In this invention, the server includes a sensor means for collecting environmental data, a transmission means for transmitting the data collected from the sensor means to a central processing unit via a communication means, an analysis means for analyzing the data and promoting appropriate manufacturing processes and maintenance actions, an advice generation means for proposing manufacturing processes and maintenance actions based on the analysis, a notification means for notifying a user device of the advice, and a feedback means for transmitting feedback data obtained from the user device to the central processing unit. This enables real-time data collection within the factory and highly accurate analysis using AI to propose optimal actions. Furthermore, the use of 5G communications enables high-speed data transmission, allowing for prompt maintenance and process adjustments as needed. This minimizes equipment downtime and improves production efficiency.

[1623] "Environmental data" refers to data related to the machinery and working environment within the factory, such as temperature, humidity, and vibration.

[1624] "Sensor means" refers to devices and techniques for collecting environmental data in real time.

[1625] "Communication Means" refers to devices and technologies for transmitting data collected from the Sensor Means, including in particular those utilizing fifth generation mobile communication systems (5G).

[1626] "Transmitting Means" refers to devices and techniques for transmitting environmental data from the Sensor Means to the Central Processing Unit.

[1627] "Central Processing Unit" means a computer system that receives, stores, and analyzes collected data.

[1628] "Analysis Tools" refers to devices and algorithms used to analyze collected environmental data and identify appropriate manufacturing process or maintenance actions.

[1629] "Advice generation means" refers to devices and techniques that generate suggestions for manufacturing processes and maintenance actions based on the analysis results obtained by the analysis means.

[1630] "Notification means" refers to devices and techniques for transmitting and notifying the user device of the suggestions generated by the advice generation means.

[1631] A "user device" is a device on which a user receives notifications, and includes a smartphone or tablet.

[1632] "Feedback means" refers to devices and techniques for transmitting feedback data obtained from user devices to a central processing unit.

[1633] This invention is a system that collects data on equipment and the environment within a factory, analyzes that data, and proposes optimal manufacturing processes and maintenance actions. The system consists of the following main components:

[1634] System configuration

[1635] The system consists of the following main components:

[1636] 1. Sensor means

[1637] The sensor means is a device or technology for collecting environmental data such as temperature, humidity, and vibration in a factory in real time. This includes temperature sensors, humidity sensors, vibration sensors, etc.

[1638] 2. Transmission Method

[1639] The transmission means is a device and technology for transmitting data collected by the sensor means to the central processing unit, and uses 5G communication, enabling high-speed and stable data transmission.

[1640] 3. Central Processing Unit

[1641] The central processing unit is a computer system that receives, stores, and analyzes collected data. Specifically, it includes a database server and an AI analysis server.

[1642] 4. Analysis method

[1643] The analysis method is an analytical algorithm using artificial intelligence in the central processing unit, which analyzes the collected data and determines whether the manufacturing process or maintenance is necessary. TensorFlow and Keras are used for the analysis.

[1644] 5. Advice Generation Methods

[1645] The advice generator is a system that proposes optimal manufacturing processes and maintenance actions based on the results obtained from the analysis means, including appropriate maintenance timing and specific action plans.

[1646] 6. Means of notification

[1647] The notification means is a device or technology for transmitting the generated advice to a user device, such as a smartphone or tablet.

[1648] 7. Feedback channels

[1649] The feedback means is an interface for transmitting feedback data obtained from the user device to the central processing unit. By inputting the actions taken by the user and their results, the central processing unit uses this information for further analysis and optimization.

[1650] Program processing

[1651] Data collection and transmission

[1652] The sensor means continuously collects environmental data from each sensor inside the factory, such as temperature, humidity, vibration, etc. The transmission means transmits the collected environmental data to the central processing unit using 5G communication.

[1653] Data analysis and advice generation

[1654] The central processing unit stores newly received data in an analysis database. The analysis means analyzes the data using an AI model and evaluates the health of the equipment and the necessary manufacturing and maintenance actions. Specifically, if vibrations exceed a certain threshold, it detects the high possibility that an abnormality has occurred. The advice generation means generates advice that suggests the optimal manufacturing and maintenance actions based on the analysis results.

[1655] Advice notifications and feedback collection

[1656] The notification means sends the generated advice to the user device and notifies it. The user can check the notification on a smartphone or tablet. The user then performs manufacturing or maintenance actions based on the notification. Specifically, this could include performing machine maintenance or adjusting process settings. The feedback means provides an interface that allows the user to input the results of the actions they have taken and send them to the central processing unit. This allows the effectiveness of the actions to be reported. The central processing unit uses this feedback for analysis and improves the accuracy of advice from the next time onwards.

[1657] Specific examples

[1658] For example, if a robot arm detects abnormal vibrations during production in a factory, the sensor means immediately collects and transmits the data. The central processing unit analyzes this data and, if it determines that machine maintenance is necessary, the advice generation means immediately suggests that "maintenance of this robot arm is necessary." The user receives the notification and promptly performs maintenance work according to the instructions. The results are entered into the user device and transmitted to the central processing unit as feedback data. This improves the accuracy of abnormality detection and maintenance actions from the next time onwards.

[1659] Example prompt for a generative AI model:

[1660] Design a system that collects and analyzes sensor data, specifically temperature, humidity, and vibration data, to propose optimal maintenance methods for equipment.

[1661] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1662] Step 1:

[1663] The sensor means collects environmental data such as temperature, humidity, and vibration in the factory in real time.

[1664] Specifically, each sensor measures data at a specified interval and temporarily stores the data in memory. The input is environmental data, and the output is the collected environmental data.

[1665] Step 2:

[1666] The transmitting means transmits the collected environmental data to the central processing unit using 5G communication.

[1667] Specifically, the data stored in the sensor's memory is converted into packets and sent to a central processing unit via a 5G modem. The input is the collected environmental data, and the output is the data sent to the central processing unit.

[1668] Step 3:

[1669] The central processing unit stores the newly received data in a database.

[1670] Specifically, the received data is written to an analysis database and a timestamp is added. The input is the data received via 5G communication, and the output is the data stored in the database.

[1671] Step 4:

[1672] The analytics tool uses AI models to analyze the data and assess the health of the equipment and any necessary production or maintenance actions.

[1673] Specifically, using a machine learning framework such as TensorFlow, collected data is input into a trained model to detect anomalies and identify necessary actions. The input is the data stored in the database, and the output is the analysis results.

[1674] Step 5:

[1675] The advice generation means generates advice that proposes optimal manufacturing and maintenance actions based on the analysis results.

[1676] Specifically, specific advice such as "This robot arm needs maintenance" is generated based on the analysis results. This identifies the necessary maintenance work and adjustments to the manufacturing process. The input is the analysis results, and the output is the generated advice.

[1677] Step 6:

[1678] The notification means transmits the generated advice to the user device to notify it.

[1679] Specifically, a messaging protocol is used via a notification server to notify the generated advice to the user's smartphone or tablet. The input is the generated advice, and the output is the notification to the user device.

[1680] Step 7:

[1681] Users can take production and maintenance actions based on notifications.

[1682] Specifically, the user checks the notification content and performs machine maintenance or process adjustments according to the instructions. The input is the notification received by the user device, and the output is the action taken.

[1683] Step 8:

[1684] A feedback means provides an interface for inputting and transmitting results of actions taken by the user to the central processing unit.

[1685] Specifically, the results of the actions taken by the user are input into the application, and the data is sent back to the central processing unit. The input is the result of the action taken, and the output is the feedback data sent to the central processing unit.

[1686] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1687] This invention is an agricultural support system that utilizes AI, IoT, 5G technology, and an emotion engine. It not only collects data on crops and the surrounding environment and suggests optimal agricultural actions to users based on the analysis results, but also recognizes the user's emotions and adjusts the content and presentation of advice to provide more effective agricultural support.

[1688] System configuration

[1689] The system consists of the following main components:

[1690] 1. Sensor means

[1691] It is an IoT sensor for collecting crop and environmental data (temperature, humidity, soil pH, water content, light intensity, etc.) in real time.

[1692] 2. Transmission Method

[1693] This is a communication device that transmits collected data to a server using 5G communication.

[1694] 3. Server

[1695] This is the central processing unit that receives, manages, and analyzes the transmitted data. The server stores the collected data and uses analytical algorithms to optimize agriculture.

[1696] 4. Analysis method

[1697] The server uses an AI-based analytical algorithm to analyze the collected data and evaluate the agricultural situation.

[1698] 5. Advice Generation Methods

[1699] Based on the analysis results, the system suggests agricultural actions and provides advice on optimal cultivation methods.

[1700] 6. Means of notification

[1701] This is a means for notifying the user terminal of the advice generated by the server and conveying it to farmers.

[1702] 7. User Devices

[1703] The devices farmers use to receive advice and send feedback include smartphones and tablets.

[1704] 8. Feedback channels

[1705] This is an interface for users to input the agricultural actions they have performed and the results of those actions, and send them to the server.

[1706] 9. Emotion Engine

[1707] This is a device that analyzes emotions from the user's voice and facial expressions and feeds the results back to the server.

[1708] System action

[1709] Data collection and transmission

[1710] The sensor means continuously collects environmental data from various sensors around the farm, such as soil pH, temperature, humidity, and water content.

[1711] The transmission means transmits the collected environmental data to the server using 5G communication, which enables high-speed and stable data transmission.

[1712] Data analysis and advice generation

[1713] The server stores the newly received data in an analysis database.

[1714] The analytics tool uses AI models to analyze the data and assess the health of the crops and the agricultural actions they need to take, such as detecting patterns of temperature and humidity fluctuations and determining whether the crops are at risk.

[1715] The advice generator generates advice that suggests optimal agricultural actions based on the analysis results, such as "spray a specific pesticide in the right amount" or "add a specific fertilizer to adjust the soil pH."

[1716] Advice Notification and Emotion Recognition

[1717] The notification means notifies the user terminal of the generated advice. Specifically, the advice content is sent as a push notification to a smartphone or tablet so that the user can immediately check it.

[1718] The emotion engine analyzes the user's voice and facial expressions and feeds back their emotional state to the server. For example, if the user is in a stressful state, that information is sent to the server.

[1719] Emotion-based advice adjustment

[1720] The server receives feedback from the emotion engine and adjusts the content and expression of advice in the advice generation means. For example, if the user is under stress, advice containing encouraging words is generated to increase the user's motivation.

[1721] Engaging with users and gathering feedback

[1722] The user device receives and displays the notification, allowing the user to review the specific agricultural action recommendations.

[1723] The user performs agricultural actions based on the advice presented on the user terminal. For example, if the advice includes "add fertilizer," the user selects an appropriate fertilizer and applies the recommended amount to the farmland.

[1724] The feedback means provides an interface for inputting the results of the agricultural actions performed by the user, and the user inputs the details of the work that he or she actually performed and the results of the work into this interface.

[1725] The user terminal transmits the input feedback data to the server. Specifically, the feedback data is packetized and transmitted to the server via 5G communication.

[1726] The server analyzes the received feedback data and uses it to improve the accuracy of the analytical model. The server stores the feedback data in a database and uses it to train the AI ​​model, improving the accuracy of advice from the next time onwards.

[1727] Specific examples

[1728] For example, if crops are placed in a high-humidity environment during the rainy season, the sensor means will collect humidity data frequently. This data will then be immediately sent to the server using 5G communications. The server will analyze this data and detect that humidity may increase the risk of pest infestation. Based on the analysis results, the advice generation means will suggest an appropriate pest control method (e.g., early application of a specific pesticide). A notification will be sent to the user's device, and the user will quickly follow the instructions to spray the pesticide. In addition, the emotion engine will analyze the user's voice and facial expressions. If the server detects that the user is under stress, the server will generate advice that includes encouraging words. The user then inputs the results of their actions into the feedback means and sends it to the server. The server will use this feedback for analysis to improve the accuracy of future advice.

[1729] This not only provides a consistent system that supports agricultural productivity and sustainability, but also responds to the user's emotional state, resulting in more effective agricultural support.

[1730] The processing flow will be explained below.

[1731] Step 1:

[1732] The sensor means collects real-time environmental data on the crops and their surroundings (temperature, humidity, soil pH, water content, light intensity, etc.). Each sensor periodically obtains its own measured value and temporarily stores the data in its internal memory.

[1733] Step 2:

[1734] The transmission means packetizes the collected environmental data and transmits it to the server using 5G communication, allowing the environmental data to be transmitted to the server at high speed and with high stability.

[1735] Step 3:

[1736] The server receives the environmental data sent from the sensor means and first checks its consistency. After the data consistency is confirmed, it stores it in a database and prepares it for analysis.

[1737] Step 4:

[1738] The analysis means stores the received data in an analytical database and inputs the data into an AI model, which compares it with past data to assess the current environmental conditions and the health of the crops. For example, it analyzes patterns of temperature and humidity fluctuations to predict whether the crops are at risk of pests or diseases.

[1739] Step 5:

[1740] The advice generation means generates specific agricultural action proposals based on the analysis results. For example, if humidity is high and the risk of a specific pest is high, the system generates specific advice such as "spray a specific pesticide in the appropriate amount."

[1741] Step 6:

[1742] The emotion engine recognizes emotions from the user's voice and facial expressions and sends the data to the server. The emotion engine determines the user's emotional state, such as stress or satisfaction, from their reaction.

[1743] Step 7:

[1744] The server analyzes the emotion data received from the emotion engine, and if the user is feeling stressed, feeds back that information to the advice generating means.

[1745] Step 8:

[1746] The advice generation means adjusts the content and expression of the advice based on the analysis results of the emotion engine. For example, if the user is in a high-stress state, the advice generation means generates advice that includes kinder expressions and encouraging words.

[1747] Step 9:

[1748] The notification means sends the generated advice to the user terminal, and the advice content is displayed on the user's smartphone or tablet via push notification.

[1749] Step 10:

[1750] The user terminal displays the received advice, and the user confirms it and plans agricultural actions based on the advice.

[1751] Step 11:

[1752] The user follows the advice displayed on the user terminal and performs specific agricultural actions. For example, if the advice is to "spray a specific pesticide in the appropriate amount," the user will spray the indicated amount of pesticide on the field.

[1753] Step 12:

[1754] The feedback means provides an interface for inputting the results of the agricultural actions performed by the user. The user inputs the details of the work actually performed and the results, and saves them in the terminal.

[1755] Step 13:

[1756] The user terminal packetizes the input feedback data and transmits it again to the server via 5G communication.

[1757] Step 14:

[1758] The server analyzes the received feedback data and uses it to improve the accuracy of the analytical model. The feedback data is stored in a database and used to train the AI ​​model, improving the accuracy of advice from the next time onwards.

[1759] This will enable a more effective agricultural support system that not only continuously monitors the health and growth environment of crops and suggests optimal agricultural actions, but also responds to the user's emotional state.

[1760] Example 2

[1761] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1762] In modern agriculture, the collection and analysis of environmental data to propose appropriate agricultural actions is extremely important. However, conventional systems do not adequately analyze the collected data or generate advice, and in particular lack support that takes into account the user's emotional state. This reduces user motivation and makes it difficult to provide effective agricultural support. Furthermore, delays in real-time data transmission and analysis can lead to reduced productivity and increased agricultural risks. Effective methods to resolve these issues are needed.

[1763] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a sensor means for collecting environmental data, a transmission means for transmitting data collected from the sensor means to the server via a communication means, an analysis means for analyzing the data and encouraging appropriate agricultural actions, an advice generation means for suggesting agricultural actions based on the analysis, a notification means for notifying the user terminal of the advice, an emotion analysis means for analyzing the emotional state of the user, an emotion adaptation means for adjusting the content and expression of the advice based on the results of the emotion analysis means, and a feedback means for transmitting feedback data obtained from the user terminal to the server. This enables effective agricultural support that takes the user's emotional state into consideration, and by realizing data transmission and analysis in real time, agricultural productivity and efficiency are significantly improved.

[1764] A "sensor means" is a device for collecting environmental data.

[1765] The "transmitting means" is a device for transmitting data collected from the sensor means to the server via the communication means.

[1766] The "analysis means" is a device that analyzes the received data and encourages appropriate agricultural actions.

[1767] The "advice generator" is a device for suggesting agricultural actions based on the results of the analysis means.

[1768] The "notification means" is a device for notifying the user terminal of the generated advice.

[1769] The "emotion analysis means" is a device for analyzing the user's emotional state.

[1770] The "emotion adaptation means" is a device for adjusting the content and expression of advice based on the results of the emotion analysis means.

[1771] The "feedback means" is a device for transmitting feedback data obtained from a user terminal to a server.

[1772] "Communication medium" is the technology used to send and receive data.

[1773] A "user terminal" is a device through which a user receives advice and sends feedback.

[1774] Basic system configuration

[1775] The system utilizes AI, IoT, 5G technology, and an emotion engine to collect data on crops and the surrounding environment, and then suggests optimal agricultural actions to users based on the analysis results.It also analyzes the user's emotional state and adjusts the content and presentation of advice to provide effective agricultural support.

[1776] Specifically, the following hardware and software are used.

[1777] Main hardware used:

[1778] IoT sensors: temperature sensors, humidity sensors, soil pH sensors, moisture sensors, light intensity sensors, etc.

[1779] Communications equipment: 5G-compatible modems and routers.

[1780] Server: The central processing unit responsible for collecting, analyzing, storing data and generating advice.

[1781] User device: A device such as a smartphone or tablet that allows a user to receive advice and send feedback.

[1782] Emotion engine: Camera and microphone for analyzing the user's voice and facial expressions.

[1783] Main software used:

[1784] AI model: The machine learning algorithm used to analyze data and generate advice.

[1785] Database software: NoSQL database, e.g. MongoDB.

[1786] Analysis algorithms: Software that analyzes environmental data such as temperature, humidity, soil pH, water content, and light intensity to assess crop health and any necessary agricultural actions.

[1787] Notification software: A mobile application for sending push notifications.

[1788] System operation example

[1789] For example, if crops are placed in a high-humidity environment during the rainy season, the sensor will collect humidity data frequently. The device will then transmit the data obtained from the humidity sensor to a server via 5G communication. The server will then receive the data and store it in a database.

[1790] The server uses an AI analysis model to analyze humidity data and detect whether humidity can increase the risk of pests and diseases in crops. Based on the analysis results, the advice generation means generates advice suggesting appropriate pest control methods (e.g., early application of a specific pesticide).

[1791] The generated advice is sent as a push notification to the user's smartphone or tablet via the notification means. The user receives the advice and sprays the pesticide as instructed. The emotion engine analyzes the user's voice and facial expression, and if it determines that the user is under stress, the server generates advice including words of encouragement.

[1792] Example prompt sentence:

[1793] "We've detected that the user is under stress. Please generate friendly, encouraging advice."

[1794] The user then sends the results of their agricultural actions to the server via a feedback mechanism. The server analyzes this feedback data, stores it in a database, and uses it as training data to improve the accuracy of advice in future.

[1795] effect

[1796] This system enables real-time, highly accurate data analysis and provides personalized agricultural support that takes into account the user's emotional state, thereby improving agricultural productivity and efficiency and enabling sustainable agricultural practices.

[1797] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1798] Step 1: Data collection

[1799] The terminal (sensor means) collects environmental data within the farm in real time. The sensor means uses temperature sensors, humidity sensors, soil pH sensors, moisture sensors, light intensity sensors, etc. to obtain data on temperature, humidity, soil pH, moisture content, light intensity, etc. For example, the temperature sensor measures 25°C, and the humidity sensor measures 80%.

[1800] Input: Environmental data

[1801] Output: Raw data from the sensor

[1802] Step 2: Send data

[1803] The environmental data collected by the device is sent to a server using 5G communication. Specifically, the data obtained from each sensor is packaged in JSON format and sent over the 5G network. For example, data such as {"temperature": 25, "humidity": 80} is sent to the server.

[1804] Input: Raw data from the sensor

[1805] Output: JSON data sent to the server via the communication method

[1806] Step 3: Data reception and storage

[1807] The server receives the data sent from the device and stores it in a database for analysis. For example, the server's API receives JSON data and executes a process to store it in a NoSQL database.

[1808] Input: JSON data sent via communication means

[1809] Output: Environmental data stored in a database

[1810] Step 4: Data analysis

[1811] The server analyzes the data using an AI analysis model. For example, it determines that the risk of pests and diseases increases when humidity levels remain high. The server compares this data with past data and makes a risk assessment. Specifically, it analyzes temperature and humidity patterns to assess the health of the crops.

[1812] Input: Environmental data stored in a database

[1813] Output: Analysis results (risk of pests, etc.)

[1814] Step 5: Advice Generation

[1815] The server generates advice based on the analysis results. For example, it generates specific instructions such as "Because humidity is high, spray a specific pesticide now." The advice generation means proposes optimal agricultural actions based on the analysis results.

[1816] Input: Analysis results

[1817] Output: Specific advice

[1818] Step 6: Advice Notification

[1819] The server sends the generated advice to the user's device. The advice is sent to the user in real time using push notifications. For example, a notification saying "Please spray pesticides now" is displayed on the smartphone.

[1820] Input: Specific advice

[1821] Output: Notification to user terminal

[1822] Step 7: User sentiment analysis

[1823] The emotion engine analyzes the user's voice and facial expression and transmits the user's emotional state to the server. For example, the emotion engine determines that the user is feeling stressed based on the user's tone of voice and facial expression.

[1824] Input: Audio and visual data

[1825] Output: Parsed emotion data

[1826] Step 8: Adjusting Advice Based on Emotions

[1827] The server adjusts the content and expression of the advice based on the results of emotion analysis. For example, for a user who is under stress, it adds an encouraging message such as, "Thank you for your hard work. Please take a short break before proceeding to the next step."

[1828] Input: Parsed emotion data

[1829] Output: Adjusted advice

[1830] Step 9: User Actions and Feedback Collection

[1831] The user receives the notification and takes action based on the advice, for example, spraying a specific pesticide as instructed. The feedback means provides an interface that records the actions taken by the user, and the user inputs the results, such as "the pesticide has been sprayed."

[1832] Input: User action result

[1833] Output: Feedback data

[1834] Step 10: Sending and using feedback data

[1835] The user device sends the recorded feedback data to a server via 5G communication. The server receives the feedback data and uses it for analysis. The server uses the feedback data to train the AI ​​model and improve the accuracy of advice from the next time onwards.

[1836] Input: Feedback data sent from the user device

[1837] Output: Updated analytical model

[1838] By repeatedly performing these steps, the system not only continues to support the user's agricultural activities, but also provides personalized support that is attuned to the user's emotional state.

[1839] (Application example 2)

[1840] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1841] Conventional security systems collect environmental data and detect anomalies, but lack the ability to adjust alerts based on the emotional state of security guards. This makes it difficult for stressed security guards to respond quickly and accurately. The present invention aims to solve this problem by providing a system that realizes effective security responses that take emotional states into account.

[1842] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes sensor means for collecting environmental data, transmission means for transmitting data collected from the sensor means to the server via communication means, analysis means for analyzing the data and encouraging appropriate action, advice generation means for suggesting an action based on the analysis, notification means for notifying the user terminal of the advice, an emotion engine for acquiring user emotion data, advice adjustment means for adjusting the content of advice based on the emotion data, and feedback means for transmitting feedback data obtained from the user terminal to the server. This enables effective alert notification that takes into account the emotional state of the security guard.

[1843] "Environmental Data" refers to information about the environment, such as ambient temperature, humidity, movement, and sound.

[1844] "Sensor means" refers to a device or group of devices that collect environmental data in real time.

[1845] "Transmission means" refers to a device or function that transmits collected data to a server via a communication means.

[1846] "Analysis means" refers to the function of using the server's algorithms and programs to analyze collected data and determine abnormalities and necessary actions.

[1847] The "advice generation means" refers to a function that generates specific actions and suggestions for the user based on the results obtained by the analysis means.

[1848] The "notification means" refers to a function or device for transmitting the generated advice or warning to the user terminal.

[1849] "User terminal" refers to a device that can receive and operate information, such as a smart device or HMD used by security guards.

[1850] An "emotion engine" refers to a function or device that analyzes the user's emotional state from their voice and facial expressions, and acquires and transmits that data.

[1851] "Advice adjustment means" refers to a function that adjusts the content and expression of advice based on emotional data obtained from the emotion engine.

[1852] "Feedback means" refers to an interface or function for recording the actions performed by the user and the results thereof, and transmitting them to the server.

[1853] System Overview

[1854] This invention is a security system designed to support security guards. This system combines IoT sensors, 5G communications, AI analysis, and an emotion engine to easily realize anomaly monitoring and emotion recognition. This section explains the details of each component and how to combine them.

[1855] System configuration

[1856] Sensor Means

[1857] The system includes multiple IoT sensors (e.g., temperature, humidity, motion, and sound sensors) to collect environmental data. These sensors are installed in different locations within the facility and collect data in real time. The collected environmental data includes temperature, humidity, motion, and sound.

[1858] Transmission method

[1859] The environmental data collected by the sensors is sent to a server using 5G communication, which enables high-speed and stable data transmission.

[1860] server

[1861] The server is a central processing unit that receives, manages, and analyzes the environmental data sent to it. The environmental data is stored in a database within the server, and an analysis algorithm using an AI model (for example, TensorFlow) evaluates the data as an analysis method.

[1862] Analysis means

[1863] The analysis means analyzes the collected data to detect anomalies, for example, if a motion sensor detects suspicious activity within the facility, the data is flagged as an anomaly.

[1864] Advice Generation Method

[1865] Based on the analysis results, advice is generated that suggests appropriate actions to take. For example, when an abnormality is detected, an instruction such as "urgent action is required" is generated.

[1866] Notification means

[1867] The generated advice and warnings are sent to the user's device (smart glasses, head-mounted display, etc.) via a notification means, allowing security guards to receive prompt alerts.

[1868] Emotion Engine

[1869] The emotion engine is a device that analyzes emotions from the user's voice and facial expressions and sends the data to a server. For example, if the user is in a stressful state, the data is sent to the server.

[1870] Advice adjustment measures

[1871] The server adjusts the advice content based on the emotion data obtained from the emotion engine. For example, if a user is under stress, the server generates advice that includes encouraging words, providing advice in a way that is optimal for the user.

[1872] Feedback Methods

[1873] The actions taken by the user and their results are sent to the server via a feedback mechanism, which allows the server to use the accumulated feedback data for analysis and to improve the accuracy of the AI ​​model.

[1874] Specific examples

[1875] For example, if a motion sensor installed in a facility detects abnormal activity, the data is immediately sent to a server via 5G communication. The server analyzes the data and detects the abnormality, generating an alert stating that "emergency action is required." This alert is then sent to the user's device, such as smart glasses. At the same time, the emotion engine analyzes the facial expressions and voice of the security guard, and if it detects that the guard is under stress, the advice adjustment means generates advice including an encouraging message such as "Please stay calm." The user then takes action in accordance with the instructions and sends the results to the server using the feedback means. The server uses this feedback data in future analyses to improve the accuracy of the entire system.

[1876] Prompt Sentence Examples

[1877] "Please provide data to train an AI model for anomaly detection for a security monitoring system. Please output the data in the following format:

[1878] timestamp

[1879] Sensor ID

[1880] Sensor Value

[1881] Normal / abnormal label

[1882] As described above, the detailed description shows how components in a security system work together to function efficiently.

[1883] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1884] Step 1:

[1885] Sensors collect environmental data. Sensors (e.g., temperature, humidity, motion, and sound sensors) capture data in real time and store the data in internal memory. The input is the sensor reading (temperature, humidity, motion, sound, etc.) and the output is the collected environmental data.

[1886] Step 2:

[1887] The transmitting means transmits the collected environmental data to a server. The data collected from the sensor is transmitted to the server using 5G communication. The input is the environmental data collected by the sensor, and the output is the data transmitted to the server.

[1888] Step 3:

[1889] The server stores the received data in an analysis database. As soon as the server receives the data, it adds it to the database and uses it as basic data for analysis. The input is the environmental data sent from the transmission means, and the output is the data stored in the database.

[1890] Step 4:

[1891] The server's analytical means analyzes the data and detects anomalies. An AI model (using, for example, TensorFlow) evaluates the environmental data and detects abnormal behavior or conditions. The input is the environmental data stored in the database, and the output is the analysis result (normal / abnormal determination).

[1892] Step 5:

[1893] The advice generator proposes actions based on the analysis results. If an anomaly is detected, it generates appropriate countermeasures (e.g., emergency response instructions). The input is the server's analysis results, and the output is the proposed actions or warnings.

[1894] Step 6:

[1895] The notification means sends the advice to the user's terminal. The generated advice or warning is notified to the user's terminal, such as smart glasses or a head-mounted display. The input is the suggestion from the advice generation means, and the output is the advice / warning displayed on the user's terminal.

[1896] Step 7:

[1897] The emotion engine analyzes the user's emotional data. It acquires the user's voice and facial expression and evaluates their state using an emotion analysis algorithm. The input is the user's voice and facial expression data, and the output is the analyzed emotional data.

[1898] Step 8:

[1899] The advice adjustment means adjusts the advice based on the emotion data. It receives the emotion analysis results and optimizes the content and expression of the advice. The input is the emotion data from the emotion engine and the initial advice content, and the output is the adjusted advice.

[1900] Step 9:

[1901] The adjusted advice is displayed on the user's device. The adjusted content is displayed on the device, and the user confirms it and takes action. The input is the adjusted advice, and the output is the user's action.

[1902] Step 10:

[1903] The feedback means acquires the result of the user's action and transmits it to the server. The user provides feedback on the action and its result using an input device. The input is the result of the user's action, and the output is the feedback data transmitted to the server.

[1904] Step 11:

[1905] The server analyzes the feedback data and uses it for future analyses. The feedback data is stored in a database and reflected in the learning of the AI ​​model. The input is the sent feedback data, and the output is an updated analysis algorithm.

[1906] Through the above processing steps, the security system of the present invention functions effectively and can provide alert notifications and feedback that take into account the emotional state of the security guard.

[1907] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1908] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1909] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1910] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1911] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1912] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1913] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1914] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1915] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1916] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1917] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1918] In the above embodiment, an example was given in which the specific proc...

Claims

1. sensor means for collecting environmental data; a transmitting means for transmitting data collected from the sensor means to a server via a communication means; an analysis means for analyzing the data and prompting appropriate agricultural actions; an advice generator that suggests agricultural actions based on said analysis; notification means for notifying the user terminal of the advice; a feedback means for transmitting feedback data obtained from the user terminal to a server; A system including:

2. 10. The system of claim 1, wherein the communication means comprises means for utilizing a fifth generation mobile communication system (5G).

3. 2. The system of claim 1, wherein said analysis means includes an artificial intelligence based analysis algorithm.

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