System

Abstract

A system is provided.SOLUTION: A system comprising: sensor means for measuring soil condition, water content and light amount; terminal means for collecting data from the sensor means and transmitting the data to a server; server means for receiving the data transmitted from the terminal means and storing the data in a database; generation AI means for analyzing the data stored in the server means to generate insights on crop management, pest forecast and optimal use of fertilizers; means for transmitting the insights generated by the generation AI means to a terminal; means for displaying the insights from the terminal to a user; and means for the user adjusting a method of managing the farm based on the insights.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] Up until now, improving agricultural production efficiency and quality has required a great deal of effort and time, as well as specialized knowledge and experience. It has also been difficult to implement appropriate measures tailored to the region and conditions, limiting efforts to reduce risk and achieve sustainable agricultural production. Additionally, it has been difficult to grasp farm conditions in real time and develop optimal management measures, making it difficult to improve productivity. This invention utilizes generative AI models and digital sensors to solve these issues, aiming to stabilize agricultural production, build consumer trust, improve the efficiency of agricultural workers, and enrich their lives. [Means for solving the problem]

[0005] This invention provides a system including: sensor means for measuring soil condition, moisture content, and light intensity; terminal means for collecting data from the sensor means and transmitting it to a server; server means for receiving the data transmitted from the terminal means and storing it in a database; generative AI means for analyzing the data stored in the server means and generating insights regarding crop management, pest and disease prediction, and optimal fertilizer use; means for transmitting the insights generated by the generative AI means to the terminal; means for displaying the insights to a user from the terminal; and means for a user to adjust a farm management method based on the insights. The system further includes means for suggesting customization options suitable for a particular farm based on the data stored in the database, and means for a user to adjust platform settings based on the customization options. In this way, a system combining a generative AI model and digital sensors can achieve efficient and sustainable agricultural management in real time.

[0006] "Sensor means" refers to a device for measuring soil conditions, moisture content and light content.

[0007] The "terminal means" is a device for transmitting data collected from the sensor means to the server.

[0008] The "server means" is a device that receives data sent from the terminal means and stores it in a database.

[0009] The "generative AI means" is an artificial intelligence system that analyzes data stored in the server means and generates insights regarding crop management, pest and disease prediction, and optimal use of fertilizer.

[0010] "Insights" are specific recommendations or information on crop management or risk prediction that generative AI methods provide through data analysis.

[0011] "Database" means a storage device for storing data received by the server means.

[0012] "User" means an individual or organization that operates the system and manages the farm.

[0013] "Real-time" refers to data collection and analysis occurring immediately, providing information without delay. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

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

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0029] 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.

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

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] This invention relates to a smart agriculture platform that combines generative AI models and digital sensors to efficiently manage agricultural data and support future agricultural practices.

[0036] overview

[0037] The system consists of the following main components:

[0038] Sensor means: Devices for measuring soil condition, moisture content and light level.

[0039] Terminal means: A device for transmitting data collected from the sensor means to the server.

[0040] Server means: A device that receives data sent from the terminal means and stores it in a database.

[0041] Generative AI Means: An artificial intelligence system that analyzes data stored in the Server Means and generates insights for crop management, pest and disease prediction, and optimal use of fertilizer.

[0042] User: The person or organization that operates the system and manages the farm.

[0043] System program and processing explanation

[0044] Data collection

[0045] The sensor means measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. For example, it measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C.

[0046] Next, the terminal means collects the data. The collected data is converted into, for example, a JSON format and transmitted to the server means. Specifically, the data is transmitted using a data transmission protocol (for example, an HTTP POST request).

[0047] Data analysis

[0048] The server means stores the received data in a database. For example, it inserts the data into the database using an SQL query. The generation AI means analyzes this stored data. The data is input into a pre-trained AI model to generate a prediction result. For example, it may determine that there is a 20% moisture deficiency, that a pH value of 7.2 is appropriate, and that a light intensity of 1,500 lux is appropriate.

[0049] The server generates insights and specific recommended actions based on the analysis results, for example, "Water content is insufficient. Irrigation is recommended."

[0050] Providing insights

[0051] The server means transmits the generated analysis results and insights to the terminal means. For example, it transmits a message in JSON format saying, "Water level is insufficient. Irrigation is recommended." The terminal means displays the received analysis results and insights to the user in a visually easy-to-understand format. For example, it displays "Water level is insufficient. Irrigation is recommended" on a dashboard.

[0052] Real-time adjustments

[0053] Users can view analytics and insights on their device screens, scroll through dashboards to identify water deficiencies, and then adjust farm management as needed, such as turning on irrigation systems to replenish water.

[0054] Customization Options and Consulting

[0055] The server means proposes suitable customization options based on the data of a particular farm. For example, it may recommend a highly disease-resistant crop variety "X." The user can review the proposed customization options and adjust the settings. The user can select crop variety "X" from the platform's settings screen. Furthermore, the user can subscribe to consulting services as needed to receive additional advice from experts.

[0056] In this way, the smart agriculture platform of the present invention provides farmers with the data and insights they need to achieve more efficient and sustainable agricultural management.

[0057] The processing flow will be explained below.

[0058] Specific processing steps of the program

[0059] Step 1: Data collection

[0060] 1. The sensor means measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time.

[0061] Example: Measure soil moisture content at 20%, pH value at 7.2, light intensity at 1500 lux, and temperature at 25°C.

[0062] 2. The terminal collects the measured data from the sensor means.

[0063] Convert the data into a format (e.g. JSON).

[0064] Example: { "soil_moisture": 20, "pH": 7.2, "light": 1500, "temperature": 25}

[0065] 3. The device sends the collected data to the server.

[0066] Send data using a data transmission protocol (e.g., HTTP POST request).

[0067] Example: POST / data { "soil_moisture": 20, "pH": 7.2, "light": 1500, "temperature": 25}

[0068] Step 2: Save data

[0069] 1. The server stores the received data in a database.

[0070] Example: Inserting data into a database using an SQL query.

[0071] Example: INSERT INTO soil_data (moisture, pH, light, temperature) VALUES (20, 7.2, 1500, 25)

[0072] Step 3: Data analysis

[0073] 1. A generative AI model on the server analyzes the stored data.

[0074] Example: Inputting data into a pre-trained AI model to generate predictions.

[0075] The model determines that "20% moisture content is insufficient," "a pH value of 7.2 is neutral and appropriate," and "a light intensity of 1500 lux is appropriate."

[0076] 2. The server generates insights and recommended actions based on the analysis results.

[0077] For example: Generate the insight "Water level is insufficient. Irrigation recommended."

[0078] Step 4: Providing insights

[0079] 1. The server sends the generated analysis results and insights to the device.

[0080] Example: Send a message to the device in JSON format saying "Water level is low. Irrigation recommended."

[0081] 2. The device displays the received analysis results and insights to the user in a visually easy-to-understand format.

[0082] Example: Displaying "Insufficient moisture. Irrigation recommended" on the dashboard.

[0083] Step 5: Real-time adjustments

[0084] 1. The user looks at the device screen to see the analysis results and insights.

[0085] Example: Scroll through your dashboard to see if you're dehydrated.

[0086] 2. Users adjust how they manage their farms as needed.

[0087] For example: Turn on the irrigation system to replenish water.

[0088] Step 6: Customization options and consultation

[0089] 1. The server suggests suitable customization options based on the data for the specific farm.

[0090] Example: Propose a highly disease-resistant crop variety "X."

[0091] 2. The user reviews the suggested customization options and adjusts the settings.

[0092] Example: Select crop variety "X" from the platform settings screen.

[0093] 3. The user applies for consulting services as needed.

[0094] For example: Consult an expert online for additional advice.

[0095] In this way, the system supports efficient and sustainable agricultural management.

[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 management systems face challenges in efficiently collecting data from the entire farm and analyzing it in real time, making it difficult to perform appropriate crop management, predict pests and diseases, and optimize fertilizer use. They also face the problem of being unable to propose customized options suited to specific farms and adjust management methods accordingly.

[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 measures soil condition, moisture content, and light intensity; a terminal device that collects data from the sensor device and transmits it to the server; a server device that receives the data transmitted from the terminal device and stores it in a database; a generating AI device that analyzes the data stored in the server device and generates insights regarding crop management, pest and disease prediction, and optimal fertilizer use; a device that transmits the insights generated by the generating AI device to the terminal device; a device that displays the insights to a user from the terminal device; and a device that allows the user to adjust farm management methods based on the insights. This enables real-time data collection and analysis, appropriate crop management, pest and disease prediction, and optimal fertilizer use. It also enables the system to quickly and effectively propose customized options suitable for specific farms and adjust management methods based on those options.

[0101] A "sensor device" is a device for measuring soil conditions, moisture content, light intensity, and the like.

[0102] A "terminal device" is a device for transmitting data collected from a sensor device to a server.

[0103] A "server device" is a device that receives data sent from a terminal device and stores it in a database.

[0104] The "generative AI device" is an artificial intelligence system that analyzes data stored on a server device and generates insights into crop management, pest and disease prediction, and optimal fertilizer use.

[0105] A "display device" is a device for visually displaying insights from a terminal device to a user.

[0106] A "user" is an individual or organization that adjusts how they manage their farm based on the insights generated.

[0107] "Customization options" are options that suggest adjustments and settings that are suitable for a particular farm.

[0108] "Real-time adjustment" is the process by which users instantly change how they manage their farm based on the insights generated.

[0109] System Overview

[0110] The present invention relates to a smart agriculture platform that combines generative AI models and digital sensors to efficiently manage agricultural data and support future agricultural practices.

[0111] The system consists of the following main components:

[0112] Sensor devices: devices for measuring soil condition, moisture content and light intensity.

[0113] Terminal device: A device for transmitting data collected from a sensor device to a server.

[0114] Server device: A device that receives data sent from a terminal device and stores it in a database.

[0115] Generative AI device: An artificial intelligence system that analyzes data stored on a server device and generates insights for crop management, pest and disease prediction, and optimal fertilizer use.

[0116] Display device: A device for transmitting the insights generated by the generative AI device to a terminal device and visually displaying them to the user.

[0117] User: The person or organization that operates the system and manages the farm.

[0118] Examples of data collection

[0119] The sensor device measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. For example, it measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C. Next, the terminal device collects this data. The collected data is converted into, for example, JSON format and sent to the server device. Specifically, the data is sent using a data transmission protocol (for example, an HTTP POST request).

[0120] Specific examples of data analysis

[0121] The server device stores the received data in a database. For example, it inserts data into the database using an SQL query. The generative AI device performs analysis based on this stored data. The data is input into a pre-trained AI model to generate a prediction result. For example, it may determine that there is a 20% moisture deficiency, that a pH value of 7.2 is appropriate, and that a light intensity of 1,500 lux is appropriate. The server device generates insights and specific recommended actions based on the analysis results. For example, it may generate an insight such as, "There is a moisture deficiency. Irrigation is recommended."

[0122] Providing insights and real-time adjustments

[0123] The server device sends the generated analysis results and insights to the terminal device. For example, it sends a message in JSON format saying, "Water level is insufficient. Irrigation recommended." The terminal device displays the received analysis results and insights to the user in a visually easy-to-understand format. For example, it displays "Water level is insufficient. Irrigation recommended" on a dashboard.

[0124] Users can view analytics and insights on their device screens, scroll through dashboards to identify water deficiencies, and then adjust farm management as needed, such as switching on irrigation systems to replenish water.

[0125] Customization options and consulting

[0126] The server device proposes suitable customization options based on the data of a specific farm. For example, it may recommend a highly disease-resistant crop variety "X." The user can review the proposed customization options and adjust the settings. They can select crop variety "X" from the platform's settings screen. Furthermore, users can request consulting services to receive additional advice from experts as needed.

[0127] Prompt Sentence Examples

[0128] For example, if the following data is measured by a sensor device on a farm:

[0129] Land Data:

[0130] Soil moisture content: 15%

[0131] pH value: 6.8

[0132] Light output: 1300 lux

[0133] Temperature: 22℃

[0134] Based on this data, we input the following prompt to the generative AI model:

[0135] Based on this data, what are your recommended actions for crop management?

[0136] In this way, the smart agriculture platform of the present invention provides farmers with the data and insights they need to achieve more efficient and sustainable agricultural management.

[0137] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0138] Step 1:

[0139] The sensor device measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. Specifically, it measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C. This is used as input data.

[0140] Step 2:

[0141] The terminal device collects measurement data from the sensor device. The collected data is converted into JSON format and sent to the server. Specifically, the JSON format data is sent to the server using a data transmission protocol (for example, an HTTP POST request). This allows the server to receive the data collected from the sensor.

[0142] Step 3:

[0143] The server stores the received data in a database. Specifically, it inserts data into the database using an SQL query. It uses the received JSON data as input and obtains the state stored in the database as output.

[0144] Step 4:

[0145] The generative AI device analyzes the data stored in the database. The data is input into a pre-trained AI model to generate a predicted result. Specifically, it determines that there is a 20% moisture deficiency, that a pH value of 7.2 is appropriate, and that a light intensity of 1500 lux is appropriate. The data in the database is used as input data, and a predicted result is obtained as output.

[0146] Step 5:

[0147] The server generates insights based on the analysis results. Specifically, it generates a message based on the analysis results saying, "Water level is insufficient. Irrigation is recommended." It uses the prediction results as input data and obtains the generated insights as output.

[0148] Step 6:

[0149] The server sends the generated insight to the terminal device. Specifically, it sends a message in JSON format saying, "Water level is insufficient. Irrigation is recommended." The terminal device then receives the insight.

[0150] Step 7:

[0151] The terminal device displays the received insight to the user. Specifically, it displays "Water level is low. Irrigation recommended" on the dashboard. This allows the user to visually confirm the insight.

[0152] Step 8:

[0153] The user looks at the screen of their device to see the analysis results and insights. Specifically, they scroll through the dashboard to identify water deficiencies. If necessary, they adjust their farm management, for example, by switching on the irrigation system to replenish water. This ensures that the farm is properly managed.

[0154] Step 9:

[0155] The server then proposes appropriate customization options based on the data of a specific farm. Specifically, it generates an option such as "We suggest a highly disease-resistant crop variety 'X'." This allows the user to have useful choices.

[0156] Step 10:

[0157] The user reviews and applies the proposed customization options, specifically by selecting crop variety "X" in the platform's settings screen, thereby optimizing farm management.

[0158] (Application example 1)

[0159] 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."

[0160] Traditional agricultural management systems often lack the ability to collect, analyze, and provide insights in real time. This can make it difficult for farmers to respond quickly and appropriately, potentially resulting in reduced production efficiency and quality. Similar issues arise in factory environments, where effective management of temperature, humidity, light levels, and machine status data is required.

[0161] 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.

[0162] In this invention, the server comprises device means for measuring soil conditions, moisture content, and light intensity, terminal device means for collecting data from the device means and transmitting it to the server, storage means for receiving data transmitted from the terminal device means and storing it in a database, generation AI system means for analyzing the data stored in the storage means and generating insights regarding crop management, pest prediction, and optimal use of fertilizer, communication means for transmitting the insights generated by the generation AI system means to a terminal, visual display means for displaying the insights from the terminal to a user, management means for allowing the user to adjust the farm management method based on the insights, and temperature, humidity, and other factors of the factory environment. The system includes a device means for measuring temperature and light levels and collecting machine status data, a terminal device means for collecting data from the device means and transmitting it to a server, a storage device means for receiving the data transmitted from the terminal device means and storing it in a database, a generative AI system means for analyzing the data stored in the storage device means and generating insights for optimizing the production process, activating a cooling system, and reducing machine load, a communication means for transmitting the insights generated by the generative AI system means to a terminal, a visual display means for displaying the insights to a worker from the terminal, and a management means for the worker to adjust the production process based on the insights. This enables real-time data collection, analysis, and provision of insights in agricultural and factory environments, enabling prompt and appropriate responses.

[0163] "Soil condition" refers to the physical, chemical, and biological characteristics of the surface and subsurface of agricultural land.

[0164] "Moisture content" refers to the percentage of water contained in a particular area or substance.

[0165] "Light intensity" refers to the intensity or amount of light measured at a particular location.

[0166] "Device means" refers to the measuring instruments or equipment used to collect specific data.

[0167] "Terminal device means" refers to a communication device for collecting data and transmitting it to other devices or servers.

[0168] "Storage means" refers to a database or storage device for storing collected data.

[0169] "Generative AI system means" refers to a system that uses a pre-trained artificial intelligence model to analyze data and generate insights and predictions.

[0170] "Communications means" refers to technologies and devices for sending and receiving data and information.

[0171] "Visual display means" refers to devices or techniques for visually displaying data or insights to a user.

[0172] "Control measures" refers to methods and means for adjusting and controlling the way farms or factories are run in real time based on said insights.

[0173] "Machine status" refers to data indicating the state of a particular machine, such as whether it is running, stopped, or overloaded.

[0174] A "production process" refers to the series of operations or steps taken to produce a particular product or service.

[0175] "Cooling system" means a system or device for reducing the temperature of an environment or machine.

[0176] "Machine load" refers to the amount of work or workload being processed by a particular machine.

[0177] The embodiment of the present invention will be specifically described as follows: The system of the present invention is mainly composed of a device means, a terminal device means, a storage means, a generation AI system means, a communication means, a visual display means, and a management means.

[0178] System configuration and operation

[0179] Device Means

[0180] The device means is for measuring environmental data of a farm or factory in real time, for example, in the case of a farm it may include sensors measuring soil condition, moisture content, and light level, and in the case of a factory it may include sensors measuring temperature, humidity, light level, and machine status.

[0181] terminal device means

[0182] The end device is responsible for transmitting the data collected from the sensors to the server. This can be a communication device such as a smartphone or a head-mounted display. The data is converted to JSON format, for example, and sent to the server using an HTTP POST request.

[0183] storage means

[0184] The server receives the data sent from the terminal device and stores it in a database, using a database management system such as SQLite, where data is stored through SQL queries.

[0185] Generative AI system means

[0186] The data stored on the server is analyzed by pre-trained generative AI models, built using frameworks such as Scikit-Learn and TensorFlow, which generate insights for crop management, pest and disease prediction, optimal fertilizer use, optimizing production processes, activating cooling systems, and reducing machine loads.

[0187] communication means

[0188] The insights generated by the generative AI system are sent to the terminal via a communication means, again using the HTTP protocol, and the insight data is typically sent in JSON format.

[0189] Visual display means

[0190] The device is responsible for visually displaying the received insights, allowing users to review them through interfaces such as dashboards and graphs.

[0191] management measures

[0192] Based on the displayed insights, users can adjust management measures, such as activating irrigation systems or adjusting machine loads. For example, if temperatures in a factory become too high, cooling systems can be activated immediately. Production processes can also be optimized and machine loads adjusted as needed.

[0193] Adding specific examples

[0194] For example, if the temperature in a factory reaches 35°C, the sensor means measures this and the terminal device means transmits the data to the server. The server stores the data in a database, and the generating AI system means analyzes that the temperature is too high and generates an insight recommending the activation of a cooling system. This insight is transmitted to the terminal via the communication means, and an operator can view it via a head-mounted display.

[0195] Examples of prompts:

[0196] “If the temperature in the factory reaches 35°C, the humidity is 70%, the light level is 5000 lux, and the machine status is ‘overloaded,’ use a generative AI model to analyze the data and suggest appropriate countermeasures.”

[0197] Thus, the present invention provides the data and insights needed to achieve efficient and sustainable management in agricultural and industrial environments.

[0198] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0199] Step 1:

[0200] The device means measures the environmental data in real time. For example, the temperature in the factory is 35°C, humidity is 70%, light level is 5000 lux, and machine status is "overloaded". This is the input data, and the collected data is converted into JSON format. The output of this step is the environmental data in JSON format.

[0201] Step 2:

[0202] The terminal device means receives the data collected from the device means and sends it to the server. Specifically, JSON format data is sent to the server using an HTTP POST request. The input is the JSON data generated in step 1, and the output is a success response to the data transmission to the server.

[0203] Step 3:

[0204] The server receives the JSON data sent from the terminal device means and stores it in a database. Here, a database management system such as SQLite is used to store the data through SQL queries. The input is the received JSON data, and the output is the data stored in the database.

[0205] Step 4:

[0206] The server inputs the stored data into the generative AI system means for analysis. Specifically, a pre-trained generative AI model using Scikit-Learn or TensorFlow analyzes this data. For example, it determines that a temperature of 35°C is too high and generates an insight recommending the activation of a cooling system. The input is environmental data stored in the database, and the output is the insight resulting from the analysis.

[0207] Step 5:

[0208] The server sends the insights generated by the generation AI system means to the terminal via a communication means. Here, too, the analysis results are sent in JSON format using the HTTP protocol. The input is the insights as the analysis results, and the output is the insight data sent to the terminal.

[0209] Step 6:

[0210] The device presents the received insights to the user through a visual display. For example, a dashboard display such as "The temperature is too high. We recommend activating the cooling system" may be displayed on a smartphone or head-mounted display. The input is the insight data sent from the server, and the output is a visual display that the user can check.

[0211] Step 7:

[0212] Based on the visually displayed insights, the user can use management tools to take appropriate measures, for example, to activate the cooling system, which will reduce the temperature in the factory and eliminate the "overload" state of the machines. The input is the visually displayed insight, and the output is the action taken by the user.

[0213] 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.

[0214] This invention relates to a smart agriculture platform that combines generative AI models with digital sensors and a user emotion engine, with the aim of efficiently managing agricultural data and supporting future agricultural practices.

[0215] overview

[0216] The system consists of the following main components:

[0217] Sensor means: Devices for measuring soil condition, moisture content and light level.

[0218] Terminal means: A device for transmitting data collected from the sensor means to the server.

[0219] Server means: A device that receives data sent from the terminal means and stores it in a database.

[0220] Generative AI Means: An artificial intelligence system that analyzes data stored in the Server Means and generates insights for crop management, pest and disease prediction, and optimal use of fertilizer.

[0221] Emotion engine: A device that recognizes the user's emotional state.

[0222] User: The person or organization that operates the system and manages the farm.

[0223] System program and processing explanation

[0224] Data collection and transmission

[0225] The sensor means measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. For example, it measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C.

[0226] The terminal collects the measured data from the sensor means. The collected data is converted into, for example, JSON format and transmitted to the server means. Specifically, the data is transmitted using a data transmission protocol (for example, an HTTP POST request).

[0227] Data storage and analysis

[0228] The server means stores the received data in a database. For example, it inserts the data into the database using an SQL query. The generation AI means analyzes this stored data. The data is input into a pre-trained AI model to generate a prediction result. For example, it may determine that there is a 20% moisture deficiency, that a pH value of 7.2 is appropriate, and that a light intensity of 1,500 lux is appropriate.

[0229] The server generates insights and specific recommended actions based on the analysis results, for example, "Water content is insufficient. Irrigation is recommended."

[0230] Providing insights and responding to users

[0231] The server means transmits the generated analysis results and insights to the terminal means. For example, it transmits a message in JSON format saying, "Water level is insufficient. Irrigation is recommended." The terminal means displays the received analysis results and insights to the user in a visually easy-to-understand format. For example, it displays "Water level is insufficient. Irrigation is recommended" on a dashboard.

[0232] Utilizing the Emotion Engine

[0233] The emotion engine analyzes the user's voice, facial expressions, and body movements to recognize the user's emotional state in real time. For example, if the user has an anxious expression, it will capture that emotional data.

[0234] The server means adjusts insights and recommended actions based on the emotion data obtained from the emotion engine. For example, if the user is in an anxious state, the server means may provide more information to improve the situation or suggest additional customization options.

[0235] Customization Options and Consulting

[0236] The server means proposes suitable customization options based on the data of a particular farm. For example, it may recommend a highly disease-resistant crop variety "X." The user can review the proposed customization options and adjust the settings. The user can select crop variety "X" from the platform's settings screen. Furthermore, the user can subscribe to consulting services as needed to receive additional advice from experts.

[0237] Specific examples

[0238] Example 1: Insufficient soil moisture

[0239] 1. The sensor measures soil moisture at 20%, pH at 7.2, light intensity at 1500 lux, and temperature at 25°C.

[0240] 2. The terminal transmits the measurement data to the server, which stores and analyzes the data.

[0241] 3. The generating AI means generates an insight that "There is insufficient moisture. Irrigation is recommended.", and the server means transmits this to the terminal means.

[0242] 4. The terminal means displays the analysis results on a dashboard for the user to confirm.

[0243] 5. The emotion engine recognizes the user's anxious facial expression, and the server means provides detailed guidance and additional information.

[0244] 6. The user turns on the irrigation system to replenish the water.

[0245] Example 2: Proposing customization options

[0246] 1. The server means suggests a highly disease-resistant crop variety "X" based on data from a particular farm.

[0247] 2. The terminal means displays the proposed content to the user, who then confirms it.

[0248] 3. The emotion engine recognizes the user's interesting facial expressions and the server means provides further detailed information.

[0249] 4. The user selects crop variety "X" from the platform settings screen.

[0250] In this way, the system not only supports efficient and sustainable agricultural management, but also provides advanced support that takes into account the user's emotional state.

[0251] The processing flow will be explained below.

[0252] Specific processing steps of the program

[0253] Step 1: Data collection

[0254] 1. The sensor means measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time.

[0255] Example: Measure soil moisture content at 20%, pH value at 7.2, light intensity at 1500 lux, and temperature at 25°C.

[0256] 2. The terminal collects the measured data from the sensor means.

[0257] Convert the data into a format (e.g. JSON).

[0258] Example: { "soil_moisture": 20, "pH": 7.2, "light": 1500, "temperature": 25}

[0259] 3. The device sends the collected data to the server.

[0260] Send data using a data transmission protocol (e.g., HTTP POST request).

[0261] Example: POST / data { "soil_moisture": 20, "pH": 7.2, "light": 1500, "temperature": 25}

[0262] Step 2: Save data

[0263] 1. The server stores the received data in a database.

[0264] For example, inserting data into a database using an SQL query.

[0265] Example: INSERT INTO soil_data (moisture, pH, light, temperature) VALUES (20, 7.2, 1500, 25)

[0266] Step 3: Data analysis

[0267] 1. A generative AI model on the server analyzes the stored data.

[0268] For example, data is input into a pre-trained AI model to generate prediction results.

[0269] The generated AI model determines that "20% moisture content is insufficient," "pH value of 7.2 is neutral and appropriate," and "light intensity of 1500 lux is appropriate."

[0270] 2. The server generates insights and recommended actions based on the analysis results.

[0271] For example, generating an insight such as "Water level is insufficient. Irrigation recommended."

[0272] Step 4: Providing insights

[0273] 1. The server sends the generated analysis results and insights to the device.

[0274] Example: Send a message to the device in JSON format saying "Water level is low. Irrigation recommended."

[0275] 2. The device displays the received analysis results and insights to the user in a visually easy-to-understand format.

[0276] Example: Displaying "Insufficient moisture. Irrigation recommended" on the dashboard.

[0277] Step 5: Real-time adjustments

[0278] 1. The user looks at the device screen to see the analysis results and insights.

[0279] Example: Scroll through your dashboard to see if you're dehydrated.

[0280] 2. Users adjust how they manage their farms as needed.

[0281] For example: Turn on the irrigation system to replenish water.

[0282] Step 6: Leverage your emotional engine

[0283] 1. The emotion engine recognizes the user's emotional state from their voice, facial expressions, and body movements.

[0284] Example: If the user has an anxious expression, obtain the emotion data.

[0285] 2. The server adjusts insights and recommended actions based on the emotion data obtained from the emotion engine.

[0286] Example: If the user is in an anxious state, generate an action such as "provide more information to remedy the situation" or "suggest additional customization options."

[0287] 3. The server sends tailored insights and recommended actions to the device.

[0288] Example: Send the message "We will introduce detailed irrigation methods" in JSON format to the terminal.

[0289] 4. The device displays tailored insights and recommended actions to the user in a visually understandable format.

[0290] Example: Display "Detailed irrigation methods" on the dashboard.

[0291] Step 7: Customization options and consultation

[0292] 1. The server suggests suitable customization options based on the data for the specific farm.

[0293] Example: Propose a highly disease-resistant crop variety "X."

[0294] 2. The device displays the suggestions to the user.

[0295] Example: Displaying "We recommend the highly disease-resistant crop variety 'X'" on the dashboard.

[0296] 3. The user reviews the suggested customization options and adjusts the settings.

[0297] Example: Select crop variety "X" from the platform settings screen.

[0298] 4. The user applies for consulting services as needed.

[0299] For example: Consult an expert online for additional advice.

[0300] Through the above processing steps, the system supports efficient agricultural management and sustainable production, and also provides advanced support that takes into account the user's emotional state.

[0301] Example 2

[0302] 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."

[0303] Modern agriculture requires real-time monitoring of soil and environmental conditions, and prompt and appropriate farm management based on that monitoring. However, while conventional systems can collect and analyze sensor data, they lack the ability to provide specific action instructions based on the analysis results or support that takes into account the user's emotional state. Therefore, a system is needed that allows users to manage farms efficiently and sustainably without feeling any emotional burden.

[0304] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0305] In this invention, the server includes an emotion engine that recognizes the emotional state of the user, means for adjusting insights and recommended actions based on data obtained from the emotion engine, and means for transmitting the insights generated by the generating AI means to a terminal, thereby providing farm management advice optimized according to the user's emotional state, enabling the user to manage the farm efficiently and with reduced emotional burden.

[0306] The "sensor means" is a device for measuring soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time.

[0307] The "terminal means" is a device for transmitting data collected from the sensor means to the server means.

[0308] The "server means" is a device that receives data sent from the terminal means, stores the data in a database, and analyzes the data.

[0309] The "generative AI means" is an artificial intelligence system for analyzing data stored in the server means and generating insights regarding crop management, pest and disease prediction, and optimal use of fertilizer.

[0310] An "emotion engine" is a device that analyzes the user's voice, facial expressions, and body movements to recognize the user's emotional state in real time.

[0311] "Insights" are specific pieces of advice or recommended actions created by generative AI means to help users manage their farms.

[0312] "User" means an individual or organization that operates the system and manages the farm.

[0313] "Customization options" are options that suggest crop varieties, management methods, and other options that are best suited to a particular farm.

[0314] This invention relates to a smart agriculture platform that combines generative AI models with digital sensors and a user emotion engine, with the aim of efficiently managing agricultural data and supporting future agricultural practices.

[0315] overview

[0316] The system consists of the following main components:

[0317] Sensor means: A device for measuring soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. Examples of sensor means include soil moisture sensors, pH sensors, light sensors, and temperature sensors.

[0318] Terminal means: A device that transmits data collected from the sensor means to the server. Specifically, a gateway, smartphone, tablet, etc. are used.

[0319] Server means: A device that receives data sent from the terminal means and stores it in a database. For example, a cloud server or an on-premise server is used.

[0320] Generative AI means: An artificial intelligence system that analyzes data stored in the server means and generates insights on crop management, pest and disease prediction, and optimal fertilizer use. Deep learning models and machine learning algorithms are used as generative AI models.

[0321] Emotion engine: A device that analyzes and recognizes the user's emotional state (voice, facial expressions, and body movements) in real time. Specifically, a voice recognition system and a face recognition system are used.

[0322] User: An individual or organization that operates the system and manages the farm.

[0323] Data collection and transmission

[0324] The sensor means measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. For example, it measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C. The terminal means converts the collected data into JSON format and sends it to the server means using an HTTP POST request.

[0325] Data storage and analysis

[0326] The server means stores the received data in a database. For example, it inserts data into the database using an SQL query. The generative AI means performs analysis based on this stored data. The data is input into a pre-trained AI model to generate a prediction result. For example, it may determine that there is a 20% moisture deficiency, that a pH value of 7.2 is appropriate, and that a light intensity of 1,500 lux is appropriate. The server means generates insights and specific recommended actions based on the analysis results. For example, it generates an insight such as, "There is a moisture deficiency. Irrigation is recommended."

[0327] Providing insights and responding to users

[0328] The server means transmits the generated analysis results and insights to the terminal means. For example, it transmits a message in JSON format saying, "Water level is insufficient. Irrigation is recommended." The terminal means displays the received analysis results and insights to the user in a visually easy-to-understand format. For example, it displays "Water level is insufficient. Irrigation is recommended" on a dashboard.

[0329] Utilizing the Emotion Engine

[0330] The emotion engine analyzes the user's voice, facial expressions, and body movements to recognize the user's emotional state in real time. For example, if the user has an anxious expression, the emotion data is acquired. The server means adjusts insights and recommended actions based on the emotion data acquired from the emotion engine. For example, if the user is in an anxious state, the server means performs actions such as "providing detailed information to improve the situation" or "suggesting additional customization options."

[0331] Customization Options and Consulting

[0332] The server means proposes suitable customization options based on the data of a particular farm. For example, it may recommend a highly disease-resistant crop variety "X." The user can review the proposed customization options and adjust the settings. The user can select crop variety "X" from the platform's settings screen. Furthermore, the user can subscribe to consulting services as needed to receive additional advice from experts.

[0333] Specific examples

[0334] Example 1: Insufficient soil moisture

[0335] The sensor means measures the soil moisture content to be 20%, the pH value to be 7.2, the light intensity to be 1500 lux, and the temperature to be 25°C. The terminal means transmits the measurement data to the server, which stores and analyzes the data. The generation AI means generates an insight that "The amount of water is insufficient. Irrigation is recommended," which the server means transmits to the terminal means. The terminal means displays the analysis results on a dashboard for the user to confirm. The emotion engine recognizes the user's anxious expression, and the server means provides detailed guidance and additional information. The user turns on the irrigation system to replenish the water.

[0336] Example 2: Proposing customization options

[0337] The server means proposes a highly disease-resistant crop variety "X" based on data from a specific farm. The terminal means displays the proposal to the user, who confirms it. The emotion engine recognizes an interesting expression from the user, and the server means provides further detailed information. The user selects crop variety "X" from the platform's setting screen.

[0338] Prompt Sentence Examples

[0339] Examples of prompts include:

[0340] "Analyze current soil moisture, pH, light, and temperature data and recommend appropriate actions."

[0341] "If the user appears anxious, please suggest what additional information or support you can provide."

[0342] "Suggest the best crop varieties for your particular farm environment."

[0343] In this way, the system not only supports efficient and sustainable agricultural management, but also provides advanced support that takes into account the user's emotional state.

[0344] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0345] Step 1: Sensor measures data

[0346] The sensor means measures environmental data such as soil moisture content, pH value, light intensity, and temperature in real time. For example, at 9:00 AM, the sensor means measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C. These data are transmitted from the sensor means to the terminal means. The sensor means receives soil and environmental measurements as inputs and generates a set of sensor data as outputs.

[0347] Step 2: Device collects and transmits data

[0348] The terminal collects data received from the sensor means and converts it into JSON format. Specifically, it generates a JSON object with data such as soil moisture content, pH value, light intensity, and temperature as keys and values. The terminal sends this JSON data to the server means using an HTTP POST request. It receives sensor data as input, generates JSON format data as output, and sends it to the server.

[0349] Step 3: The server saves the data

[0350] The server receives the JSON data sent from the terminal means and stores it in the database using an SQL query. For example, it inserts it into the database using the SQL query "INSERT INTO sensor_data (timestamp, moisture, pH, lux, temperature) VALUES ('2023-10-01 09:00:00', 20, 7.2, 1500, 25);". It takes JSON data as input and generates stored data as output.

[0351] Step 4: The generative AI model analyzes the data

[0352] The server inputs the stored data into the generative AI model for analysis. The AI ​​model generates insights such as a 20% lack of moisture, an appropriate pH value of 7.2, and an appropriate light intensity of 1,500 lux. This allows insights such as "Irrigation is recommended" to be obtained as analysis results. It receives stored sensor data as input and generates analysis results as output.

[0353] Step 5: The server generates insights and sends them to the device

[0354] The server generates a specific insight message based on the analysis results obtained from the generative AI model. For example, it creates a message saying, "Water level is insufficient. Irrigation is recommended." This insight message is then converted back to JSON format and sent to the terminal. It receives the analysis results as input, generates an insight message as output, and sends it to the terminal.

[0355] Step 6: The device displays the insight to the user

[0356] The terminal analyzes the insight message received from the server and displays it to the user in a visually easy-to-understand format. Specifically, it displays a warning message on the dashboard saying, "Water level is insufficient. Irrigation is recommended." It receives the insight message as input and displays it to the user as output.

[0357] Step 7: The emotion engine recognizes the user's emotion

[0358] The emotion engine analyzes the user's voice, facial expressions, and body movements to recognize the user's emotional state in real time. For example, if the user has an anxious expression, it acquires that information and sends it to the server. It receives the user's voice and facial expression data as input and generates emotion data as output.

[0359] Step 8: The server adjusts the insights based on the sentiment data

[0360] The server adjusts insights and recommended actions based on the emotional data obtained from the emotion engine. If the user is anxious, it generates a message that provides specific operating procedures or additional support information. For example, it provides a detailed guide such as, "Here are the specific operating procedures for the irrigation system." It receives emotional data as input and generates adjusted recommended messages as output.

[0361] Step 9: User performs action

[0362] The user takes specific actions based on the insights and recommended actions displayed on the device, such as turning on the irrigation system to replenish soil moisture. The system takes insight messages from the device as input and actual farm management actions as output.

[0363] (Application example 2)

[0364] 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."

[0365] Factories need to realize efficient environmental monitoring and machine management, and respond quickly to unexpected machine breakdowns and environmental changes. Conventional systems collect data using sensors, but it is difficult to support real-time emotion recognition and detailed analysis of environmental conditions. They also lack the ability to effectively predict maintenance needs and propose appropriate management methods. Therefore, it is necessary to provide a system that can constantly monitor the environmental conditions in factories and provide optimal machine management and maintenance predictions while taking into account the user's emotional state.

[0366] The specific processing by the specific 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 measuring soil condition, moisture content, and light intensity; terminal means for collecting data from the sensor means and transmitting it to the server; server means for receiving data transmitted from the terminal means and storing it in a database; generation AI means for analyzing the data stored in the server means and generating insights regarding crop management, pest prediction, and optimal fertilizer use; means for transmitting the insights generated by the generation AI means to the terminal; means for displaying the insights to the user from the terminal; means for the user to adjust the farm management method based on the insights; emotion engine means for recognizing the user's emotional state and adjusting the insights and recommended actions generated by the server means; and monitoring means for constantly monitoring the environmental conditions (temperature, humidity, light, vibration) within the factory and performing appropriate machine management and maintenance prediction. This enables real-time environmental monitoring, appropriate machine management, and adjustments that take user emotions into consideration.

[0367] - "Soil conditions" refers to the physical and chemical conditions that affect crop growth, such as soil moisture, temperature, pH, and nutrient content.

[0368] "Moisture content" refers to the amount of water present in the soil or environment, and indicates the amount of water required for crop growth.

[0369] "Light intensity" refers to the amount of light that hits a certain area per unit of time, and is generally measured in lux.

[0370] "Sensor means" refers to a device for measuring soil moisture, pH value, temperature, light intensity, etc.

[0371] The "terminal means" is a device for collecting data from the sensor means and transmitting the data to the server.

[0372] The "server means" is a device that receives data sent from the terminal means, stores it in a database, and analyzes it.

[0373] A "generative AI means" is an artificial intelligence system that analyzes data stored in the server means and generates insights and predictions.

[0374] The "emotion engine means" is a device that analyzes the user's voice, facial expressions, body movements, etc., and recognizes the user's emotional state in real time.

[0375] A "monitoring means" is a device that constantly monitors the environmental conditions within a factory (temperature, humidity, light, vibration, etc.) and performs appropriate machine management and maintenance predictions.

[0376] "Insights" refers to specific indications and recommendations based on the results of data analysis that indicate future situations and necessary countermeasures.

[0377] "Customization options" refers to suggestions for achieving more optimal management and operation by adjusting options and settings to suit specific conditions.

[0378] "Platform" refers to the entire system realized by the mutual cooperation of multiple elements such as sensor means, terminal means, server means, and generating AI means.

[0379] This invention is a system for advanced environmental monitoring and machine management in factories. This system is composed of multiple elements such as sensor means, terminal means, server means, generation AI means, emotion engine means, and monitoring means.

[0380] 1. Sensor data collection

[0381] Sensor means are devices that measure environmental data such as temperature, humidity, light intensity, and vibration in real time. For example, a temperature sensor measures the temperature at each operating point in a factory, a humidity sensor measures humidity, a light sensor detects illuminance, and a vibration sensor monitors the vibration state of machines.

[0382] 2. Data transmission

[0383] The terminal means is a device that transmits data collected from the sensor means to the server means. The data is converted to JSON format and transmitted using an HTTP POST request. For example, the data transmitted may be temperature 25.5 degrees, humidity 55%, light intensity 1200 lux, and vibration 0.3 m / s².

[0384] 3. Data storage and analysis

[0385] The server means receives the data sent from the terminal means and stores it in a database. The generation AI means analyzes the stored data and generates insights. Here, an AI model is used to detect abnormalities in temperature or vibration and consider countermeasures. For example, it generates an insight such as "The temperature is too high, so the cooling system needs to be activated."

[0386] 4. Providing insights and notifying users

[0387] The server means transmits the generated insight to the terminal means, which visually presents the insight to the user, for example, by displaying "Temperature is too high. Please turn on the cooling system" on a dashboard.

[0388] 5. Leveraging Emotional Engines

[0389] The emotion engine means recognizes the emotional state of the user by detecting the user's voice, facial expression, and body movements. For example, if the user has an anxious expression, the server means acquires that data. The server means adjusts the insight based on the emotional data acquired from the emotion engine means. For example, it is possible to respond by saying, "Since the user has an anxious expression, we will provide detailed guidance."

[0390] 6. Environmental monitoring and machinery management

[0391] The monitoring tool is a device that constantly monitors the environmental conditions in the factory and performs appropriate machine management and maintenance predictions. For example, if an abnormal rise in temperature is detected, it will immediately issue a warning and recommend activation of the cooling system.

[0392] Specific examples

[0393] For example, if a temperature sensor in a factory detects that the temperature has reached 30 degrees, the data is sent to the server via the terminal means. Analysis is performed on the server side and activation of the cooling system is recommended. The generated insight is sent to the terminal and displayed on the user's dashboard as "The temperature has reached 30 degrees. Please turn on the cooling system." Also, if the emotion engine detects that the user has an anxious expression, detailed guidance is provided.

[0394] Prompt Sentence Examples

[0395] An example of a prompt used in this system is, "The current factory environment data is temperature 30°C, humidity 60%, light intensity 1000 lux, and vibration 0.4 m / s². Please perform optimal machine management and maintenance prediction based on this." By analyzing this prompt, the AI ​​can generate specific insights and actions.

[0396] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0397] Step 1:

[0398] The sensor means measures environmental data (temperature, humidity, light intensity, vibration) in the factory in real time. For example, the temperature sensor measures the temperature as 25.5 degrees, the humidity sensor measures the humidity as 55%, the light sensor measures the light intensity as 1200 lux, and the vibration sensor measures the vibration as 0.3 m / s². These data are collected and sent to the terminal means as input.

[0399] Step 2:

[0400] The terminal means converts the data collected from the sensor means into JSON format and sends it to the server. For example, the JSON format data includes { "temperature": 25.5, "humidity": 55, "light": 1200, "vibration": 0.3}. This data is sent using an HTTP POST request. The data converted into JSON format is input to the server.

[0401] Step 3:

[0402] The server means receives the data sent from the terminal means and stores it in the database. The data is inserted into the database using an SQL query. For example, the query INSERT INTO environmental_data (temperature, humidity, light, vibration) VALUES (25.5, 55, 1200, 0.3) is executed. The received data is stored as output in the database.

[0403] Step 4:

[0404] The server means analyzes the stored data using the generating AI means and generates insights. For example, a temperature of 25.5 degrees is judged to be normal, while a vibration of 0.3 m / s² is judged to be abnormal. This analysis result is the result of data calculation by the generating AI and is output as insights.

[0405] Step 5:

[0406] The server means transmits the generated insight to the terminal means. The generated insight, for example, "Vibration is high. Machine maintenance is required," is transmitted in JSON format. The transmitted insight is sent as input to the terminal.

[0407] Step 6:

[0408] The terminal means visually presents the received insight to the user, for example by displaying a message on the dashboard saying "Vibration is high. Machine maintenance required." The user is provided with visual information and maintenance instructions as output.

[0409] Step 7:

[0410] The emotion engine means detects the user's voice, facial expression, and body movement to recognize the user's emotional state. For example, if the user has an anxious expression, that data is acquired from the emotion engine means. The acquired emotion data is input to the server.

[0411] Step 8:

[0412] The server means adjusts the insight based on the emotion data acquired from the emotion engine means. For example, an action such as "provide detailed guidance" is generated based on anxious facial expression data. Based on the emotion data, the adjusted insight or additional information is generated as an output.

[0413] Step 9:

[0414] Monitoring means constantly monitor the environmental conditions within the factory and perform appropriate machine management and maintenance predictions. If an abnormality is detected, an alert is issued immediately and necessary measures are recommended. For example, a warning such as "The temperature is rising sharply. Please turn on the cooling system" is output.

[0415] Example prompt sentence:

[0416] "The current factory environment data is: temperature 30°C, humidity 60%, light intensity 1000 lux, vibration 0.4 m / s². Please use this information to perform optimal machine management and maintenance predictions."

[0417] 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.

[0418] 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.

[0419] 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.

[0420] [Second embodiment]

[0421] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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."

[0433] This invention relates to a smart agriculture platform that combines generative AI models and digital sensors to efficiently manage agricultural data and support future agricultural practices.

[0434] overview

[0435] The system consists of the following main components:

[0436] Sensor means: Devices for measuring soil condition, moisture content and light level.

[0437] Terminal means: A device for transmitting data collected from the sensor means to the server.

[0438] Server means: A device that receives data sent from the terminal means and stores it in a database.

[0439] Generative AI Means: An artificial intelligence system that analyzes data stored in the Server Means and generates insights for crop management, pest and disease prediction, and optimal use of fertilizer.

[0440] User: The person or organization that operates the system and manages the farm.

[0441] System program and processing explanation

[0442] Data collection

[0443] The sensor means measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. For example, it measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C.

[0444] Next, the terminal means collects the data. The collected data is converted into, for example, a JSON format and transmitted to the server means. Specifically, the data is transmitted using a data transmission protocol (for example, an HTTP POST request).

[0445] Data analysis

[0446] The server means stores the received data in a database. For example, it inserts the data into the database using an SQL query. The generation AI means analyzes this stored data. The data is input into a pre-trained AI model to generate a prediction result. For example, it may determine that there is a 20% moisture deficiency, that a pH value of 7.2 is appropriate, and that a light intensity of 1,500 lux is appropriate.

[0447] The server generates insights and specific recommended actions based on the analysis results, for example, "Water content is insufficient. Irrigation is recommended."

[0448] Providing insights

[0449] The server means transmits the generated analysis results and insights to the terminal means. For example, it transmits a message in JSON format saying, "Water level is insufficient. Irrigation is recommended." The terminal means displays the received analysis results and insights to the user in a visually easy-to-understand format. For example, it displays "Water level is insufficient. Irrigation is recommended" on a dashboard.

[0450] Real-time adjustments

[0451] Users can view analytics and insights on their device screens, scroll through dashboards to identify water deficiencies, and then adjust farm management as needed, such as turning on irrigation systems to replenish water.

[0452] Customization Options and Consulting

[0453] The server means proposes suitable customization options based on the data of a particular farm. For example, it may recommend a highly disease-resistant crop variety "X." The user can review the proposed customization options and adjust the settings. The user can select crop variety "X" from the platform's settings screen. Furthermore, the user can subscribe to consulting services as needed to receive additional advice from experts.

[0454] In this way, the smart agriculture platform of the present invention provides farmers with the data and insights they need to achieve more efficient and sustainable agricultural management.

[0455] The processing flow will be explained below.

[0456] Specific processing steps of the program

[0457] Step 1: Data collection

[0458] 1. The sensor means measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time.

[0459] Example: Measure soil moisture content at 20%, pH value at 7.2, light intensity at 1500 lux, and temperature at 25°C.

[0460] 2. The terminal collects the measured data from the sensor means.

[0461] Convert the data into a format (e.g. JSON).

[0462] Example: { "soil_moisture": 20, "pH": 7.2, "light": 1500, "temperature": 25}

[0463] 3. The device sends the collected data to the server.

[0464] Send data using a data transmission protocol (e.g., HTTP POST request).

[0465] Example: POST / data { "soil_moisture": 20, "pH": 7.2, "light": 1500, "temperature": 25}

[0466] Step 2: Save data

[0467] 1. The server stores the received data in a database.

[0468] Example: Inserting data into a database using an SQL query.

[0469] Example: INSERT INTO soil_data (moisture, pH, light, temperature) VALUES (20, 7.2, 1500, 25)

[0470] Step 3: Data analysis

[0471] 1. A generative AI model on the server analyzes the stored data.

[0472] Example: Inputting data into a pre-trained AI model to generate predictions.

[0473] The model determines that "20% moisture content is insufficient," "a pH value of 7.2 is neutral and appropriate," and "a light intensity of 1500 lux is appropriate."

[0474] 2. The server generates insights and recommended actions based on the analysis results.

[0475] For example: Generate the insight "Water level is insufficient. Irrigation recommended."

[0476] Step 4: Providing insights

[0477] 1. The server sends the generated analysis results and insights to the device.

[0478] Example: Send a message to the device in JSON format saying "Water level is low. Irrigation recommended."

[0479] 2. The device displays the received analysis results and insights to the user in a visually easy-to-understand format.

[0480] Example: Displaying "Insufficient moisture. Irrigation recommended" on the dashboard.

[0481] Step 5: Real-time adjustments

[0482] 1. The user looks at the device screen to see the analysis results and insights.

[0483] Example: Scroll through your dashboard to see if you're dehydrated.

[0484] 2. Users adjust how they manage their farms as needed.

[0485] For example: Turn on the irrigation system to replenish water.

[0486] Step 6: Customization options and consultation

[0487] 1. The server suggests suitable customization options based on the data for the specific farm.

[0488] Example: Propose a highly disease-resistant crop variety "X."

[0489] 2. The user reviews the suggested customization options and adjusts the settings.

[0490] Example: Select crop variety "X" from the platform settings screen.

[0491] 3. The user applies for consulting services as needed.

[0492] For example: Consult an expert online for additional advice.

[0493] In this way, the system supports efficient and sustainable agricultural management.

[0494] Example 1

[0495] 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."

[0496] Conventional agricultural management systems face challenges in efficiently collecting data from the entire farm and analyzing it in real time, making it difficult to perform appropriate crop management, predict pests and diseases, and optimize fertilizer use. They also face the problem of being unable to propose customized options suited to specific farms and adjust management methods accordingly.

[0497] 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.

[0498] In this invention, the server includes a sensor device that measures soil condition, moisture content, and light intensity; a terminal device that collects data from the sensor device and transmits it to the server; a server device that receives the data transmitted from the terminal device and stores it in a database; a generating AI device that analyzes the data stored in the server device and generates insights regarding crop management, pest and disease prediction, and optimal fertilizer use; a device that transmits the insights generated by the generating AI device to the terminal device; a device that displays the insights to a user from the terminal device; and a device that allows the user to adjust farm management methods based on the insights. This enables real-time data collection and analysis, appropriate crop management, pest and disease prediction, and optimal fertilizer use. It also enables the system to quickly and effectively propose customized options suitable for specific farms and adjust management methods based on those options.

[0499] A "sensor device" is a device for measuring soil conditions, moisture content, light intensity, and the like.

[0500] A "terminal device" is a device for transmitting data collected from a sensor device to a server.

[0501] A "server device" is a device that receives data sent from a terminal device and stores it in a database.

[0502] The "generative AI device" is an artificial intelligence system that analyzes data stored on a server device and generates insights into crop management, pest and disease prediction, and optimal fertilizer use.

[0503] A "display device" is a device for visually displaying insights from a terminal device to a user.

[0504] A "user" is an individual or organization that adjusts how they manage their farm based on the insights generated.

[0505] "Customization options" are options that suggest adjustments and settings that are suitable for a particular farm.

[0506] "Real-time adjustment" is the process by which users instantly change how they manage their farm based on the insights generated.

[0507] System Overview

[0508] The present invention relates to a smart agriculture platform that combines generative AI models and digital sensors to efficiently manage agricultural data and support future agricultural practices.

[0509] The system consists of the following main components:

[0510] Sensor devices: devices for measuring soil condition, moisture content and light intensity.

[0511] Terminal device: A device for transmitting data collected from a sensor device to a server.

[0512] Server device: A device that receives data sent from a terminal device and stores it in a database.

[0513] Generative AI device: An artificial intelligence system that analyzes data stored on a server device and generates insights for crop management, pest and disease prediction, and optimal fertilizer use.

[0514] Display device: A device for transmitting the insights generated by the generative AI device to a terminal device and visually displaying them to the user.

[0515] User: The person or organization that operates the system and manages the farm.

[0516] Examples of data collection

[0517] The sensor device measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. For example, it measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C. Next, the terminal device collects this data. The collected data is converted into, for example, JSON format and sent to the server device. Specifically, the data is sent using a data transmission protocol (for example, an HTTP POST request).

[0518] Specific examples of data analysis

[0519] The server device stores the received data in a database. For example, it inserts data into the database using an SQL query. The generative AI device performs analysis based on this stored data. The data is input into a pre-trained AI model to generate a prediction result. For example, it may determine that there is a 20% moisture deficiency, that a pH value of 7.2 is appropriate, and that a light intensity of 1,500 lux is appropriate. The server device generates insights and specific recommended actions based on the analysis results. For example, it may generate an insight such as, "There is a moisture deficiency. Irrigation is recommended."

[0520] Providing insights and real-time adjustments

[0521] The server device sends the generated analysis results and insights to the terminal device. For example, it sends a message in JSON format saying, "Water level is insufficient. Irrigation recommended." The terminal device displays the received analysis results and insights to the user in a visually easy-to-understand format. For example, it displays "Water level is insufficient. Irrigation recommended" on a dashboard.

[0522] Users can view analytics and insights on their device screens, scroll through dashboards to identify water deficiencies, and then adjust farm management as needed, such as switching on irrigation systems to replenish water.

[0523] Customization options and consulting

[0524] The server device proposes suitable customization options based on the data of a specific farm. For example, it may recommend a highly disease-resistant crop variety "X." The user can review the proposed customization options and adjust the settings. They can select crop variety "X" from the platform's settings screen. Furthermore, users can request consulting services to receive additional advice from experts as needed.

[0525] Prompt Sentence Examples

[0526] For example, if the following data is measured by a sensor device on a farm:

[0527] Land Data:

[0528] Soil moisture content: 15%

[0529] pH value: 6.8

[0530] Light output: 1300 lux

[0531] Temperature: 22℃

[0532] Based on this data, we input the following prompt to the generative AI model:

[0533] Based on this data, what are your recommended actions for crop management?

[0534] In this way, the smart agriculture platform of the present invention provides farmers with the data and insights they need to achieve more efficient and sustainable agricultural management.

[0535] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0536] Step 1:

[0537] The sensor device measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. Specifically, it measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C. This is used as input data.

[0538] Step 2:

[0539] The terminal device collects measurement data from the sensor device. The collected data is converted into JSON format and sent to the server. Specifically, the JSON format data is sent to the server using a data transmission protocol (for example, an HTTP POST request). This allows the server to receive the data collected from the sensor.

[0540] Step 3:

[0541] The server stores the received data in a database. Specifically, it inserts data into the database using an SQL query. It uses the received JSON data as input and obtains the state stored in the database as output.

[0542] Step 4:

[0543] The generative AI device analyzes the data stored in the database. The data is input into a pre-trained AI model to generate a predicted result. Specifically, it determines that there is a 20% moisture deficiency, that a pH value of 7.2 is appropriate, and that a light intensity of 1500 lux is appropriate. The data in the database is used as input data, and a predicted result is obtained as output.

[0544] Step 5:

[0545] The server generates insights based on the analysis results. Specifically, it generates a message based on the analysis results saying, "Water level is insufficient. Irrigation is recommended." It uses the prediction results as input data and obtains the generated insights as output.

[0546] Step 6:

[0547] The server sends the generated insight to the terminal device. Specifically, it sends a message in JSON format saying, "Water level is insufficient. Irrigation is recommended." The terminal device then receives the insight.

[0548] Step 7:

[0549] The terminal device displays the received insight to the user. Specifically, it displays "Water level is low. Irrigation recommended" on the dashboard. This allows the user to visually confirm the insight.

[0550] Step 8:

[0551] The user looks at the screen of their device to see the analysis results and insights. Specifically, they scroll through the dashboard to identify water deficiencies. If necessary, they adjust their farm management, for example, by switching on the irrigation system to replenish water. This ensures that the farm is properly managed.

[0552] Step 9:

[0553] The server then proposes appropriate customization options based on the data of a specific farm. Specifically, it generates an option such as "We suggest a highly disease-resistant crop variety 'X'." This allows the user to have useful choices.

[0554] Step 10:

[0555] The user reviews and applies the proposed customization options, specifically by selecting crop variety "X" in the platform's settings screen, thereby optimizing farm management.

[0556] (Application example 1)

[0557] 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."

[0558] Traditional agricultural management systems often lack the ability to collect, analyze, and provide insights in real time. This can make it difficult for farmers to respond quickly and appropriately, potentially resulting in reduced production efficiency and quality. Similar issues arise in factory environments, where effective management of temperature, humidity, light levels, and machine status data is required.

[0559] 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.

[0560] In this invention, the server comprises device means for measuring soil conditions, moisture content, and light intensity, terminal device means for collecting data from the device means and transmitting it to the server, storage means for receiving data transmitted from the terminal device means and storing it in a database, generation AI system means for analyzing the data stored in the storage means and generating insights regarding crop management, pest prediction, and optimal use of fertilizer, communication means for transmitting the insights generated by the generation AI system means to a terminal, visual display means for displaying the insights from the terminal to a user, management means for allowing the user to adjust the farm management method based on the insights, and temperature, humidity, and other factors of the factory environment. The system includes a device means for measuring temperature and light levels and collecting machine status data, a terminal device means for collecting data from the device means and transmitting it to a server, a storage device means for receiving the data transmitted from the terminal device means and storing it in a database, a generative AI system means for analyzing the data stored in the storage device means and generating insights for optimizing the production process, activating a cooling system, and reducing machine load, a communication means for transmitting the insights generated by the generative AI system means to a terminal, a visual display means for displaying the insights to a worker from the terminal, and a management means for the worker to adjust the production process based on the insights. This enables real-time data collection, analysis, and provision of insights in agricultural and factory environments, enabling prompt and appropriate responses.

[0561] "Soil condition" refers to the physical, chemical, and biological characteristics of the surface and subsurface of agricultural land.

[0562] "Moisture content" refers to the percentage of water contained in a particular area or substance.

[0563] "Light intensity" refers to the intensity or amount of light measured at a particular location.

[0564] "Device means" refers to the measuring instruments or equipment used to collect specific data.

[0565] "Terminal device means" refers to a communication device for collecting data and transmitting it to other devices or servers.

[0566] "Storage means" refers to a database or storage device for storing collected data.

[0567] "Generative AI system means" refers to a system that uses a pre-trained artificial intelligence model to analyze data and generate insights and predictions.

[0568] "Communications means" refers to technologies and devices for sending and receiving data and information.

[0569] "Visual display means" refers to devices or techniques for visually displaying data or insights to a user.

[0570] "Control measures" refers to methods and means for adjusting and controlling the way farms or factories are run in real time based on said insights.

[0571] "Machine status" refers to data indicating the state of a particular machine, such as whether it is running, stopped, or overloaded.

[0572] A "production process" refers to the series of operations or steps taken to produce a particular product or service.

[0573] "Cooling system" means a system or device for reducing the temperature of an environment or machine.

[0574] "Machine load" refers to the amount of work or workload being processed by a particular machine.

[0575] The embodiment of the present invention will be specifically described as follows: The system of the present invention is mainly composed of a device means, a terminal device means, a storage means, a generation AI system means, a communication means, a visual display means, and a management means.

[0576] System configuration and operation

[0577] Device Means

[0578] The device means is for measuring environmental data of a farm or factory in real time, for example, in the case of a farm it may include sensors measuring soil condition, moisture content, and light level, and in the case of a factory it may include sensors measuring temperature, humidity, light level, and machine status.

[0579] terminal device means

[0580] The end device is responsible for transmitting the data collected from the sensors to the server. This can be a communication device such as a smartphone or a head-mounted display. The data is converted to JSON format, for example, and sent to the server using an HTTP POST request.

[0581] storage means

[0582] The server receives the data sent from the terminal device and stores it in a database, using a database management system such as SQLite, where data is stored through SQL queries.

[0583] Generative AI system means

[0584] The data stored on the server is analyzed by pre-trained generative AI models, built using frameworks such as Scikit-Learn and TensorFlow, which generate insights for crop management, pest and disease prediction, optimal fertilizer use, optimizing production processes, activating cooling systems, and reducing machine loads.

[0585] communication means

[0586] The insights generated by the generative AI system are sent to the terminal via a communication means, again using the HTTP protocol, and the insight data is typically sent in JSON format.

[0587] Visual display means

[0588] The device is responsible for visually displaying the received insights, allowing users to review them through interfaces such as dashboards and graphs.

[0589] management measures

[0590] Based on the displayed insights, users can adjust management measures, such as activating irrigation systems or adjusting machine loads. For example, if temperatures in a factory become too high, cooling systems can be activated immediately. Production processes can also be optimized and machine loads adjusted as needed.

[0591] Adding specific examples

[0592] For example, if the temperature in a factory reaches 35°C, the sensor means measures this and the terminal device means transmits the data to the server. The server stores the data in a database, and the generating AI system means analyzes that the temperature is too high and generates an insight recommending the activation of a cooling system. This insight is transmitted to the terminal via the communication means, and an operator can view it via a head-mounted display.

[0593] Examples of prompts:

[0594] “If the temperature in the factory reaches 35°C, the humidity is 70%, the light level is 5000 lux, and the machine status is ‘overloaded,’ use a generative AI model to analyze the data and suggest appropriate countermeasures.”

[0595] Thus, the present invention provides the data and insights needed to achieve efficient and sustainable management in agricultural and industrial environments.

[0596] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0597] Step 1:

[0598] The device means measures the environmental data in real time. For example, the temperature in the factory is 35°C, humidity is 70%, light level is 5000 lux, and machine status is "overloaded". This is the input data, and the collected data is converted into JSON format. The output of this step is the environmental data in JSON format.

[0599] Step 2:

[0600] The terminal device means receives the data collected from the device means and sends it to the server. Specifically, JSON format data is sent to the server using an HTTP POST request. The input is the JSON data generated in step 1, and the output is a success response to the data transmission to the server.

[0601] Step 3:

[0602] The server receives the JSON data sent from the terminal device means and stores it in a database. Here, a database management system such as SQLite is used to store the data through SQL queries. The input is the received JSON data, and the output is the data stored in the database.

[0603] Step 4:

[0604] The server inputs the stored data into the generative AI system means for analysis. Specifically, a pre-trained generative AI model using Scikit-Learn or TensorFlow analyzes this data. For example, it determines that a temperature of 35°C is too high and generates an insight recommending the activation of a cooling system. The input is environmental data stored in the database, and the output is the insight resulting from the analysis.

[0605] Step 5:

[0606] The server sends the insights generated by the generation AI system means to the terminal via a communication means. Here, too, the analysis results are sent in JSON format using the HTTP protocol. The input is the insights as the analysis results, and the output is the insight data sent to the terminal.

[0607] Step 6:

[0608] The device presents the received insights to the user through a visual display. For example, a dashboard display such as "The temperature is too high. We recommend activating the cooling system" may be displayed on a smartphone or head-mounted display. The input is the insight data sent from the server, and the output is a visual display that the user can check.

[0609] Step 7:

[0610] Based on the visually displayed insights, the user can use management tools to take appropriate measures, for example, to activate the cooling system, which will reduce the temperature in the factory and eliminate the "overload" state of the machines. The input is the visually displayed insight, and the output is the action taken by the user.

[0611] 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.

[0612] This invention relates to a smart agriculture platform that combines generative AI models with digital sensors and a user emotion engine, with the aim of efficiently managing agricultural data and supporting future agricultural practices.

[0613] overview

[0614] The system consists of the following main components:

[0615] Sensor means: Devices for measuring soil condition, moisture content and light level.

[0616] Terminal means: A device for transmitting data collected from the sensor means to the server.

[0617] Server means: A device that receives data sent from the terminal means and stores it in a database.

[0618] Generative AI Means: An artificial intelligence system that analyzes data stored in the Server Means and generates insights for crop management, pest and disease prediction, and optimal use of fertilizer.

[0619] Emotion engine: A device that recognizes the user's emotional state.

[0620] User: The person or organization that operates the system and manages the farm.

[0621] System program and processing explanation

[0622] Data collection and transmission

[0623] The sensor means measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. For example, it measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C.

[0624] The terminal collects the measured data from the sensor means. The collected data is converted into, for example, JSON format and transmitted to the server means. Specifically, the data is transmitted using a data transmission protocol (for example, an HTTP POST request).

[0625] Data storage and analysis

[0626] The server means stores the received data in a database. For example, it inserts the data into the database using an SQL query. The generation AI means analyzes this stored data. The data is input into a pre-trained AI model to generate a prediction result. For example, it may determine that there is a 20% moisture deficiency, that a pH value of 7.2 is appropriate, and that a light intensity of 1,500 lux is appropriate.

[0627] The server generates insights and specific recommended actions based on the analysis results, for example, "Water content is insufficient. Irrigation is recommended."

[0628] Providing insights and responding to users

[0629] The server means transmits the generated analysis results and insights to the terminal means. For example, it transmits a message in JSON format saying, "Water level is insufficient. Irrigation is recommended." The terminal means displays the received analysis results and insights to the user in a visually easy-to-understand format. For example, it displays "Water level is insufficient. Irrigation is recommended" on a dashboard.

[0630] Utilizing the Emotion Engine

[0631] The emotion engine analyzes the user's voice, facial expressions, and body movements to recognize the user's emotional state in real time. For example, if the user has an anxious expression, it will capture that emotional data.

[0632] The server means adjusts insights and recommended actions based on the emotion data obtained from the emotion engine. For example, if the user is in an anxious state, the server means may provide more information to improve the situation or suggest additional customization options.

[0633] Customization Options and Consulting

[0634] The server means proposes suitable customization options based on the data of a particular farm. For example, it may recommend a highly disease-resistant crop variety "X." The user can review the proposed customization options and adjust the settings. The user can select crop variety "X" from the platform's settings screen. Furthermore, the user can subscribe to consulting services as needed to receive additional advice from experts.

[0635] Specific examples

[0636] Example 1: Insufficient soil moisture

[0637] 1. The sensor measures soil moisture at 20%, pH at 7.2, light intensity at 1500 lux, and temperature at 25°C.

[0638] 2. The terminal transmits the measurement data to the server, which stores and analyzes the data.

[0639] 3. The generating AI means generates an insight that "There is insufficient moisture. Irrigation is recommended.", and the server means transmits this to the terminal means.

[0640] 4. The terminal means displays the analysis results on a dashboard for the user to confirm.

[0641] 5. The emotion engine recognizes the user's anxious facial expression, and the server means provides detailed guidance and additional information.

[0642] 6. The user turns on the irrigation system to replenish the water.

[0643] Example 2: Proposing customization options

[0644] 1. The server means suggests a highly disease-resistant crop variety "X" based on data from a particular farm.

[0645] 2. The terminal means displays the proposed content to the user, who then confirms it.

[0646] 3. The emotion engine recognizes the user's interesting facial expressions and the server means provides further detailed information.

[0647] 4. The user selects crop variety "X" from the platform settings screen.

[0648] In this way, the system not only supports efficient and sustainable agricultural management, but also provides advanced support that takes into account the user's emotional state.

[0649] The processing flow will be explained below.

[0650] Specific processing steps of the program

[0651] Step 1: Data collection

[0652] 1. The sensor means measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time.

[0653] Example: Measure soil moisture content at 20%, pH value at 7.2, light intensity at 1500 lux, and temperature at 25°C.

[0654] 2. The terminal collects the measured data from the sensor means.

[0655] Convert the data into a format (e.g. JSON).

[0656] Example: { "soil_moisture": 20, "pH": 7.2, "light": 1500, "temperature": 25}

[0657] 3. The device sends the collected data to the server.

[0658] Send data using a data transmission protocol (e.g., HTTP POST request).

[0659] Example: POST / data { "soil_moisture": 20, "pH": 7.2, "light": 1500, "temperature": 25}

[0660] Step 2: Save data

[0661] 1. The server stores the received data in a database.

[0662] For example, inserting data into a database using an SQL query.

[0663] Example: INSERT INTO soil_data (moisture, pH, light, temperature) VALUES (20, 7.2, 1500, 25)

[0664] Step 3: Data analysis

[0665] 1. A generative AI model on the server analyzes the stored data.

[0666] For example, data is input into a pre-trained AI model to generate prediction results.

[0667] The generated AI model determines that "20% moisture content is insufficient," "pH value of 7.2 is neutral and appropriate," and "light intensity of 1500 lux is appropriate."

[0668] 2. The server generates insights and recommended actions based on the analysis results.

[0669] For example, generating an insight such as "Water level is insufficient. Irrigation recommended."

[0670] Step 4: Providing insights

[0671] 1. The server sends the generated analysis results and insights to the device.

[0672] Example: Send a message to the device in JSON format saying "Water level is low. Irrigation recommended."

[0673] 2. The device displays the received analysis results and insights to the user in a visually easy-to-understand format.

[0674] Example: Displaying "Insufficient moisture. Irrigation recommended" on the dashboard.

[0675] Step 5: Real-time adjustments

[0676] 1. The user looks at the device screen to see the analysis results and insights.

[0677] Example: Scroll through your dashboard to see if you're dehydrated.

[0678] 2. Users adjust how they manage their farms as needed.

[0679] For example: Turn on the irrigation system to replenish water.

[0680] Step 6: Leverage your emotional engine

[0681] 1. The emotion engine recognizes the user's emotional state from their voice, facial expressions, and body movements.

[0682] Example: If the user has an anxious expression, obtain the emotion data.

[0683] 2. The server adjusts insights and recommended actions based on the emotion data obtained from the emotion engine.

[0684] Example: If the user is in an anxious state, generate an action such as "provide more information to remedy the situation" or "suggest additional customization options."

[0685] 3. The server sends tailored insights and recommended actions to the device.

[0686] Example: Send the message "We will introduce detailed irrigation methods" in JSON format to the terminal.

[0687] 4. The device displays tailored insights and recommended actions to the user in a visually understandable format.

[0688] Example: Display "Detailed irrigation methods" on the dashboard.

[0689] Step 7: Customization options and consultation

[0690] 1. The server suggests suitable customization options based on the data for the specific farm.

[0691] Example: Propose a highly disease-resistant crop variety "X."

[0692] 2. The device displays the suggestions to the user.

[0693] Example: Displaying "We recommend the highly disease-resistant crop variety 'X'" on the dashboard.

[0694] 3. The user reviews the suggested customization options and adjusts the settings.

[0695] Example: Select crop variety "X" from the platform settings screen.

[0696] 4. The user applies for consulting services as needed.

[0697] For example: Consult an expert online for additional advice.

[0698] Through the above processing steps, the system supports efficient agricultural management and sustainable production, and also provides advanced support that takes into account the user's emotional state.

[0699] Example 2

[0700] 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."

[0701] Modern agriculture requires real-time monitoring of soil and environmental conditions, and prompt and appropriate farm management based on that monitoring. However, while conventional systems can collect and analyze sensor data, they lack the ability to provide specific action instructions based on the analysis results or support that takes into account the user's emotional state. Therefore, a system is needed that allows users to manage farms efficiently and sustainably without feeling any emotional burden.

[0702] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0703] In this invention, the server includes an emotion engine that recognizes the emotional state of the user, means for adjusting insights and recommended actions based on data obtained from the emotion engine, and means for transmitting the insights generated by the generating AI means to a terminal, thereby providing farm management advice optimized according to the user's emotional state, enabling the user to manage the farm efficiently and with reduced emotional burden.

[0704] The "sensor means" is a device for measuring soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time.

[0705] The "terminal means" is a device for transmitting data collected from the sensor means to the server means.

[0706] The "server means" is a device that receives data sent from the terminal means, stores the data in a database, and analyzes the data.

[0707] The "generative AI means" is an artificial intelligence system for analyzing data stored in the server means and generating insights regarding crop management, pest and disease prediction, and optimal use of fertilizer.

[0708] An "emotion engine" is a device that analyzes the user's voice, facial expressions, and body movements to recognize the user's emotional state in real time.

[0709] "Insights" are specific pieces of advice or recommended actions created by generative AI means to help users manage their farms.

[0710] "User" means an individual or organization that operates the system and manages the farm.

[0711] "Customization options" are options that suggest crop varieties, management methods, and other options that are best suited to a particular farm.

[0712] This invention relates to a smart agriculture platform that combines generative AI models with digital sensors and a user emotion engine, with the aim of efficiently managing agricultural data and supporting future agricultural practices.

[0713] overview

[0714] The system consists of the following main components:

[0715] Sensor means: A device for measuring soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. Examples of sensor means include soil moisture sensors, pH sensors, light sensors, and temperature sensors.

[0716] Terminal means: A device that transmits data collected from the sensor means to the server. Specifically, a gateway, smartphone, tablet, etc. are used.

[0717] Server means: A device that receives data sent from the terminal means and stores it in a database. For example, a cloud server or an on-premise server is used.

[0718] Generative AI means: An artificial intelligence system that analyzes data stored in the server means and generates insights on crop management, pest and disease prediction, and optimal fertilizer use. Deep learning models and machine learning algorithms are used as generative AI models.

[0719] Emotion engine: A device that analyzes and recognizes the user's emotional state (voice, facial expressions, and body movements) in real time. Specifically, a voice recognition system and a face recognition system are used.

[0720] User: An individual or organization that operates the system and manages the farm.

[0721] Data collection and transmission

[0722] The sensor means measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. For example, it measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C. The terminal means converts the collected data into JSON format and sends it to the server means using an HTTP POST request.

[0723] Data storage and analysis

[0724] The server means stores the received data in a database. For example, it inserts data into the database using an SQL query. The generative AI means performs analysis based on this stored data. The data is input into a pre-trained AI model to generate a prediction result. For example, it may determine that there is a 20% moisture deficiency, that a pH value of 7.2 is appropriate, and that a light intensity of 1,500 lux is appropriate. The server means generates insights and specific recommended actions based on the analysis results. For example, it generates an insight such as, "There is a moisture deficiency. Irrigation is recommended."

[0725] Providing insights and responding to users

[0726] The server means transmits the generated analysis results and insights to the terminal means. For example, it transmits a message in JSON format saying, "Water level is insufficient. Irrigation is recommended." The terminal means displays the received analysis results and insights to the user in a visually easy-to-understand format. For example, it displays "Water level is insufficient. Irrigation is recommended" on a dashboard.

[0727] Utilizing the Emotion Engine

[0728] The emotion engine analyzes the user's voice, facial expressions, and body movements to recognize the user's emotional state in real time. For example, if the user has an anxious expression, the emotion data is acquired. The server means adjusts insights and recommended actions based on the emotion data acquired from the emotion engine. For example, if the user is in an anxious state, the server means performs actions such as "providing detailed information to improve the situation" or "suggesting additional customization options."

[0729] Customization Options and Consulting

[0730] The server means proposes suitable customization options based on the data of a particular farm. For example, it may recommend a highly disease-resistant crop variety "X." The user can review the proposed customization options and adjust the settings. The user can select crop variety "X" from the platform's settings screen. Furthermore, the user can subscribe to consulting services as needed to receive additional advice from experts.

[0731] Specific examples

[0732] Example 1: Insufficient soil moisture

[0733] The sensor means measures the soil moisture content to be 20%, the pH value to be 7.2, the light intensity to be 1500 lux, and the temperature to be 25°C. The terminal means transmits the measurement data to the server, which stores and analyzes the data. The generation AI means generates an insight that "The amount of water is insufficient. Irrigation is recommended," which the server means transmits to the terminal means. The terminal means displays the analysis results on a dashboard for the user to confirm. The emotion engine recognizes the user's anxious expression, and the server means provides detailed guidance and additional information. The user turns on the irrigation system to replenish the water.

[0734] Example 2: Proposing customization options

[0735] The server means proposes a highly disease-resistant crop variety "X" based on data from a specific farm. The terminal means displays the proposal to the user, who confirms it. The emotion engine recognizes an interesting expression from the user, and the server means provides further detailed information. The user selects crop variety "X" from the platform's setting screen.

[0736] Prompt Sentence Examples

[0737] Examples of prompts include:

[0738] "Analyze current soil moisture, pH, light, and temperature data and recommend appropriate actions."

[0739] "If the user appears anxious, please suggest what additional information or support you can provide."

[0740] "Suggest the best crop varieties for your particular farm environment."

[0741] In this way, the system not only supports efficient and sustainable agricultural management, but also provides advanced support that takes into account the user's emotional state.

[0742] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0743] Step 1: Sensor measures data

[0744] The sensor means measures environmental data such as soil moisture content, pH value, light intensity, and temperature in real time. For example, at 9:00 AM, the sensor means measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C. These data are transmitted from the sensor means to the terminal means. The sensor means receives soil and environmental measurements as inputs and generates a set of sensor data as outputs.

[0745] Step 2: Device collects and transmits data

[0746] The terminal collects data received from the sensor means and converts it into JSON format. Specifically, it generates a JSON object with data such as soil moisture content, pH value, light intensity, and temperature as keys and values. The terminal sends this JSON data to the server means using an HTTP POST request. It receives sensor data as input, generates JSON format data as output, and sends it to the server.

[0747] Step 3: The server saves the data

[0748] The server receives the JSON data sent from the terminal means and stores it in the database using an SQL query. For example, it inserts it into the database using the SQL query "INSERT INTO sensor_data (timestamp, moisture, pH, lux, temperature) VALUES ('2023-10-01 09:00:00', 20, 7.2, 1500, 25);". It takes JSON data as input and generates stored data as output.

[0749] Step 4: The generative AI model analyzes the data

[0750] The server inputs the stored data into the generative AI model for analysis. The AI ​​model generates insights such as a 20% lack of moisture, an appropriate pH value of 7.2, and an appropriate light intensity of 1,500 lux. This allows insights such as "Irrigation is recommended" to be obtained as analysis results. It receives stored sensor data as input and generates analysis results as output.

[0751] Step 5: The server generates insights and sends them to the device

[0752] The server generates a specific insight message based on the analysis results obtained from the generative AI model. For example, it creates a message saying, "Water level is insufficient. Irrigation is recommended." This insight message is then converted back to JSON format and sent to the terminal. It receives the analysis results as input, generates an insight message as output, and sends it to the terminal.

[0753] Step 6: The device displays the insight to the user

[0754] The terminal analyzes the insight message received from the server and displays it to the user in a visually easy-to-understand format. Specifically, it displays a warning message on the dashboard saying, "Water level is insufficient. Irrigation is recommended." It receives the insight message as input and displays it to the user as output.

[0755] Step 7: The emotion engine recognizes the user's emotion

[0756] The emotion engine analyzes the user's voice, facial expressions, and body movements to recognize the user's emotional state in real time. For example, if the user has an anxious expression, it acquires that information and sends it to the server. It receives the user's voice and facial expression data as input and generates emotion data as output.

[0757] Step 8: The server adjusts the insights based on the sentiment data

[0758] The server adjusts insights and recommended actions based on the emotional data obtained from the emotion engine. If the user is anxious, it generates a message that provides specific operating procedures or additional support information. For example, it provides a detailed guide such as, "Here are the specific operating procedures for the irrigation system." It receives emotional data as input and generates adjusted recommended messages as output.

[0759] Step 9: User performs action

[0760] The user takes specific actions based on the insights and recommended actions displayed on the device, such as turning on the irrigation system to replenish soil moisture. The system takes insight messages from the device as input and actual farm management actions as output.

[0761] (Application example 2)

[0762] 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."

[0763] Factories need to realize efficient environmental monitoring and machine management, and respond quickly to unexpected machine breakdowns and environmental changes. Conventional systems collect data using sensors, but it is difficult to support real-time emotion recognition and detailed analysis of environmental conditions. They also lack the ability to effectively predict maintenance needs and propose appropriate management methods. Therefore, it is necessary to provide a system that can constantly monitor the environmental conditions in factories and provide optimal machine management and maintenance predictions while taking into account the user's emotional state.

[0764] The specific processing by the specific 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 measuring soil condition, moisture content, and light intensity; terminal means for collecting data from the sensor means and transmitting it to the server; server means for receiving data transmitted from the terminal means and storing it in a database; generation AI means for analyzing the data stored in the server means and generating insights regarding crop management, pest prediction, and optimal fertilizer use; means for transmitting the insights generated by the generation AI means to the terminal; means for displaying the insights to the user from the terminal; means for the user to adjust the farm management method based on the insights; emotion engine means for recognizing the user's emotional state and adjusting the insights and recommended actions generated by the server means; and monitoring means for constantly monitoring the environmental conditions (temperature, humidity, light, vibration) within the factory and performing appropriate machine management and maintenance prediction. This enables real-time environmental monitoring, appropriate machine management, and adjustments that take user emotions into consideration.

[0765] - "Soil conditions" refers to the physical and chemical conditions that affect crop growth, such as soil moisture, temperature, pH, and nutrient content.

[0766] "Moisture content" refers to the amount of water present in the soil or environment, and indicates the amount of water required for crop growth.

[0767] "Light intensity" refers to the amount of light that hits a certain area per unit of time, and is generally measured in lux.

[0768] "Sensor means" refers to a device for measuring soil moisture, pH value, temperature, light intensity, etc.

[0769] The "terminal means" is a device for collecting data from the sensor means and transmitting the data to the server.

[0770] The "server means" is a device that receives data sent from the terminal means, stores it in a database, and analyzes it.

[0771] A "generative AI means" is an artificial intelligence system that analyzes data stored in the server means and generates insights and predictions.

[0772] The "emotion engine means" is a device that analyzes the user's voice, facial expressions, body movements, etc., and recognizes the user's emotional state in real time.

[0773] A "monitoring means" is a device that constantly monitors the environmental conditions within a factory (temperature, humidity, light, vibration, etc.) and performs appropriate machine management and maintenance predictions.

[0774] "Insights" refers to specific indications and recommendations based on the results of data analysis that indicate future situations and necessary countermeasures.

[0775] "Customization options" refers to suggestions for achieving more optimal management and operation by adjusting options and settings to suit specific conditions.

[0776] "Platform" refers to the entire system realized by the mutual cooperation of multiple elements such as sensor means, terminal means, server means, and generating AI means.

[0777] This invention is a system for advanced environmental monitoring and machine management in factories. This system is composed of multiple elements such as sensor means, terminal means, server means, generation AI means, emotion engine means, and monitoring means.

[0778] 1. Sensor data collection

[0779] Sensor means are devices that measure environmental data such as temperature, humidity, light intensity, and vibration in real time. For example, a temperature sensor measures the temperature at each operating point in a factory, a humidity sensor measures humidity, a light sensor detects illuminance, and a vibration sensor monitors the vibration state of machines.

[0780] 2. Data transmission

[0781] The terminal means is a device that transmits data collected from the sensor means to the server means. The data is converted to JSON format and transmitted using an HTTP POST request. For example, the data transmitted may be temperature 25.5 degrees, humidity 55%, light intensity 1200 lux, and vibration 0.3 m / s².

[0782] 3. Data storage and analysis

[0783] The server means receives the data sent from the terminal means and stores it in a database. The generation AI means analyzes the stored data and generates insights. Here, an AI model is used to detect abnormalities in temperature or vibration and consider countermeasures. For example, it generates an insight such as "The temperature is too high, so the cooling system needs to be activated."

[0784] 4. Providing insights and notifying users

[0785] The server means transmits the generated insight to the terminal means, which visually presents the insight to the user, for example, by displaying "Temperature is too high. Please turn on the cooling system" on a dashboard.

[0786] 5. Leveraging Emotional Engines

[0787] The emotion engine means recognizes the emotional state of the user by detecting the user's voice, facial expression, and body movements. For example, if the user has an anxious expression, the server means acquires that data. The server means adjusts the insight based on the emotional data acquired from the emotion engine means. For example, it is possible to respond by saying, "Since the user has an anxious expression, we will provide detailed guidance."

[0788] 6. Environmental monitoring and machinery management

[0789] The monitoring tool is a device that constantly monitors the environmental conditions in the factory and performs appropriate machine management and maintenance predictions. For example, if an abnormal rise in temperature is detected, it will immediately issue a warning and recommend activation of the cooling system.

[0790] Specific examples

[0791] For example, if a temperature sensor in a factory detects that the temperature has reached 30 degrees, the data is sent to the server via the terminal means. Analysis is performed on the server side and activation of the cooling system is recommended. The generated insight is sent to the terminal and displayed on the user's dashboard as "The temperature has reached 30 degrees. Please turn on the cooling system." Also, if the emotion engine detects that the user has an anxious expression, detailed guidance is provided.

[0792] Prompt Sentence Examples

[0793] An example of a prompt used in this system is, "The current factory environment data is temperature 30°C, humidity 60%, light intensity 1000 lux, and vibration 0.4 m / s². Please perform optimal machine management and maintenance prediction based on this." By analyzing this prompt, the AI ​​can generate specific insights and actions.

[0794] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0795] Step 1:

[0796] The sensor means measures environmental data (temperature, humidity, light intensity, vibration) in the factory in real time. For example, the temperature sensor measures the temperature as 25.5 degrees, the humidity sensor measures the humidity as 55%, the light sensor measures the light intensity as 1200 lux, and the vibration sensor measures the vibration as 0.3 m / s². These data are collected and sent to the terminal means as input.

[0797] Step 2:

[0798] The terminal means converts the data collected from the sensor means into JSON format and sends it to the server. For example, the JSON format data includes { "temperature": 25.5, "humidity": 55, "light": 1200, "vibration": 0.3}. This data is sent using an HTTP POST request. The data converted into JSON format is input to the server.

[0799] Step 3:

[0800] The server means receives the data sent from the terminal means and stores it in the database. The data is inserted into the database using an SQL query. For example, the query INSERT INTO environmental_data (temperature, humidity, light, vibration) VALUES (25.5, 55, 1200, 0.3) is executed. The received data is stored as output in the database.

[0801] Step 4:

[0802] The server means analyzes the stored data using the generating AI means and generates insights. For example, a temperature of 25.5 degrees is judged to be normal, while a vibration of 0.3 m / s² is judged to be abnormal. This analysis result is the result of data calculation by the generating AI and is output as insights.

[0803] Step 5:

[0804] The server means transmits the generated insight to the terminal means. The generated insight, for example, "Vibration is high. Machine maintenance is required," is transmitted in JSON format. The transmitted insight is sent as input to the terminal.

[0805] Step 6:

[0806] The terminal means visually presents the received insight to the user, for example by displaying a message on the dashboard saying "Vibration is high. Machine maintenance required." The user is provided with visual information and maintenance instructions as output.

[0807] Step 7:

[0808] The emotion engine means detects the user's voice, facial expression, and body movement to recognize the user's emotional state. For example, if the user has an anxious expression, that data is acquired from the emotion engine means. The acquired emotion data is input to the server.

[0809] Step 8:

[0810] The server means adjusts the insight based on the emotion data acquired from the emotion engine means. For example, an action such as "provide detailed guidance" is generated based on anxious facial expression data. Based on the emotion data, the adjusted insight or additional information is generated as an output.

[0811] Step 9:

[0812] Monitoring means constantly monitor the environmental conditions within the factory and perform appropriate machine management and maintenance predictions. If an abnormality is detected, an alert is issued immediately and necessary measures are recommended. For example, a warning such as "The temperature is rising sharply. Please turn on the cooling system" is output.

[0813] Example prompt sentence:

[0814] "The current factory environment data is: temperature 30°C, humidity 60%, light intensity 1000 lux, vibration 0.4 m / s². Please use this information to perform optimal machine management and maintenance predictions."

[0815] 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.

[0816] 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.

[0817] 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.

[0818] [Third embodiment]

[0819] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0820] 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.

[0821] 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).

[0822] 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.

[0823] 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.

[0824] 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).

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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."

[0831] This invention relates to a smart agriculture platform that combines generative AI models and digital sensors to efficiently manage agricultural data and support future agricultural practices.

[0832] overview

[0833] The system consists of the following main components:

[0834] Sensor means: Devices for measuring soil condition, moisture content and light level.

[0835] Terminal means: A device for transmitting data collected from the sensor means to the server.

[0836] Server means: A device that receives data sent from the terminal means and stores it in a database.

[0837] Generative AI Means: An artificial intelligence system that analyzes data stored in the Server Means and generates insights for crop management, pest and disease prediction, and optimal use of fertilizer.

[0838] User: The person or organization that operates the system and manages the farm.

[0839] System program and processing explanation

[0840] Data collection

[0841] The sensor means measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. For example, it measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C.

[0842] Next, the terminal means collects the data. The collected data is converted into, for example, a JSON format and transmitted to the server means. Specifically, the data is transmitted using a data transmission protocol (for example, an HTTP POST request).

[0843] Data analysis

[0844] The server means stores the received data in a database. For example, it inserts the data into the database using an SQL query. The generation AI means analyzes this stored data. The data is input into a pre-trained AI model to generate a prediction result. For example, it may determine that there is a 20% moisture deficiency, that a pH value of 7.2 is appropriate, and that a light intensity of 1,500 lux is appropriate.

[0845] The server generates insights and specific recommended actions based on the analysis results, for example, "Water content is insufficient. Irrigation is recommended."

[0846] Providing insights

[0847] The server means transmits the generated analysis results and insights to the terminal means. For example, it transmits a message in JSON format saying, "Water level is insufficient. Irrigation is recommended." The terminal means displays the received analysis results and insights to the user in a visually easy-to-understand format. For example, it displays "Water level is insufficient. Irrigation is recommended" on a dashboard.

[0848] Real-time adjustments

[0849] Users can view analytics and insights on their device screens, scroll through dashboards to identify water deficiencies, and then adjust farm management as needed, such as turning on irrigation systems to replenish water.

[0850] Customization Options and Consulting

[0851] The server means proposes suitable customization options based on the data of a particular farm. For example, it may recommend a highly disease-resistant crop variety "X." The user can review the proposed customization options and adjust the settings. The user can select crop variety "X" from the platform's settings screen. Furthermore, the user can subscribe to consulting services as needed to receive additional advice from experts.

[0852] In this way, the smart agriculture platform of the present invention provides farmers with the data and insights they need to achieve more efficient and sustainable agricultural management.

[0853] The processing flow will be explained below.

[0854] Specific processing steps of the program

[0855] Step 1: Data collection

[0856] 1. The sensor means measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time.

[0857] Example: Measure soil moisture content at 20%, pH value at 7.2, light intensity at 1500 lux, and temperature at 25°C.

[0858] 2. The terminal collects the measured data from the sensor means.

[0859] Convert the data into a format (e.g. JSON).

[0860] Example: { "soil_moisture": 20, "pH": 7.2, "light": 1500, "temperature": 25}

[0861] 3. The device sends the collected data to the server.

[0862] Send data using a data transmission protocol (e.g., HTTP POST request).

[0863] Example: POST / data { "soil_moisture": 20, "pH": 7.2, "light": 1500, "temperature": 25}

[0864] Step 2: Save data

[0865] 1. The server stores the received data in a database.

[0866] Example: Inserting data into a database using an SQL query.

[0867] Example: INSERT INTO soil_data (moisture, pH, light, temperature) VALUES (20, 7.2, 1500, 25)

[0868] Step 3: Data analysis

[0869] 1. A generative AI model on the server analyzes the stored data.

[0870] Example: Inputting data into a pre-trained AI model to generate predictions.

[0871] The model determines that "20% moisture content is insufficient," "a pH value of 7.2 is neutral and appropriate," and "a light intensity of 1500 lux is appropriate."

[0872] 2. The server generates insights and recommended actions based on the analysis results.

[0873] For example: Generate the insight "Water level is insufficient. Irrigation recommended."

[0874] Step 4: Providing insights

[0875] 1. The server sends the generated analysis results and insights to the device.

[0876] Example: Send a message to the device in JSON format saying "Water level is low. Irrigation recommended."

[0877] 2. The device displays the received analysis results and insights to the user in a visually easy-to-understand format.

[0878] Example: Displaying "Insufficient moisture. Irrigation recommended" on the dashboard.

[0879] Step 5: Real-time adjustments

[0880] 1. The user looks at the device screen to see the analysis results and insights.

[0881] Example: Scroll through your dashboard to see if you're dehydrated.

[0882] 2. Users adjust how they manage their farms as needed.

[0883] For example: Turn on the irrigation system to replenish water.

[0884] Step 6: Customization options and consultation

[0885] 1. The server suggests suitable customization options based on the data for the specific farm.

[0886] Example: Propose a highly disease-resistant crop variety "X."

[0887] 2. The user reviews the suggested customization options and adjusts the settings.

[0888] Example: Select crop variety "X" from the platform settings screen.

[0889] 3. The user applies for consulting services as needed.

[0890] For example: Consult an expert online for additional advice.

[0891] In this way, the system supports efficient and sustainable agricultural management.

[0892] Example 1

[0893] 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."

[0894] Conventional agricultural management systems face challenges in efficiently collecting data from the entire farm and analyzing it in real time, making it difficult to perform appropriate crop management, predict pests and diseases, and optimize fertilizer use. They also face the problem of being unable to propose customized options suited to specific farms and adjust management methods accordingly.

[0895] 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.

[0896] In this invention, the server includes a sensor device that measures soil condition, moisture content, and light intensity; a terminal device that collects data from the sensor device and transmits it to the server; a server device that receives the data transmitted from the terminal device and stores it in a database; a generating AI device that analyzes the data stored in the server device and generates insights regarding crop management, pest and disease prediction, and optimal fertilizer use; a device that transmits the insights generated by the generating AI device to the terminal device; a device that displays the insights to a user from the terminal device; and a device that allows the user to adjust farm management methods based on the insights. This enables real-time data collection and analysis, appropriate crop management, pest and disease prediction, and optimal fertilizer use. It also enables the system to quickly and effectively propose customized options suitable for specific farms and adjust management methods based on those options.

[0897] A "sensor device" is a device for measuring soil conditions, moisture content, light intensity, and the like.

[0898] A "terminal device" is a device for transmitting data collected from a sensor device to a server.

[0899] A "server device" is a device that receives data sent from a terminal device and stores it in a database.

[0900] The "generative AI device" is an artificial intelligence system that analyzes data stored on a server device and generates insights into crop management, pest and disease prediction, and optimal fertilizer use.

[0901] A "display device" is a device for visually displaying insights from a terminal device to a user.

[0902] A "user" is an individual or organization that adjusts how they manage their farm based on the insights generated.

[0903] "Customization options" are options that suggest adjustments and settings that are suitable for a particular farm.

[0904] "Real-time adjustment" is the process by which users instantly change how they manage their farm based on the insights generated.

[0905] System Overview

[0906] The present invention relates to a smart agriculture platform that combines generative AI models and digital sensors to efficiently manage agricultural data and support future agricultural practices.

[0907] The system consists of the following main components:

[0908] Sensor devices: devices for measuring soil condition, moisture content and light intensity.

[0909] Terminal device: A device for transmitting data collected from a sensor device to a server.

[0910] Server device: A device that receives data sent from a terminal device and stores it in a database.

[0911] Generative AI device: An artificial intelligence system that analyzes data stored on a server device and generates insights for crop management, pest and disease prediction, and optimal fertilizer use.

[0912] Display device: A device for transmitting the insights generated by the generative AI device to a terminal device and visually displaying them to the user.

[0913] User: The person or organization that operates the system and manages the farm.

[0914] Examples of data collection

[0915] The sensor device measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. For example, it measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C. Next, the terminal device collects this data. The collected data is converted into, for example, JSON format and sent to the server device. Specifically, the data is sent using a data transmission protocol (for example, an HTTP POST request).

[0916] Specific examples of data analysis

[0917] The server device stores the received data in a database. For example, it inserts data into the database using an SQL query. The generative AI device performs analysis based on this stored data. The data is input into a pre-trained AI model to generate a prediction result. For example, it may determine that there is a 20% moisture deficiency, that a pH value of 7.2 is appropriate, and that a light intensity of 1,500 lux is appropriate. The server device generates insights and specific recommended actions based on the analysis results. For example, it may generate an insight such as, "There is a moisture deficiency. Irrigation is recommended."

[0918] Providing insights and real-time adjustments

[0919] The server device sends the generated analysis results and insights to the terminal device. For example, it sends a message in JSON format saying, "Water level is insufficient. Irrigation recommended." The terminal device displays the received analysis results and insights to the user in a visually easy-to-understand format. For example, it displays "Water level is insufficient. Irrigation recommended" on a dashboard.

[0920] Users can view analytics and insights on their device screens, scroll through dashboards to identify water deficiencies, and then adjust farm management as needed, such as switching on irrigation systems to replenish water.

[0921] Customization options and consulting

[0922] The server device proposes suitable customization options based on the data of a specific farm. For example, it may recommend a highly disease-resistant crop variety "X." The user can review the proposed customization options and adjust the settings. They can select crop variety "X" from the platform's settings screen. Furthermore, users can request consulting services to receive additional advice from experts as needed.

[0923] Prompt Sentence Examples

[0924] For example, if the following data is measured by a sensor device on a farm:

[0925] Land Data:

[0926] Soil moisture content: 15%

[0927] pH value: 6.8

[0928] Light output: 1300 lux

[0929] Temperature: 22℃

[0930] Based on this data, we input the following prompt to the generative AI model:

[0931] Based on this data, what are your recommended actions for crop management?

[0932] In this way, the smart agriculture platform of the present invention provides farmers with the data and insights they need to achieve more efficient and sustainable agricultural management.

[0933] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0934] Step 1:

[0935] The sensor device measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. Specifically, it measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C. This is used as input data.

[0936] Step 2:

[0937] The terminal device collects measurement data from the sensor device. The collected data is converted into JSON format and sent to the server. Specifically, the JSON format data is sent to the server using a data transmission protocol (for example, an HTTP POST request). This allows the server to receive the data collected from the sensor.

[0938] Step 3:

[0939] The server stores the received data in a database. Specifically, it inserts data into the database using an SQL query. It uses the received JSON data as input and obtains the state stored in the database as output.

[0940] Step 4:

[0941] The generative AI device analyzes the data stored in the database. The data is input into a pre-trained AI model to generate a predicted result. Specifically, it determines that there is a 20% moisture deficiency, that a pH value of 7.2 is appropriate, and that a light intensity of 1500 lux is appropriate. The data in the database is used as input data, and a predicted result is obtained as output.

[0942] Step 5:

[0943] The server generates insights based on the analysis results. Specifically, it generates a message based on the analysis results saying, "Water level is insufficient. Irrigation is recommended." It uses the prediction results as input data and obtains the generated insights as output.

[0944] Step 6:

[0945] The server sends the generated insight to the terminal device. Specifically, it sends a message in JSON format saying, "Water level is insufficient. Irrigation is recommended." The terminal device then receives the insight.

[0946] Step 7:

[0947] The terminal device displays the received insight to the user. Specifically, it displays "Water level is low. Irrigation recommended" on the dashboard. This allows the user to visually confirm the insight.

[0948] Step 8:

[0949] The user looks at the screen of their device to see the analysis results and insights. Specifically, they scroll through the dashboard to identify water deficiencies. If necessary, they adjust their farm management, for example, by switching on the irrigation system to replenish water. This ensures that the farm is properly managed.

[0950] Step 9:

[0951] The server then proposes appropriate customization options based on the data of a specific farm. Specifically, it generates an option such as "We suggest a highly disease-resistant crop variety 'X'." This allows the user to have useful choices.

[0952] Step 10:

[0953] The user reviews and applies the proposed customization options, specifically by selecting crop variety "X" in the platform's settings screen, thereby optimizing farm management.

[0954] (Application example 1)

[0955] 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."

[0956] Traditional agricultural management systems often lack the ability to collect, analyze, and provide insights in real time. This can make it difficult for farmers to respond quickly and appropriately, potentially resulting in reduced production efficiency and quality. Similar issues arise in factory environments, where effective management of temperature, humidity, light levels, and machine status data is required.

[0957] 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.

[0958] In this invention, the server comprises device means for measuring soil conditions, moisture content, and light intensity, terminal device means for collecting data from the device means and transmitting it to the server, storage means for receiving data transmitted from the terminal device means and storing it in a database, generation AI system means for analyzing the data stored in the storage means and generating insights regarding crop management, pest prediction, and optimal use of fertilizer, communication means for transmitting the insights generated by the generation AI system means to a terminal, visual display means for displaying the insights from the terminal to a user, management means for allowing the user to adjust the farm management method based on the insights, and temperature, humidity, and other factors of the factory environment. The system includes a device means for measuring temperature and light levels and collecting machine status data, a terminal device means for collecting data from the device means and transmitting it to a server, a storage device means for receiving the data transmitted from the terminal device means and storing it in a database, a generative AI system means for analyzing the data stored in the storage device means and generating insights for optimizing the production process, activating a cooling system, and reducing machine load, a communication means for transmitting the insights generated by the generative AI system means to a terminal, a visual display means for displaying the insights to a worker from the terminal, and a management means for the worker to adjust the production process based on the insights. This enables real-time data collection, analysis, and provision of insights in agricultural and factory environments, enabling prompt and appropriate responses.

[0959] "Soil condition" refers to the physical, chemical, and biological characteristics of the surface and subsurface of agricultural land.

[0960] "Moisture content" refers to the percentage of water contained in a particular area or substance.

[0961] "Light intensity" refers to the intensity or amount of light measured at a particular location.

[0962] "Device means" refers to the measuring instruments or equipment used to collect specific data.

[0963] "Terminal device means" refers to a communication device for collecting data and transmitting it to other devices or servers.

[0964] "Storage means" refers to a database or storage device for storing collected data.

[0965] "Generative AI system means" refers to a system that uses a pre-trained artificial intelligence model to analyze data and generate insights and predictions.

[0966] "Communications means" refers to technologies and devices for sending and receiving data and information.

[0967] "Visual display means" refers to devices or techniques for visually displaying data or insights to a user.

[0968] "Control measures" refers to methods and means for adjusting and controlling the way farms or factories are run in real time based on said insights.

[0969] "Machine status" refers to data indicating the state of a particular machine, such as whether it is running, stopped, or overloaded.

[0970] A "production process" refers to the series of operations or steps taken to produce a particular product or service.

[0971] "Cooling system" means a system or device for reducing the temperature of an environment or machine.

[0972] "Machine load" refers to the amount of work or workload being processed by a particular machine.

[0973] The embodiment of the present invention will be specifically described as follows: The system of the present invention is mainly composed of a device means, a terminal device means, a storage means, a generation AI system means, a communication means, a visual display means, and a management means.

[0974] System configuration and operation

[0975] Device Means

[0976] The device means is for measuring environmental data of a farm or factory in real time, for example, in the case of a farm it may include sensors measuring soil condition, moisture content, and light level, and in the case of a factory it may include sensors measuring temperature, humidity, light level, and machine status.

[0977] terminal device means

[0978] The end device is responsible for transmitting the data collected from the sensors to the server. This can be a communication device such as a smartphone or a head-mounted display. The data is converted to JSON format, for example, and sent to the server using an HTTP POST request.

[0979] storage means

[0980] The server receives the data sent from the terminal device and stores it in a database, using a database management system such as SQLite, where data is stored through SQL queries.

[0981] Generative AI system means

[0982] The data stored on the server is analyzed by pre-trained generative AI models, built using frameworks such as Scikit-Learn and TensorFlow, which generate insights for crop management, pest and disease prediction, optimal fertilizer use, optimizing production processes, activating cooling systems, and reducing machine loads.

[0983] communication means

[0984] The insights generated by the generative AI system are sent to the terminal via a communication means, again using the HTTP protocol, and the insight data is typically sent in JSON format.

[0985] Visual display means

[0986] The device is responsible for visually displaying the received insights, allowing users to review them through interfaces such as dashboards and graphs.

[0987] management measures

[0988] Based on the displayed insights, users can adjust management measures, such as activating irrigation systems or adjusting machine loads. For example, if temperatures in a factory become too high, cooling systems can be activated immediately. Production processes can also be optimized and machine loads adjusted as needed.

[0989] Adding specific examples

[0990] For example, if the temperature in a factory reaches 35°C, the sensor means measures this and the terminal device means transmits the data to the server. The server stores the data in a database, and the generating AI system means analyzes that the temperature is too high and generates an insight recommending the activation of a cooling system. This insight is transmitted to the terminal via the communication means, and an operator can view it via a head-mounted display.

[0991] Examples of prompts:

[0992] “If the temperature in the factory reaches 35°C, the humidity is 70%, the light level is 5000 lux, and the machine status is ‘overloaded,’ use a generative AI model to analyze the data and suggest appropriate countermeasures.”

[0993] Thus, the present invention provides the data and insights needed to achieve efficient and sustainable management in agricultural and industrial environments.

[0994] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0995] Step 1:

[0996] The device means measures the environmental data in real time. For example, the temperature in the factory is 35°C, humidity is 70%, light level is 5000 lux, and machine status is "overloaded". This is the input data, and the collected data is converted into JSON format. The output of this step is the environmental data in JSON format.

[0997] Step 2:

[0998] The terminal device means receives the data collected from the device means and sends it to the server. Specifically, JSON format data is sent to the server using an HTTP POST request. The input is the JSON data generated in step 1, and the output is a success response to the data transmission to the server.

[0999] Step 3:

[1000] The server receives the JSON data sent from the terminal device means and stores it in a database. Here, a database management system such as SQLite is used to store the data through SQL queries. The input is the received JSON data, and the output is the data stored in the database.

[1001] Step 4:

[1002] The server inputs the stored data into the generative AI system means for analysis. Specifically, a pre-trained generative AI model using Scikit-Learn or TensorFlow analyzes this data. For example, it determines that a temperature of 35°C is too high and generates an insight recommending the activation of a cooling system. The input is environmental data stored in the database, and the output is the insight resulting from the analysis.

[1003] Step 5:

[1004] The server sends the insights generated by the generation AI system means to the terminal via a communication means. Here, too, the analysis results are sent in JSON format using the HTTP protocol. The input is the insights as the analysis results, and the output is the insight data sent to the terminal.

[1005] Step 6:

[1006] The device presents the received insights to the user through a visual display. For example, a dashboard display such as "The temperature is too high. We recommend activating the cooling system" may be displayed on a smartphone or head-mounted display. The input is the insight data sent from the server, and the output is a visual display that the user can check.

[1007] Step 7:

[1008] Based on the visually displayed insights, the user can use management tools to take appropriate measures, for example, to activate the cooling system, which will reduce the temperature in the factory and eliminate the "overload" state of the machines. The input is the visually displayed insight, and the output is the action taken by the user.

[1009] 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.

[1010] This invention relates to a smart agriculture platform that combines generative AI models with digital sensors and a user emotion engine, with the aim of efficiently managing agricultural data and supporting future agricultural practices.

[1011] overview

[1012] The system consists of the following main components:

[1013] Sensor means: Devices for measuring soil condition, moisture content and light level.

[1014] Terminal means: A device for transmitting data collected from the sensor means to the server.

[1015] Server means: A device that receives data sent from the terminal means and stores it in a database.

[1016] Generative AI Means: An artificial intelligence system that analyzes data stored in the Server Means and generates insights for crop management, pest and disease prediction, and optimal use of fertilizer.

[1017] Emotion engine: A device that recognizes the user's emotional state.

[1018] User: The person or organization that operates the system and manages the farm.

[1019] System program and processing explanation

[1020] Data collection and transmission

[1021] The sensor means measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. For example, it measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C.

[1022] The terminal collects the measured data from the sensor means. The collected data is converted into, for example, JSON format and transmitted to the server means. Specifically, the data is transmitted using a data transmission protocol (for example, an HTTP POST request).

[1023] Data storage and analysis

[1024] The server means stores the received data in a database. For example, it inserts the data into the database using an SQL query. The generation AI means analyzes this stored data. The data is input into a pre-trained AI model to generate a prediction result. For example, it may determine that there is a 20% moisture deficiency, that a pH value of 7.2 is appropriate, and that a light intensity of 1,500 lux is appropriate.

[1025] The server generates insights and specific recommended actions based on the analysis results, for example, "Water content is insufficient. Irrigation is recommended."

[1026] Providing insights and responding to users

[1027] The server means transmits the generated analysis results and insights to the terminal means. For example, it transmits a message in JSON format saying, "Water level is insufficient. Irrigation is recommended." The terminal means displays the received analysis results and insights to the user in a visually easy-to-understand format. For example, it displays "Water level is insufficient. Irrigation is recommended" on a dashboard.

[1028] Utilizing the Emotion Engine

[1029] The emotion engine analyzes the user's voice, facial expressions, and body movements to recognize the user's emotional state in real time. For example, if the user has an anxious expression, it will capture that emotional data.

[1030] The server means adjusts insights and recommended actions based on the emotion data obtained from the emotion engine. For example, if the user is in an anxious state, the server means may provide more information to improve the situation or suggest additional customization options.

[1031] Customization Options and Consulting

[1032] The server means proposes suitable customization options based on the data of a particular farm. For example, it may recommend a highly disease-resistant crop variety "X." The user can review the proposed customization options and adjust the settings. The user can select crop variety "X" from the platform's settings screen. Furthermore, the user can subscribe to consulting services as needed to receive additional advice from experts.

[1033] Specific examples

[1034] Example 1: Insufficient soil moisture

[1035] 1. The sensor measures soil moisture at 20%, pH at 7.2, light intensity at 1500 lux, and temperature at 25°C.

[1036] 2. The terminal transmits the measurement data to the server, which stores and analyzes the data.

[1037] 3. The generating AI means generates an insight that "There is insufficient moisture. Irrigation is recommended.", and the server means transmits this to the terminal means.

[1038] 4. The terminal means displays the analysis results on a dashboard for the user to confirm.

[1039] 5. The emotion engine recognizes the user's anxious facial expression, and the server means provides detailed guidance and additional information.

[1040] 6. The user turns on the irrigation system to replenish the water.

[1041] Example 2: Proposing customization options

[1042] 1. The server means suggests a highly disease-resistant crop variety "X" based on data from a particular farm.

[1043] 2. The terminal means displays the proposed content to the user, who then confirms it.

[1044] 3. The emotion engine recognizes the user's interesting facial expressions and the server means provides further detailed information.

[1045] 4. The user selects crop variety "X" from the platform settings screen.

[1046] In this way, the system not only supports efficient and sustainable agricultural management, but also provides advanced support that takes into account the user's emotional state.

[1047] The processing flow will be explained below.

[1048] Specific processing steps of the program

[1049] Step 1: Data collection

[1050] 1. The sensor means measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time.

[1051] Example: Measure soil moisture content at 20%, pH value at 7.2, light intensity at 1500 lux, and temperature at 25°C.

[1052] 2. The terminal collects the measured data from the sensor means.

[1053] Convert the data into a format (e.g. JSON).

[1054] Example: { "soil_moisture": 20, "pH": 7.2, "light": 1500, "temperature": 25}

[1055] 3. The device sends the collected data to the server.

[1056] Send data using a data transmission protocol (e.g., HTTP POST request).

[1057] Example: POST / data { "soil_moisture": 20, "pH": 7.2, "light": 1500, "temperature": 25}

[1058] Step 2: Save data

[1059] 1. The server stores the received data in a database.

[1060] For example, inserting data into a database using an SQL query.

[1061] Example: INSERT INTO soil_data (moisture, pH, light, temperature) VALUES (20, 7.2, 1500, 25)

[1062] Step 3: Data analysis

[1063] 1. A generative AI model on the server analyzes the stored data.

[1064] For example, data is input into a pre-trained AI model to generate prediction results.

[1065] The generated AI model determines that "20% moisture content is insufficient," "pH value of 7.2 is neutral and appropriate," and "light intensity of 1500 lux is appropriate."

[1066] 2. The server generates insights and recommended actions based on the analysis results.

[1067] For example, generating an insight such as "Water level is insufficient. Irrigation recommended."

[1068] Step 4: Providing insights

[1069] 1. The server sends the generated analysis results and insights to the device.

[1070] Example: Send a message to the device in JSON format saying "Water level is low. Irrigation recommended."

[1071] 2. The device displays the received analysis results and insights to the user in a visually easy-to-understand format.

[1072] Example: Displaying "Insufficient moisture. Irrigation recommended" on the dashboard.

[1073] Step 5: Real-time adjustments

[1074] 1. The user looks at the device screen to see the analysis results and insights.

[1075] Example: Scroll through your dashboard to see if you're dehydrated.

[1076] 2. Users adjust how they manage their farms as needed.

[1077] For example: Turn on the irrigation system to replenish water.

[1078] Step 6: Leverage your emotional engine

[1079] 1. The emotion engine recognizes the user's emotional state from their voice, facial expressions, and body movements.

[1080] Example: If the user has an anxious expression, obtain the emotion data.

[1081] 2. The server adjusts insights and recommended actions based on the emotion data obtained from the emotion engine.

[1082] Example: If the user is in an anxious state, generate an action such as "provide more information to remedy the situation" or "suggest additional customization options."

[1083] 3. The server sends tailored insights and recommended actions to the device.

[1084] Example: Send the message "We will introduce detailed irrigation methods" in JSON format to the terminal.

[1085] 4. The device displays tailored insights and recommended actions to the user in a visually understandable format.

[1086] Example: Display "Detailed irrigation methods" on the dashboard.

[1087] Step 7: Customization options and consultation

[1088] 1. The server suggests suitable customization options based on the data for the specific farm.

[1089] Example: Propose a highly disease-resistant crop variety "X."

[1090] 2. The device displays the suggestions to the user.

[1091] Example: Displaying "We recommend the highly disease-resistant crop variety 'X'" on the dashboard.

[1092] 3. The user reviews the suggested customization options and adjusts the settings.

[1093] Example: Select crop variety "X" from the platform settings screen.

[1094] 4. The user applies for consulting services as needed.

[1095] For example: Consult an expert online for additional advice.

[1096] Through the above processing steps, the system supports efficient agricultural management and sustainable production, and also provides advanced support that takes into account the user's emotional state.

[1097] Example 2

[1098] 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."

[1099] Modern agriculture requires real-time monitoring of soil and environmental conditions, and prompt and appropriate farm management based on that monitoring. However, while conventional systems can collect and analyze sensor data, they lack the ability to provide specific action instructions based on the analysis results or support that takes into account the user's emotional state. Therefore, a system is needed that allows users to manage farms efficiently and sustainably without feeling any emotional burden.

[1100] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1101] In this invention, the server includes an emotion engine that recognizes the emotional state of the user, means for adjusting insights and recommended actions based on data obtained from the emotion engine, and means for transmitting the insights generated by the generating AI means to a terminal, thereby providing farm management advice optimized according to the user's emotional state, enabling the user to manage the farm efficiently and with reduced emotional burden.

[1102] The "sensor means" is a device for measuring soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time.

[1103] The "terminal means" is a device for transmitting data collected from the sensor means to the server means.

[1104] The "server means" is a device that receives data sent from the terminal means, stores the data in a database, and analyzes the data.

[1105] The "generative AI means" is an artificial intelligence system for analyzing data stored in the server means and generating insights regarding crop management, pest and disease prediction, and optimal use of fertilizer.

[1106] An "emotion engine" is a device that analyzes the user's voice, facial expressions, and body movements to recognize the user's emotional state in real time.

[1107] "Insights" are specific pieces of advice or recommended actions created by generative AI means to help users manage their farms.

[1108] "User" means an individual or organization that operates the system and manages the farm.

[1109] "Customization options" are options that suggest crop varieties, management methods, and other options that are best suited to a particular farm.

[1110] This invention relates to a smart agriculture platform that combines generative AI models with digital sensors and a user emotion engine, with the aim of efficiently managing agricultural data and supporting future agricultural practices.

[1111] overview

[1112] The system consists of the following main components:

[1113] Sensor means: A device for measuring soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. Examples of sensor means include soil moisture sensors, pH sensors, light sensors, and temperature sensors.

[1114] Terminal means: A device that transmits data collected from the sensor means to the server. Specifically, a gateway, smartphone, tablet, etc. are used.

[1115] Server means: A device that receives data sent from the terminal means and stores it in a database. For example, a cloud server or an on-premise server is used.

[1116] Generative AI means: An artificial intelligence system that analyzes data stored in the server means and generates insights on crop management, pest and disease prediction, and optimal fertilizer use. Deep learning models and machine learning algorithms are used as generative AI models.

[1117] Emotion engine: A device that analyzes and recognizes the user's emotional state (voice, facial expressions, and body movements) in real time. Specifically, a voice recognition system and a face recognition system are used.

[1118] User: An individual or organization that operates the system and manages the farm.

[1119] Data collection and transmission

[1120] The sensor means measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. For example, it measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C. The terminal means converts the collected data into JSON format and sends it to the server means using an HTTP POST request.

[1121] Data storage and analysis

[1122] The server means stores the received data in a database. For example, it inserts data into the database using an SQL query. The generative AI means performs analysis based on this stored data. The data is input into a pre-trained AI model to generate a prediction result. For example, it may determine that there is a 20% moisture deficiency, that a pH value of 7.2 is appropriate, and that a light intensity of 1,500 lux is appropriate. The server means generates insights and specific recommended actions based on the analysis results. For example, it generates an insight such as, "There is a moisture deficiency. Irrigation is recommended."

[1123] Providing insights and responding to users

[1124] The server means transmits the generated analysis results and insights to the terminal means. For example, it transmits a message in JSON format saying, "Water level is insufficient. Irrigation is recommended." The terminal means displays the received analysis results and insights to the user in a visually easy-to-understand format. For example, it displays "Water level is insufficient. Irrigation is recommended" on a dashboard.

[1125] Utilizing the Emotion Engine

[1126] The emotion engine analyzes the user's voice, facial expressions, and body movements to recognize the user's emotional state in real time. For example, if the user has an anxious expression, the emotion data is acquired. The server means adjusts insights and recommended actions based on the emotion data acquired from the emotion engine. For example, if the user is in an anxious state, the server means performs actions such as "providing detailed information to improve the situation" or "suggesting additional customization options."

[1127] Customization Options and Consulting

[1128] The server means proposes suitable customization options based on the data of a particular farm. For example, it may recommend a highly disease-resistant crop variety "X." The user can review the proposed customization options and adjust the settings. The user can select crop variety "X" from the platform's settings screen. Furthermore, the user can subscribe to consulting services as needed to receive additional advice from experts.

[1129] Specific examples

[1130] Example 1: Insufficient soil moisture

[1131] The sensor means measures the soil moisture content to be 20%, the pH value to be 7.2, the light intensity to be 1500 lux, and the temperature to be 25°C. The terminal means transmits the measurement data to the server, which stores and analyzes the data. The generation AI means generates an insight that "The amount of water is insufficient. Irrigation is recommended," which the server means transmits to the terminal means. The terminal means displays the analysis results on a dashboard for the user to confirm. The emotion engine recognizes the user's anxious expression, and the server means provides detailed guidance and additional information. The user turns on the irrigation system to replenish the water.

[1132] Example 2: Proposing customization options

[1133] The server means proposes a highly disease-resistant crop variety "X" based on data from a specific farm. The terminal means displays the proposal to the user, who confirms it. The emotion engine recognizes an interesting expression from the user, and the server means provides further detailed information. The user selects crop variety "X" from the platform's setting screen.

[1134] Prompt Sentence Examples

[1135] Examples of prompts include:

[1136] "Analyze current soil moisture, pH, light, and temperature data and recommend appropriate actions."

[1137] "If the user appears anxious, please suggest what additional information or support you can provide."

[1138] "Suggest the best crop varieties for your particular farm environment."

[1139] In this way, the system not only supports efficient and sustainable agricultural management, but also provides advanced support that takes into account the user's emotional state.

[1140] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1141] Step 1: Sensor measures data

[1142] The sensor means measures environmental data such as soil moisture content, pH value, light intensity, and temperature in real time. For example, at 9:00 AM, the sensor means measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C. These data are transmitted from the sensor means to the terminal means. The sensor means receives soil and environmental measurements as inputs and generates a set of sensor data as outputs.

[1143] Step 2: Device collects and transmits data

[1144] The terminal collects data received from the sensor means and converts it into JSON format. Specifically, it generates a JSON object with data such as soil moisture content, pH value, light intensity, and temperature as keys and values. The terminal sends this JSON data to the server means using an HTTP POST request. It receives sensor data as input, generates JSON format data as output, and sends it to the server.

[1145] Step 3: The server saves the data

[1146] The server receives the JSON data sent from the terminal means and stores it in the database using an SQL query. For example, it inserts it into the database using the SQL query "INSERT INTO sensor_data (timestamp, moisture, pH, lux, temperature) VALUES ('2023-10-01 09:00:00', 20, 7.2, 1500, 25);". It takes JSON data as input and generates stored data as output.

[1147] Step 4: The generative AI model analyzes the data

[1148] The server inputs the stored data into the generative AI model for analysis. The AI ​​model generates insights such as a 20% lack of moisture, an appropriate pH value of 7.2, and an appropriate light intensity of 1,500 lux. This allows insights such as "Irrigation is recommended" to be obtained as analysis results. It receives stored sensor data as input and generates analysis results as output.

[1149] Step 5: The server generates insights and sends them to the device

[1150] The server generates a specific insight message based on the analysis results obtained from the generative AI model. For example, it creates a message saying, "Water level is insufficient. Irrigation is recommended." This insight message is then converted back to JSON format and sent to the terminal. It receives the analysis results as input, generates an insight message as output, and sends it to the terminal.

[1151] Step 6: The device displays the insight to the user

[1152] The terminal analyzes the insight message received from the server and displays it to the user in a visually easy-to-understand format. Specifically, it displays a warning message on the dashboard saying, "Water level is insufficient. Irrigation is recommended." It receives the insight message as input and displays it to the user as output.

[1153] Step 7: The emotion engine recognizes the user's emotion

[1154] The emotion engine analyzes the user's voice, facial expressions, and body movements to recognize the user's emotional state in real time. For example, if the user has an anxious expression, it acquires that information and sends it to the server. It receives the user's voice and facial expression data as input and generates emotion data as output.

[1155] Step 8: The server adjusts the insights based on the sentiment data

[1156] The server adjusts insights and recommended actions based on the emotional data obtained from the emotion engine. If the user is anxious, it generates a message that provides specific operating procedures or additional support information. For example, it provides a detailed guide such as, "Here are the specific operating procedures for the irrigation system." It receives emotional data as input and generates adjusted recommended messages as output.

[1157] Step 9: User performs action

[1158] The user takes specific actions based on the insights and recommended actions displayed on the device, such as turning on the irrigation system to replenish soil moisture. The system takes insight messages from the device as input and actual farm management actions as output.

[1159] (Application example 2)

[1160] 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."

[1161] Factories need to realize efficient environmental monitoring and machine management, and respond quickly to unexpected machine breakdowns and environmental changes. Conventional systems collect data using sensors, but it is difficult to support real-time emotion recognition and detailed analysis of environmental conditions. They also lack the ability to effectively predict maintenance needs and propose appropriate management methods. Therefore, it is necessary to provide a system that can constantly monitor the environmental conditions in factories and provide optimal machine management and maintenance predictions while taking into account the user's emotional state.

[1162] The specific processing by the specific 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 measuring soil condition, moisture content, and light intensity; terminal means for collecting data from the sensor means and transmitting it to the server; server means for receiving data transmitted from the terminal means and storing it in a database; generation AI means for analyzing the data stored in the server means and generating insights regarding crop management, pest prediction, and optimal fertilizer use; means for transmitting the insights generated by the generation AI means to the terminal; means for displaying the insights to the user from the terminal; means for the user to adjust the farm management method based on the insights; emotion engine means for recognizing the user's emotional state and adjusting the insights and recommended actions generated by the server means; and monitoring means for constantly monitoring the environmental conditions (temperature, humidity, light, vibration) within the factory and performing appropriate machine management and maintenance prediction. This enables real-time environmental monitoring, appropriate machine management, and adjustments that take user emotions into consideration.

[1163] - "Soil conditions" refers to the physical and chemical conditions that affect crop growth, such as soil moisture, temperature, pH, and nutrient content.

[1164] "Moisture content" refers to the amount of water present in the soil or environment, and indicates the amount of water required for crop growth.

[1165] "Light intensity" refers to the amount of light that hits a certain area per unit of time, and is generally measured in lux.

[1166] "Sensor means" refers to a device for measuring soil moisture, pH value, temperature, light intensity, etc.

[1167] The "terminal means" is a device for collecting data from the sensor means and transmitting the data to the server.

[1168] The "server means" is a device that receives data sent from the terminal means, stores it in a database, and analyzes it.

[1169] A "generative AI means" is an artificial intelligence system that analyzes data stored in the server means and generates insights and predictions.

[1170] The "emotion engine means" is a device that analyzes the user's voice, facial expressions, body movements, etc., and recognizes the user's emotional state in real time.

[1171] A "monitoring means" is a device that constantly monitors the environmental conditions within a factory (temperature, humidity, light, vibration, etc.) and performs appropriate machine management and maintenance predictions.

[1172] "Insights" refers to specific indications and recommendations based on the results of data analysis that indicate future situations and necessary countermeasures.

[1173] "Customization options" refers to suggestions for achieving more optimal management and operation by adjusting options and settings to suit specific conditions.

[1174] "Platform" refers to the entire system realized by the mutual cooperation of multiple elements such as sensor means, terminal means, server means, and generating AI means.

[1175] This invention is a system for advanced environmental monitoring and machine management in factories. This system is composed of multiple elements such as sensor means, terminal means, server means, generation AI means, emotion engine means, and monitoring means.

[1176] 1. Sensor data collection

[1177] Sensor means are devices that measure environmental data such as temperature, humidity, light intensity, and vibration in real time. For example, a temperature sensor measures the temperature at each operating point in a factory, a humidity sensor measures humidity, a light sensor detects illuminance, and a vibration sensor monitors the vibration state of machines.

[1178] 2. Data transmission

[1179] The terminal means is a device that transmits data collected from the sensor means to the server means. The data is converted to JSON format and transmitted using an HTTP POST request. For example, the data transmitted may be temperature 25.5 degrees, humidity 55%, light intensity 1200 lux, and vibration 0.3 m / s².

[1180] 3. Data storage and analysis

[1181] The server means receives the data sent from the terminal means and stores it in a database. The generation AI means analyzes the stored data and generates insights. Here, an AI model is used to detect abnormalities in temperature or vibration and consider countermeasures. For example, it generates an insight such as "The temperature is too high, so the cooling system needs to be activated."

[1182] 4. Providing insights and notifying users

[1183] The server means transmits the generated insight to the terminal means, which visually presents the insight to the user, for example, by displaying "Temperature is too high. Please turn on the cooling system" on a dashboard.

[1184] 5. Leveraging Emotional Engines

[1185] The emotion engine means recognizes the emotional state of the user by detecting the user's voice, facial expression, and body movements. For example, if the user has an anxious expression, the server means acquires that data. The server means adjusts the insight based on the emotional data acquired from the emotion engine means. For example, it is possible to respond by saying, "Since the user has an anxious expression, we will provide detailed guidance."

[1186] 6. Environmental monitoring and machinery management

[1187] The monitoring tool is a device that constantly monitors the environmental conditions in the factory and performs appropriate machine management and maintenance predictions. For example, if an abnormal rise in temperature is detected, it will immediately issue a warning and recommend activation of the cooling system.

[1188] Specific examples

[1189] For example, if a temperature sensor in a factory detects that the temperature has reached 30 degrees, the data is sent to the server via the terminal means. Analysis is performed on the server side and activation of the cooling system is recommended. The generated insight is sent to the terminal and displayed on the user's dashboard as "The temperature has reached 30 degrees. Please turn on the cooling system." Also, if the emotion engine detects that the user has an anxious expression, detailed guidance is provided.

[1190] Prompt Sentence Examples

[1191] An example of a prompt used in this system is, "The current factory environment data is temperature 30°C, humidity 60%, light intensity 1000 lux, and vibration 0.4 m / s². Please perform optimal machine management and maintenance prediction based on this." By analyzing this prompt, the AI ​​can generate specific insights and actions.

[1192] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1193] Step 1:

[1194] The sensor means measures environmental data (temperature, humidity, light intensity, vibration) in the factory in real time. For example, the temperature sensor measures the temperature as 25.5 degrees, the humidity sensor measures the humidity as 55%, the light sensor measures the light intensity as 1200 lux, and the vibration sensor measures the vibration as 0.3 m / s². These data are collected and sent to the terminal means as input.

[1195] Step 2:

[1196] The terminal means converts the data collected from the sensor means into JSON format and sends it to the server. For example, the JSON format data includes { "temperature": 25.5, "humidity": 55, "light": 1200, "vibration": 0.3}. This data is sent using an HTTP POST request. The data converted into JSON format is input to the server.

[1197] Step 3:

[1198] The server means receives the data sent from the terminal means and stores it in the database. The data is inserted into the database using an SQL query. For example, the query INSERT INTO environmental_data (temperature, humidity, light, vibration) VALUES (25.5, 55, 1200, 0.3) is executed. The received data is stored as output in the database.

[1199] Step 4:

[1200] The server means analyzes the stored data using the generating AI means and generates insights. For example, a temperature of 25.5 degrees is judged to be normal, while a vibration of 0.3 m / s² is judged to be abnormal. This analysis result is the result of data calculation by the generating AI and is output as insights.

[1201] Step 5:

[1202] The server means transmits the generated insight to the terminal means. The generated insight, for example, "Vibration is high. Machine maintenance is required," is transmitted in JSON format. The transmitted insight is sent as input to the terminal.

[1203] Step 6:

[1204] The terminal means visually presents the received insight to the user, for example by displaying a message on the dashboard saying "Vibration is high. Machine maintenance required." The user is provided with visual information and maintenance instructions as output.

[1205] Step 7:

[1206] The emotion engine means detects the user's voice, facial expression, and body movement to recognize the user's emotional state. For example, if the user has an anxious expression, that data is acquired from the emotion engine means. The acquired emotion data is input to the server.

[1207] Step 8:

[1208] The server means adjusts the insight based on the emotion data acquired from the emotion engine means. For example, an action such as "provide detailed guidance" is generated based on anxious facial expression data. Based on the emotion data, the adjusted insight or additional information is generated as an output.

[1209] Step 9:

[1210] Monitoring means constantly monitor the environmental conditions within the factory and perform appropriate machine management and maintenance predictions. If an abnormality is detected, an alert is issued immediately and necessary measures are recommended. For example, a warning such as "The temperature is rising sharply. Please turn on the cooling system" is output.

[1211] Example prompt sentence:

[1212] "The current factory environment data is: temperature 30°C, humidity 60%, light intensity 1000 lux, vibration 0.4 m / s². Please use this information to perform optimal machine management and maintenance predictions."

[1213] 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.

[1214] 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.

[1215] 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.

[1216] [Fourth embodiment]

[1217] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1218] 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.

[1219] 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).

[1220] 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.

[1221] 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.

[1222] 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).

[1223] 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.

[1224] 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.

[1225] 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.

[1226] 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.

[1227] 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.

[1228] 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.

[1229] 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."

[1230] This invention relates to a smart agriculture platform that combines generative AI models and digital sensors to efficiently manage agricultural data and support future agricultural practices.

[1231] overview

[1232] The system consists of the following main components:

[1233] Sensor means: Devices for measuring soil condition, moisture content and light level.

[1234] Terminal means: A device for transmitting data collected from the sensor means to the server.

[1235] Server means: A device that receives data sent from the terminal means and stores it in a database.

[1236] Generative AI Means: An artificial intelligence system that analyzes data stored in the Server Means and generates insights for crop management, pest and disease prediction, and optimal use of fertilizer.

[1237] User: The person or organization that operates the system and manages the farm.

[1238] System program and processing explanation

[1239] Data collection

[1240] The sensor means measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. For example, it measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C.

[1241] Next, the terminal means collects the data. The collected data is converted into, for example, a JSON format and transmitted to the server means. Specifically, the data is transmitted using a data transmission protocol (for example, an HTTP POST request).

[1242] Data analysis

[1243] The server means stores the received data in a database. For example, it inserts the data into the database using an SQL query. The generation AI means analyzes this stored data. The data is input into a pre-trained AI model to generate a prediction result. For example, it may determine that there is a 20% moisture deficiency, that a pH value of 7.2 is appropriate, and that a light intensity of 1,500 lux is appropriate.

[1244] The server generates insights and specific recommended actions based on the analysis results, for example, "Water content is insufficient. Irrigation is recommended."

[1245] Providing insights

[1246] The server means transmits the generated analysis results and insights to the terminal means. For example, it transmits a message in JSON format saying, "Water level is insufficient. Irrigation is recommended." The terminal means displays the received analysis results and insights to the user in a visually easy-to-understand format. For example, it displays "Water level is insufficient. Irrigation is recommended" on a dashboard.

[1247] Real-time adjustments

[1248] Users can view analytics and insights on their device screens, scroll through dashboards to identify water deficiencies, and then adjust farm management as needed, such as turning on irrigation systems to replenish water.

[1249] Customization Options and Consulting

[1250] The server means proposes suitable customization options based on the data of a particular farm. For example, it may recommend a highly disease-resistant crop variety "X." The user can review the proposed customization options and adjust the settings. The user can select crop variety "X" from the platform's settings screen. Furthermore, the user can subscribe to consulting services as needed to receive additional advice from experts.

[1251] In this way, the smart agriculture platform of the present invention provides farmers with the data and insights they need to achieve more efficient and sustainable agricultural management.

[1252] The processing flow will be explained below.

[1253] Specific processing steps of the program

[1254] Step 1: Data collection

[1255] 1. The sensor means measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time.

[1256] Example: Measure soil moisture content at 20%, pH value at 7.2, light intensity at 1500 lux, and temperature at 25°C.

[1257] 2. The terminal collects the measured data from the sensor means.

[1258] Convert the data into a format (e.g. JSON).

[1259] Example: { "soil_moisture": 20, "pH": 7.2, "light": 1500, "temperature": 25}

[1260] 3. The device sends the collected data to the server.

[1261] Send data using a data transmission protocol (e.g., HTTP POST request).

[1262] Example: POST / data { "soil_moisture": 20, "pH": 7.2, "light": 1500, "temperature": 25}

[1263] Step 2: Save data

[1264] 1. The server stores the received data in a database.

[1265] Example: Inserting data into a database using an SQL query.

[1266] Example: INSERT INTO soil_data (moisture, pH, light, temperature) VALUES (20, 7.2, 1500, 25)

[1267] Step 3: Data analysis

[1268] 1. A generative AI model on the server analyzes the stored data.

[1269] Example: Inputting data into a pre-trained AI model to generate predictions.

[1270] The model determines that "20% moisture content is insufficient," "a pH value of 7.2 is neutral and appropriate," and "a light intensity of 1500 lux is appropriate."

[1271] 2. The server generates insights and recommended actions based on the analysis results.

[1272] For example: Generate the insight "Water level is insufficient. Irrigation recommended."

[1273] Step 4: Providing insights

[1274] 1. The server sends the generated analysis results and insights to the device.

[1275] Example: Send a message to the device in JSON format saying "Water level is low. Irrigation recommended."

[1276] 2. The device displays the received analysis results and insights to the user in a visually easy-to-understand format.

[1277] Example: Displaying "Insufficient moisture. Irrigation recommended" on the dashboard.

[1278] Step 5: Real-time adjustments

[1279] 1. The user looks at the device screen to see the analysis results and insights.

[1280] Example: Scroll through your dashboard to see if you're dehydrated.

[1281] 2. Users adjust how they manage their farms as needed.

[1282] For example: Turn on the irrigation system to replenish water.

[1283] Step 6: Customization options and consultation

[1284] 1. The server suggests suitable customization options based on the data for the specific farm.

[1285] Example: Propose a highly disease-resistant crop variety "X."

[1286] 2. The user reviews the suggested customization options and adjusts the settings.

[1287] Example: Select crop variety "X" from the platform settings screen.

[1288] 3. The user applies for consulting services as needed.

[1289] For example: Consult an expert online for additional advice.

[1290] In this way, the system supports efficient and sustainable agricultural management.

[1291] Example 1

[1292] 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."

[1293] Conventional agricultural management systems face challenges in efficiently collecting data from the entire farm and analyzing it in real time, making it difficult to perform appropriate crop management, predict pests and diseases, and optimize fertilizer use. They also face the problem of being unable to propose customized options suited to specific farms and adjust management methods accordingly.

[1294] 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.

[1295] In this invention, the server includes a sensor device that measures soil condition, moisture content, and light intensity; a terminal device that collects data from the sensor device and transmits it to the server; a server device that receives the data transmitted from the terminal device and stores it in a database; a generating AI device that analyzes the data stored in the server device and generates insights regarding crop management, pest and disease prediction, and optimal fertilizer use; a device that transmits the insights generated by the generating AI device to the terminal device; a device that displays the insights to a user from the terminal device; and a device that allows the user to adjust farm management methods based on the insights. This enables real-time data collection and analysis, appropriate crop management, pest and disease prediction, and optimal fertilizer use. It also enables the system to quickly and effectively propose customized options suitable for specific farms and adjust management methods based on those options.

[1296] A "sensor device" is a device for measuring soil conditions, moisture content, light intensity, and the like.

[1297] A "terminal device" is a device for transmitting data collected from a sensor device to a server.

[1298] A "server device" is a device that receives data sent from a terminal device and stores it in a database.

[1299] The "generative AI device" is an artificial intelligence system that analyzes data stored on a server device and generates insights into crop management, pest and disease prediction, and optimal fertilizer use.

[1300] A "display device" is a device for visually displaying insights from a terminal device to a user.

[1301] A "user" is an individual or organization that adjusts how they manage their farm based on the insights generated.

[1302] "Customization options" are options that suggest adjustments and settings that are suitable for a particular farm.

[1303] "Real-time adjustment" is the process by which users instantly change how they manage their farm based on the insights generated.

[1304] System Overview

[1305] The present invention relates to a smart agriculture platform that combines generative AI models and digital sensors to efficiently manage agricultural data and support future agricultural practices.

[1306] The system consists of the following main components:

[1307] Sensor devices: devices for measuring soil condition, moisture content and light intensity.

[1308] Terminal device: A device for transmitting data collected from a sensor device to a server.

[1309] Server device: A device that receives data sent from a terminal device and stores it in a database.

[1310] Generative AI device: An artificial intelligence system that analyzes data stored on a server device and generates insights for crop management, pest and disease prediction, and optimal fertilizer use.

[1311] Display device: A device for transmitting the insights generated by the generative AI device to a terminal device and visually displaying them to the user.

[1312] User: The person or organization that operates the system and manages the farm.

[1313] Examples of data collection

[1314] The sensor device measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. For example, it measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C. Next, the terminal device collects this data. The collected data is converted into, for example, JSON format and sent to the server device. Specifically, the data is sent using a data transmission protocol (for example, an HTTP POST request).

[1315] Specific examples of data analysis

[1316] The server device stores the received data in a database. For example, it inserts data into the database using an SQL query. The generative AI device performs analysis based on this stored data. The data is input into a pre-trained AI model to generate a prediction result. For example, it may determine that there is a 20% moisture deficiency, that a pH value of 7.2 is appropriate, and that a light intensity of 1,500 lux is appropriate. The server device generates insights and specific recommended actions based on the analysis results. For example, it may generate an insight such as, "There is a moisture deficiency. Irrigation is recommended."

[1317] Providing insights and real-time adjustments

[1318] The server device sends the generated analysis results and insights to the terminal device. For example, it sends a message in JSON format saying, "Water level is insufficient. Irrigation recommended." The terminal device displays the received analysis results and insights to the user in a visually easy-to-understand format. For example, it displays "Water level is insufficient. Irrigation recommended" on a dashboard.

[1319] Users can view analytics and insights on their device screens, scroll through dashboards to identify water deficiencies, and then adjust farm management as needed, such as switching on irrigation systems to replenish water.

[1320] Customization options and consulting

[1321] The server device proposes suitable customization options based on the data of a specific farm. For example, it may recommend a highly disease-resistant crop variety "X." The user can review the proposed customization options and adjust the settings. They can select crop variety "X" from the platform's settings screen. Furthermore, users can request consulting services to receive additional advice from experts as needed.

[1322] Prompt Sentence Examples

[1323] For example, if the following data is measured by a sensor device on a farm:

[1324] Land Data:

[1325] Soil moisture content: 15%

[1326] pH value: 6.8

[1327] Light output: 1300 lux

[1328] Temperature: 22℃

[1329] Based on this data, we input the following prompt to the generative AI model:

[1330] Based on this data, what are your recommended actions for crop management?

[1331] In this way, the smart agriculture platform of the present invention provides farmers with the data and insights they need to achieve more efficient and sustainable agricultural management.

[1332] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1333] Step 1:

[1334] The sensor device measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. Specifically, it measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C. This is used as input data.

[1335] Step 2:

[1336] The terminal device collects measurement data from the sensor device. The collected data is converted into JSON format and sent to the server. Specifically, the JSON format data is sent to the server using a data transmission protocol (for example, an HTTP POST request). This allows the server to receive the data collected from the sensor.

[1337] Step 3:

[1338] The server stores the received data in a database. Specifically, it inserts data into the database using an SQL query. It uses the received JSON data as input and obtains the state stored in the database as output.

[1339] Step 4:

[1340] The generative AI device analyzes the data stored in the database. The data is input into a pre-trained AI model to generate a predicted result. Specifically, it determines that there is a 20% moisture deficiency, that a pH value of 7.2 is appropriate, and that a light intensity of 1500 lux is appropriate. The data in the database is used as input data, and a predicted result is obtained as output.

[1341] Step 5:

[1342] The server generates insights based on the analysis results. Specifically, it generates a message based on the analysis results saying, "Water level is insufficient. Irrigation is recommended." It uses the prediction results as input data and obtains the generated insights as output.

[1343] Step 6:

[1344] The server sends the generated insight to the terminal device. Specifically, it sends a message in JSON format saying, "Water level is insufficient. Irrigation is recommended." The terminal device then receives the insight.

[1345] Step 7:

[1346] The terminal device displays the received insight to the user. Specifically, it displays "Water level is low. Irrigation recommended" on the dashboard. This allows the user to visually confirm the insight.

[1347] Step 8:

[1348] The user looks at the screen of their device to see the analysis results and insights. Specifically, they scroll through the dashboard to identify water deficiencies. If necessary, they adjust their farm management, for example, by switching on the irrigation system to replenish water. This ensures that the farm is properly managed.

[1349] Step 9:

[1350] The server then proposes appropriate customization options based on the data of a specific farm. Specifically, it generates an option such as "We suggest a highly disease-resistant crop variety 'X'." This allows the user to have useful choices.

[1351] Step 10:

[1352] The user reviews and applies the proposed customization options, specifically by selecting crop variety "X" in the platform's settings screen, thereby optimizing farm management.

[1353] (Application example 1)

[1354] 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."

[1355] Traditional agricultural management systems often lack the ability to collect, analyze, and provide insights in real time. This can make it difficult for farmers to respond quickly and appropriately, potentially resulting in reduced production efficiency and quality. Similar issues arise in factory environments, where effective management of temperature, humidity, light levels, and machine status data is required.

[1356] 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.

[1357] In this invention, the server comprises device means for measuring soil conditions, moisture content, and light intensity, terminal device means for collecting data from the device means and transmitting it to the server, storage means for receiving data transmitted from the terminal device means and storing it in a database, generation AI system means for analyzing the data stored in the storage means and generating insights regarding crop management, pest prediction, and optimal use of fertilizer, communication means for transmitting the insights generated by the generation AI system means to a terminal, visual display means for displaying the insights from the terminal to a user, management means for allowing the user to adjust the farm management method based on the insights, and temperature, humidity, and other factors of the factory environment. The system includes a device means for measuring temperature and light levels and collecting machine status data, a terminal device means for collecting data from the device means and transmitting it to a server, a storage device means for receiving the data transmitted from the terminal device means and storing it in a database, a generative AI system means for analyzing the data stored in the storage device means and generating insights for optimizing the production process, activating a cooling system, and reducing machine load, a communication means for transmitting the insights generated by the generative AI system means to a terminal, a visual display means for displaying the insights to a worker from the terminal, and a management means for the worker to adjust the production process based on the insights. This enables real-time data collection, analysis, and provision of insights in agricultural and factory environments, enabling prompt and appropriate responses.

[1358] "Soil condition" refers to the physical, chemical, and biological characteristics of the surface and subsurface of agricultural land.

[1359] "Moisture content" refers to the percentage of water contained in a particular area or substance.

[1360] "Light intensity" refers to the intensity or amount of light measured at a particular location.

[1361] "Device means" refers to the measuring instruments or equipment used to collect specific data.

[1362] "Terminal device means" refers to a communication device for collecting data and transmitting it to other devices or servers.

[1363] "Storage means" refers to a database or storage device for storing collected data.

[1364] "Generative AI system means" refers to a system that uses a pre-trained artificial intelligence model to analyze data and generate insights and predictions.

[1365] "Communications means" refers to technologies and devices for sending and receiving data and information.

[1366] "Visual display means" refers to devices or techniques for visually displaying data or insights to a user.

[1367] "Control measures" refers to methods and means for adjusting and controlling the way farms or factories are run in real time based on said insights.

[1368] "Machine status" refers to data indicating the state of a particular machine, such as whether it is running, stopped, or overloaded.

[1369] A "production process" refers to the series of operations or steps taken to produce a particular product or service.

[1370] "Cooling system" means a system or device for reducing the temperature of an environment or machine.

[1371] "Machine load" refers to the amount of work or workload being processed by a particular machine.

[1372] The embodiment of the present invention will be specifically described as follows: The system of the present invention is mainly composed of a device means, a terminal device means, a storage means, a generation AI system means, a communication means, a visual display means, and a management means.

[1373] System configuration and operation

[1374] Device Means

[1375] The device means is for measuring environmental data of a farm or factory in real time, for example, in the case of a farm it may include sensors measuring soil condition, moisture content, and light level, and in the case of a factory it may include sensors measuring temperature, humidity, light level, and machine status.

[1376] terminal device means

[1377] The end device is responsible for transmitting the data collected from the sensors to the server. This can be a communication device such as a smartphone or a head-mounted display. The data is converted to JSON format, for example, and sent to the server using an HTTP POST request.

[1378] storage means

[1379] The server receives the data sent from the terminal device and stores it in a database, using a database management system such as SQLite, where data is stored through SQL queries.

[1380] Generative AI system means

[1381] The data stored on the server is analyzed by pre-trained generative AI models, built using frameworks such as Scikit-Learn and TensorFlow, which generate insights for crop management, pest and disease prediction, optimal fertilizer use, optimizing production processes, activating cooling systems, and reducing machine loads.

[1382] communication means

[1383] The insights generated by the generative AI system are sent to the terminal via a communication means, again using the HTTP protocol, and the insight data is typically sent in JSON format.

[1384] Visual display means

[1385] The device is responsible for visually displaying the received insights, allowing users to review them through interfaces such as dashboards and graphs.

[1386] management measures

[1387] Based on the displayed insights, users can adjust management measures, such as activating irrigation systems or adjusting machine loads. For example, if temperatures in a factory become too high, cooling systems can be activated immediately. Production processes can also be optimized and machine loads adjusted as needed.

[1388] Adding specific examples

[1389] For example, if the temperature in a factory reaches 35°C, the sensor means measures this and the terminal device means transmits the data to the server. The server stores the data in a database, and the generating AI system means analyzes that the temperature is too high and generates an insight recommending the activation of a cooling system. This insight is transmitted to the terminal via the communication means, and an operator can view it via a head-mounted display.

[1390] Examples of prompts:

[1391] “If the temperature in the factory reaches 35°C, the humidity is 70%, the light level is 5000 lux, and the machine status is ‘overloaded,’ use a generative AI model to analyze the data and suggest appropriate countermeasures.”

[1392] Thus, the present invention provides the data and insights needed to achieve efficient and sustainable management in agricultural and industrial environments.

[1393] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1394] Step 1:

[1395] The device means measures the environmental data in real time. For example, the temperature in the factory is 35°C, humidity is 70%, light level is 5000 lux, and machine status is "overloaded". This is the input data, and the collected data is converted into JSON format. The output of this step is the environmental data in JSON format.

[1396] Step 2:

[1397] The terminal device means receives the data collected from the device means and sends it to the server. Specifically, JSON format data is sent to the server using an HTTP POST request. The input is the JSON data generated in step 1, and the output is a success response to the data transmission to the server.

[1398] Step 3:

[1399] The server receives the JSON data sent from the terminal device means and stores it in a database. Here, a database management system such as SQLite is used to store the data through SQL queries. The input is the received JSON data, and the output is the data stored in the database.

[1400] Step 4:

[1401] The server inputs the stored data into the generative AI system means for analysis. Specifically, a pre-trained generative AI model using Scikit-Learn or TensorFlow analyzes this data. For example, it determines that a temperature of 35°C is too high and generates an insight recommending the activation of a cooling system. The input is environmental data stored in the database, and the output is the insight resulting from the analysis.

[1402] Step 5:

[1403] The server sends the insights generated by the generation AI system means to the terminal via a communication means. Here, too, the analysis results are sent in JSON format using the HTTP protocol. The input is the insights as the analysis results, and the output is the insight data sent to the terminal.

[1404] Step 6:

[1405] The device presents the received insights to the user through a visual display. For example, a dashboard display such as "The temperature is too high. We recommend activating the cooling system" may be displayed on a smartphone or head-mounted display. The input is the insight data sent from the server, and the output is a visual display that the user can check.

[1406] Step 7:

[1407] Based on the visually displayed insights, the user can use management tools to take appropriate measures, for example, to activate the cooling system, which will reduce the temperature in the factory and eliminate the "overload" state of the machines. The input is the visually displayed insight, and the output is the action taken by the user.

[1408] 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.

[1409] This invention relates to a smart agriculture platform that combines generative AI models with digital sensors and a user emotion engine, with the aim of efficiently managing agricultural data and supporting future agricultural practices.

[1410] overview

[1411] The system consists of the following main components:

[1412] Sensor means: Devices for measuring soil condition, moisture content and light level.

[1413] Terminal means: A device for transmitting data collected from the sensor means to the server.

[1414] Server means: A device that receives data sent from the terminal means and stores it in a database.

[1415] Generative AI Means: An artificial intelligence system that analyzes data stored in the Server Means and generates insights for crop management, pest and disease prediction, and optimal use of fertilizer.

[1416] Emotion engine: A device that recognizes the user's emotional state.

[1417] User: The person or organization that operates the system and manages the farm.

[1418] System program and processing explanation

[1419] Data collection and transmission

[1420] The sensor means measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. For example, it measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C.

[1421] The terminal collects the measured data from the sensor means. The collected data is converted into, for example, JSON format and transmitted to the server means. Specifically, the data is transmitted using a data transmission protocol (for example, an HTTP POST request).

[1422] Data storage and analysis

[1423] The server means stores the received data in a database. For example, it inserts the data into the database using an SQL query. The generation AI means analyzes this stored data. The data is input into a pre-trained AI model to generate a prediction result. For example, it may determine that there is a 20% moisture deficiency, that a pH value of 7.2 is appropriate, and that a light intensity of 1,500 lux is appropriate.

[1424] The server generates insights and specific recommended actions based on the analysis results, for example, "Water content is insufficient. Irrigation is recommended."

[1425] Providing insights and responding to users

[1426] The server means transmits the generated analysis results and insights to the terminal means. For example, it transmits a message in JSON format saying, "Water level is insufficient. Irrigation is recommended." The terminal means displays the received analysis results and insights to the user in a visually easy-to-understand format. For example, it displays "Water level is insufficient. Irrigation is recommended" on a dashboard.

[1427] Utilizing the Emotion Engine

[1428] The emotion engine analyzes the user's voice, facial expressions, and body movements to recognize the user's emotional state in real time. For example, if the user has an anxious expression, it will capture that emotional data.

[1429] The server means adjusts insights and recommended actions based on the emotion data obtained from the emotion engine. For example, if the user is in an anxious state, the server means may provide more information to improve the situation or suggest additional customization options.

[1430] Customization Options and Consulting

[1431] The server means proposes suitable customization options based on the data of a particular farm. For example, it may recommend a highly disease-resistant crop variety "X." The user can review the proposed customization options and adjust the settings. The user can select crop variety "X" from the platform's settings screen. Furthermore, the user can subscribe to consulting services as needed to receive additional advice from experts.

[1432] Specific examples

[1433] Example 1: Insufficient soil moisture

[1434] 1. The sensor measures soil moisture at 20%, pH at 7.2, light intensity at 1500 lux, and temperature at 25°C.

[1435] 2. The terminal transmits the measurement data to the server, which stores and analyzes the data.

[1436] 3. The generating AI means generates an insight that "There is insufficient moisture. Irrigation is recommended.", and the server means transmits this to the terminal means.

[1437] 4. The terminal means displays the analysis results on a dashboard for the user to confirm.

[1438] 5. The emotion engine recognizes the user's anxious facial expression, and the server means provides detailed guidance and additional information.

[1439] 6. The user turns on the irrigation system to replenish the water.

[1440] Example 2: Proposing customization options

[1441] 1. The server means suggests a highly disease-resistant crop variety "X" based on data from a particular farm.

[1442] 2. The terminal means displays the proposed content to the user, who then confirms it.

[1443] 3. The emotion engine recognizes the user's interesting facial expressions and the server means provides further detailed information.

[1444] 4. The user selects crop variety "X" from the platform settings screen.

[1445] In this way, the system not only supports efficient and sustainable agricultural management, but also provides advanced support that takes into account the user's emotional state.

[1446] The processing flow will be explained below.

[1447] Specific processing steps of the program

[1448] Step 1: Data collection

[1449] 1. The sensor means measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time.

[1450] Example: Measure soil moisture content at 20%, pH value at 7.2, light intensity at 1500 lux, and temperature at 25°C.

[1451] 2. The terminal collects the measured data from the sensor means.

[1452] Convert the data into a format (e.g. JSON).

[1453] Example: { "soil_moisture": 20, "pH": 7.2, "light": 1500, "temperature": 25}

[1454] 3. The device sends the collected data to the server.

[1455] Send data using a data transmission protocol (e.g., HTTP POST request).

[1456] Example: POST / data { "soil_moisture": 20, "pH": 7.2, "light": 1500, "temperature": 25}

[1457] Step 2: Save data

[1458] 1. The server stores the received data in a database.

[1459] For example, inserting data into a database using an SQL query.

[1460] Example: INSERT INTO soil_data (moisture, pH, light, temperature) VALUES (20, 7.2, 1500, 25)

[1461] Step 3: Data analysis

[1462] 1. A generative AI model on the server analyzes the stored data.

[1463] For example, data is input into a pre-trained AI model to generate prediction results.

[1464] The generated AI model determines that "20% moisture content is insufficient," "pH value of 7.2 is neutral and appropriate," and "light intensity of 1500 lux is appropriate."

[1465] 2. The server generates insights and recommended actions based on the analysis results.

[1466] For example, generating an insight such as "Water level is insufficient. Irrigation recommended."

[1467] Step 4: Providing insights

[1468] 1. The server sends the generated analysis results and insights to the device.

[1469] Example: Send a message to the device in JSON format saying "Water level is low. Irrigation recommended."

[1470] 2. The device displays the received analysis results and insights to the user in a visually easy-to-understand format.

[1471] Example: Displaying "Insufficient moisture. Irrigation recommended" on the dashboard.

[1472] Step 5: Real-time adjustments

[1473] 1. The user looks at the device screen to see the analysis results and insights.

[1474] Example: Scroll through your dashboard to see if you're dehydrated.

[1475] 2. Users adjust how they manage their farms as needed.

[1476] For example: Turn on the irrigation system to replenish water.

[1477] Step 6: Leverage your emotional engine

[1478] 1. The emotion engine recognizes the user's emotional state from their voice, facial expressions, and body movements.

[1479] Example: If the user has an anxious expression, obtain the emotion data.

[1480] 2. The server adjusts insights and recommended actions based on the emotion data obtained from the emotion engine.

[1481] Example: If the user is in an anxious state, generate an action such as "provide more information to remedy the situation" or "suggest additional customization options."

[1482] 3. The server sends tailored insights and recommended actions to the device.

[1483] Example: Send the message "We will introduce detailed irrigation methods" in JSON format to the terminal.

[1484] 4. The device displays tailored insights and recommended actions to the user in a visually understandable format.

[1485] Example: Display "Detailed irrigation methods" on the dashboard.

[1486] Step 7: Customization options and consultation

[1487] 1. The server suggests suitable customization options based on the data for the specific farm.

[1488] Example: Propose a highly disease-resistant crop variety "X."

[1489] 2. The device displays the suggestions to the user.

[1490] Example: Displaying "We recommend the highly disease-resistant crop variety 'X'" on the dashboard.

[1491] 3. The user reviews the suggested customization options and adjusts the settings.

[1492] Example: Select crop variety "X" from the platform settings screen.

[1493] 4. The user applies for consulting services as needed.

[1494] For example: Consult an expert online for additional advice.

[1495] Through the above processing steps, the system supports efficient agricultural management and sustainable production, and also provides advanced support that takes into account the user's emotional state.

[1496] Example 2

[1497] 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."

[1498] Modern agriculture requires real-time monitoring of soil and environmental conditions, and prompt and appropriate farm management based on that monitoring. However, while conventional systems can collect and analyze sensor data, they lack the ability to provide specific action instructions based on the analysis results or support that takes into account the user's emotional state. Therefore, a system is needed that allows users to manage farms efficiently and sustainably without feeling any emotional burden.

[1499] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1500] In this invention, the server includes an emotion engine that recognizes the emotional state of the user, means for adjusting insights and recommended actions based on data obtained from the emotion engine, and means for transmitting the insights generated by the generating AI means to a terminal, thereby providing farm management advice optimized according to the user's emotional state, enabling the user to manage the farm efficiently and with reduced emotional burden.

[1501] The "sensor means" is a device for measuring soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time.

[1502] The "terminal means" is a device for transmitting data collected from the sensor means to the server means.

[1503] The "server means" is a device that receives data sent from the terminal means, stores the data in a database, and analyzes the data.

[1504] The "generative AI means" is an artificial intelligence system for analyzing data stored in the server means and generating insights regarding crop management, pest and disease prediction, and optimal use of fertilizer.

[1505] An "emotion engine" is a device that analyzes the user's voice, facial expressions, and body movements to recognize the user's emotional state in real time.

[1506] "Insights" are specific pieces of advice or recommended actions created by generative AI means to help users manage their farms.

[1507] "User" means an individual or organization that operates the system and manages the farm.

[1508] "Customization options" are options that suggest crop varieties, management methods, and other options that are best suited to a particular farm.

[1509] This invention relates to a smart agriculture platform that combines generative AI models with digital sensors and a user emotion engine, with the aim of efficiently managing agricultural data and supporting future agricultural practices.

[1510] overview

[1511] The system consists of the following main components:

[1512] Sensor means: A device for measuring soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. Examples of sensor means include soil moisture sensors, pH sensors, light sensors, and temperature sensors.

[1513] Terminal means: A device that transmits data collected from the sensor means to the server. Specifically, a gateway, smartphone, tablet, etc. are used.

[1514] Server means: A device that receives data sent from the terminal means and stores it in a database. For example, a cloud server or an on-premise server is used.

[1515] Generative AI means: An artificial intelligence system that analyzes data stored in the server means and generates insights on crop management, pest and disease prediction, and optimal fertilizer use. Deep learning models and machine learning algorithms are used as generative AI models.

[1516] Emotion engine: A device that analyzes and recognizes the user's emotional state (voice, facial expressions, and body movements) in real time. Specifically, a voice recognition system and a face recognition system are used.

[1517] User: An individual or organization that operates the system and manages the farm.

[1518] Data collection and transmission

[1519] The sensor means measures soil conditions (moisture content, pH value) and environmental conditions (light intensity, temperature) in real time. For example, it measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C. The terminal means converts the collected data into JSON format and sends it to the server means using an HTTP POST request.

[1520] Data storage and analysis

[1521] The server means stores the received data in a database. For example, it inserts data into the database using an SQL query. The generative AI means performs analysis based on this stored data. The data is input into a pre-trained AI model to generate a prediction result. For example, it may determine that there is a 20% moisture deficiency, that a pH value of 7.2 is appropriate, and that a light intensity of 1,500 lux is appropriate. The server means generates insights and specific recommended actions based on the analysis results. For example, it generates an insight such as, "There is a moisture deficiency. Irrigation is recommended."

[1522] Providing insights and responding to users

[1523] The server means transmits the generated analysis results and insights to the terminal means. For example, it transmits a message in JSON format saying, "Water level is insufficient. Irrigation is recommended." The terminal means displays the received analysis results and insights to the user in a visually easy-to-understand format. For example, it displays "Water level is insufficient. Irrigation is recommended" on a dashboard.

[1524] Utilizing the Emotion Engine

[1525] The emotion engine analyzes the user's voice, facial expressions, and body movements to recognize the user's emotional state in real time. For example, if the user has an anxious expression, the emotion data is acquired. The server means adjusts insights and recommended actions based on the emotion data acquired from the emotion engine. For example, if the user is in an anxious state, the server means performs actions such as "providing detailed information to improve the situation" or "suggesting additional customization options."

[1526] Customization Options and Consulting

[1527] The server means proposes suitable customization options based on the data of a particular farm. For example, it may recommend a highly disease-resistant crop variety "X." The user can review the proposed customization options and adjust the settings. The user can select crop variety "X" from the platform's settings screen. Furthermore, the user can subscribe to consulting services as needed to receive additional advice from experts.

[1528] Specific examples

[1529] Example 1: Insufficient soil moisture

[1530] The sensor means measures the soil moisture content to be 20%, the pH value to be 7.2, the light intensity to be 1500 lux, and the temperature to be 25°C. The terminal means transmits the measurement data to the server, which stores and analyzes the data. The generation AI means generates an insight that "The amount of water is insufficient. Irrigation is recommended," which the server means transmits to the terminal means. The terminal means displays the analysis results on a dashboard for the user to confirm. The emotion engine recognizes the user's anxious expression, and the server means provides detailed guidance and additional information. The user turns on the irrigation system to replenish the water.

[1531] Example 2: Proposing customization options

[1532] The server means proposes a highly disease-resistant crop variety "X" based on data from a specific farm. The terminal means displays the proposal to the user, who confirms it. The emotion engine recognizes an interesting expression from the user, and the server means provides further detailed information. The user selects crop variety "X" from the platform's setting screen.

[1533] Prompt Sentence Examples

[1534] Examples of prompts include:

[1535] "Analyze current soil moisture, pH, light, and temperature data and recommend appropriate actions."

[1536] "If the user appears anxious, please suggest what additional information or support you can provide."

[1537] "Suggest the best crop varieties for your particular farm environment."

[1538] In this way, the system not only supports efficient and sustainable agricultural management, but also provides advanced support that takes into account the user's emotional state.

[1539] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1540] Step 1: Sensor measures data

[1541] The sensor means measures environmental data such as soil moisture content, pH value, light intensity, and temperature in real time. For example, at 9:00 AM, the sensor means measures that the soil moisture content is 20%, the pH value is 7.2, the light intensity is 1500 lux, and the temperature is 25°C. These data are transmitted from the sensor means to the terminal means. The sensor means receives soil and environmental measurements as inputs and generates a set of sensor data as outputs.

[1542] Step 2: Device collects and transmits data

[1543] The terminal collects data received from the sensor means and converts it into JSON format. Specifically, it generates a JSON object with data such as soil moisture content, pH value, light intensity, and temperature as keys and values. The terminal sends this JSON data to the server means using an HTTP POST request. It receives sensor data as input, generates JSON format data as output, and sends it to the server.

[1544] Step 3: The server saves the data

[1545] The server receives the JSON data sent from the terminal means and stores it in the database using an SQL query. For example, it inserts it into the database using the SQL query "INSERT INTO sensor_data (timestamp, moisture, pH, lux, temperature) VALUES ('2023-10-01 09:00:00', 20, 7.2, 1500, 25);". It takes JSON data as input and generates stored data as output.

[1546] Step 4: The generative AI model analyzes the data

[1547] The server inputs the stored data into the generative AI model for analysis. The AI ​​model generates insights such as a 20% lack of moisture, an appropriate pH value of 7.2, and an appropriate light intensity of 1,500 lux. This allows insights such as "Irrigation is recommended" to be obtained as analysis results. It receives stored sensor data as input and generates analysis results as output.

[1548] Step 5: The server generates insights and sends them to the device

[1549] The server generates a specific insight message based on the analysis results obtained from the generative AI model. For example, it creates a message saying, "Water level is insufficient. Irrigation is recommended." This insight message is then converted back to JSON format and sent to the terminal. It receives the analysis results as input, generates an insight message as output, and sends it to the terminal.

[1550] Step 6: The device displays the insight to the user

[1551] The terminal analyzes the insight message received from the server and displays it to the user in a visually easy-to-understand format. Specifically, it displays a warning message on the dashboard saying, "Water level is insufficient. Irrigation is recommended." It receives the insight message as input and displays it to the user as output.

[1552] Step 7: The emotion engine recognizes the user's emotion

[1553] The emotion engine analyzes the user's voice, facial expressions, and body movements to recognize the user's emotional state in real time. For example, if the user has an anxious expression, it acquires that information and sends it to the server. It receives the user's voice and facial expression data as input and generates emotion data as output.

[1554] Step 8: The server adjusts the insights based on the sentiment data

[1555] The server adjusts insights and recommended actions based on the emotional data obtained from the emotion engine. If the user is anxious, it generates a message that provides specific operating procedures or additional support information. For example, it provides a detailed guide such as, "Here are the specific operating procedures for the irrigation system." It receives emotional data as input and generates adjusted recommended messages as output.

[1556] Step 9: User performs action

[1557] The user takes specific actions based on the insights and recommended actions displayed on the device, such as turning on the irrigation system to replenish soil moisture. The system takes insight messages from the device as input and actual farm management actions as output.

[1558] (Application example 2)

[1559] 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."

[1560] Factories need to realize efficient environmental monitoring and machine management, and respond quickly to unexpected machine breakdowns and environmental changes. Conventional systems collect data using sensors, but it is difficult to support real-time emotion recognition and detailed analysis of environmental conditions. They also lack the ability to effectively predict maintenance needs and propose appropriate management methods. Therefore, it is necessary to provide a system that can constantly monitor the environmental conditions in factories and provide optimal machine management and maintenance predictions while taking into account the user's emotional state.

[1561] The specific processing by the specific 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 measuring soil condition, moisture content, and light intensity; terminal means for collecting data from the sensor means and transmitting it to the server; server means for receiving data transmitted from the terminal means and storing it in a database; generation AI means for analyzing the data stored in the server means and generating insights regarding crop management, pest prediction, and optimal fertilizer use; means for transmitting the insights generated by the generation AI means to the terminal; means for displaying the insights to the user from the terminal; means for the user to adjust the farm management method based on the insights; emotion engine means for recognizing the user's emotional state and adjusting the insights and recommended actions generated by the server means; and monitoring means for constantly monitoring the environmental conditions (temperature, humidity, light, vibration) within the factory and performing appropriate machine management and maintenance prediction. This enables real-time environmental monitoring, appropriate machine management, and adjustments that take user emotions into consideration.

[1562] - "Soil conditions" refers to the physical and chemical conditions that affect crop growth, such as soil moisture, temperature, pH, and nutrient content.

[1563] "Moisture content" refers to the amount of water present in the soil or environment, and indicates the amount of water required for crop growth.

[1564] "Light intensity" refers to the amount of light that hits a certain area per unit of time, and is generally measured in lux.

[1565] "Sensor means" refers to a device for measuring soil moisture, pH value, temperature, light intensity, etc.

[1566] The "terminal means" is a device for collecting data from the sensor means and transmitting the data to the server.

[1567] The "server means" is a device that receives data sent from the terminal means, stores it in a database, and analyzes it.

[1568] A "generative AI means" is an artificial intelligence system that analyzes data stored in the server means and generates insights and predictions.

[1569] The "emotion engine means" is a device that analyzes the user's voice, facial expressions, body movements, etc., and recognizes the user's emotional state in real time.

[1570] A "monitoring means" is a device that constantly monitors the environmental conditions within a factory (temperature, humidity, light, vibration, etc.) and performs appropriate machine management and maintenance predictions.

[1571] "Insights" refers to specific indications and recommendations based on the results of data analysis that indicate future situations and necessary countermeasures.

[1572] "Customization options" refers to suggestions for achieving more optimal management and operation by adjusting options and settings to suit specific conditions.

[1573] "Platform" refers to the entire system realized by the mutual cooperation of multiple elements such as sensor means, terminal means, server means, and generating AI means.

[1574] This invention is a system for advanced environmental monitoring and machine management in factories. This system is composed of multiple elements such as sensor means, terminal means, server means, generation AI means, emotion engine means, and monitoring means.

[1575] 1. Sensor data collection

[1576] Sensor means are devices that measure environmental data such as temperature, humidity, light intensity, and vibration in real time. For example, a temperature sensor measures the temperature at each operating point in a factory, a humidity sensor measures humidity, a light sensor detects illuminance, and a vibration sensor monitors the vibration state of machines.

[1577] 2. Data transmission

[1578] The terminal means is a device that transmits data collected from the sensor means to the server means. The data is converted to JSON format and transmitted using an HTTP POST request. For example, the data transmitted may be temperature 25.5 degrees, humidity 55%, light intensity 1200 lux, and vibration 0.3 m / s².

[1579] 3. Data storage and analysis

[1580] The server means receives the data sent from the terminal means and stores it in a database. The generation AI means analyzes the stored data and generates insights. Here, an AI model is used to detect abnormalities in temperature or vibration and consider countermeasures. For example, it generates an insight such as "The temperature is too high, so the cooling system needs to be activated."

[1581] 4. Providing insights and notifying users

[1582] The server means transmits the generated insight to the terminal means, which visually presents the insight to the user, for example, by displaying "Temperature is too high. Please turn on the cooling system" on a dashboard.

[1583] 5. Leveraging Emotional Engines

[1584] The emotion engine means recognizes the emotional state of the user by detecting the user's voice, facial expression, and body movements. For example, if the user has an anxious expression, the server means acquires that data. The server means adjusts the insight based on the emotional data acquired from the emotion engine means. For example, it is possible to respond by saying, "Since the user has an anxious expression, we will provide detailed guidance."

[1585] 6. Environmental monitoring and machinery management

[1586] The monitoring tool is a device that constantly monitors the environmental conditions in the factory and performs appropriate machine management and maintenance predictions. For example, if an abnormal rise in temperature is detected, it will immediately issue a warning and recommend activation of the cooling system.

[1587] Specific examples

[1588] For example, if a temperature sensor in a factory detects that the temperature has reached 30 degrees, the data is sent to the server via the terminal means. Analysis is performed on the server side and activation of the cooling system is recommended. The generated insight is sent to the terminal and displayed on the user's dashboard as "The temperature has reached 30 degrees. Please turn on the cooling system." Also, if the emotion engine detects that the user has an anxious expression, detailed guidance is provided.

[1589] Prompt Sentence Examples

[1590] An example of a prompt used in this system is, "The current factory environment data is temperature 30°C, humidity 60%, light intensity 1000 lux, and vibration 0.4 m / s². Please perform optimal machine management and maintenance prediction based on this." By analyzing this prompt, the AI ​​can generate specific insights and actions.

[1591] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1592] Step 1:

[1593] The sensor means measures environmental data (temperature, humidity, light intensity, vibration) in the factory in real time. For example, the temperature sensor measures the temperature as 25.5 degrees, the humidity sensor measures the humidity as 55%, the light sensor measures the light intensity as 1200 lux, and the vibration sensor measures the vibration as 0.3 m / s². These data are collected and sent to the terminal means as input.

[1594] Step 2:

[1595] The terminal means converts the data collected from the sensor means into JSON format and sends it to the server. For example, the JSON format data includes { "temperature": 25.5, "humidity": 55, "light": 1200, "vibration": 0.3}. This data is sent using an HTTP POST request. The data converted into JSON format is input to the server.

[1596] Step 3:

[1597] The server means receives the data sent from the terminal means and stores it in the database. The data is inserted into the database using an SQL query. For example, the query INSERT INTO environmental_data (temperature, humidity, light, vibration) VALUES (25.5, 55, 1200, 0.3) is executed. The received data is stored as output in the database.

[1598] Step 4:

[1599] The server means analyzes the stored data using the generating AI means and generates insights. For example, a temperature of 25.5 degrees is judged to be normal, while a vibration of 0.3 m / s² is judged to be abnormal. This analysis result is the result of data calculation by the generating AI and is output as insights.

[1600] Step 5:

[1601] The server means transmits the generated insight to the terminal means. The generated insight, for example, "Vibration is high. Machine maintenance is required," is transmitted in JSON format. The transmitted insight is sent as input to the terminal.

[1602] Step 6:

[1603] The terminal means visually presents the received insight to the user, for example by displaying a message on the dashboard saying "Vibration is high. Machine maintenance required." The user is provided with visual information and maintenance instructions as output.

[1604] Step 7:

[1605] The emotion engine means detects the user's voice, facial expression, and body movement to recognize the user's emotional state. For example, if the user has an anxious expression, that data is acquired from the emotion engine means. The acquired emotion data is input to the server.

[1606] Step 8:

[1607] The server means adjusts the insight based on the emotion data acquired from the emotion engine means. For example, an action such as "provide detailed guidance" is generated based on anxious facial expression data. Based on the emotion data, the adjusted insight or additional information is generated as an output.

[1608] Step 9:

[1609] Monitoring means constantly monitor the environmental conditions within the factory and perform appropriate machine management and maintenance predictions. If an abnormality is detected, an alert is issued immediately and necessary measures are recommended. For example, a warning such as "The temperature is rising sharply. Please turn on the cooling system" is output.

[1610] Example prompt sentence:

[1611] "The current factory environment data is: temperature 30°C, humidity 60%, light intensity 1000 lux, vibration 0.4 m / s². Please use this information to perform optimal machine management and maintenance predictions."

[1612] 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.

[1613] 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.

[1614] 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.

[1615] 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.

[1616] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1617] 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.

[1618] 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).

[1619] 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.

[1620] 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."

[1621] 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.

[1622] 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).

[1623] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1624] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1625] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1626] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1627] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1628] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1629] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1630] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1631] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1632] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1633] The following is further disclosed regarding the above embodiment.

[1634] (Claim 1)

[1635] Sensor means for measuring soil condition, moisture content and light content;

[1636] a terminal means for collecting data from the sensor means and transmitting the data to a server;

[1637] a server means for receiving data transmitted from the terminal means and storing the data in a database;

[1638] a generating AI means for analyzing the data stored in the server means and generating insights regarding crop management, pest prediction, and optimal use of fertilizer;

[1639] A means for transmitting the insight generated by the generating AI means to a terminal;

[1640] means for displaying insights to a user from said terminal;

[1641] a means for said user to adjust how the farm is managed based on said insights;

[1642] Including system.

[1643] (Claim 2)

[1644] 10. The system of claim 1, further comprising means for suggesting customization options appropriate for a particular farm based on data stored in said database.

[1645] (Claim 3)

[1646] The system of claim 1 , further comprising means for the user to adjust platform settings based on the customization options.

[1647] "Example 1"

[1648] (Claim 1)

[1649] a sensor d...

Claims

1. Sensor means for measuring soil condition, moisture content and light content; a terminal means for collecting data from the sensor means and transmitting the data to a server; a server means for receiving data transmitted from the terminal means and storing the data in a database; a generating AI means for analyzing the data stored in the server means and generating insights regarding crop management, pest prediction, and optimal use of fertilizer; A means for transmitting the insight generated by the generating AI means to a terminal; means for displaying insights to a user from said terminal; a means for said user to adjust how the farm is managed based on said insights; Including system.

2. The system of claim 1 , further comprising means for suggesting customization options suitable for a particular farm based on data stored in the database.

3. The system of claim 1 , further comprising means for the user to adjust platform settings based on the customization options.

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