system

Abstract

We provide the system. [Solution] A sensor unit for detecting the growth status of crops and environmental conditions, A generation AI unit analyzes the data detected by the aforementioned sensor unit, Based on the aforementioned analysis results, a control unit generates work procedures for optimizing agricultural work, An operating unit for operating agricultural machinery based on the aforementioned work procedure, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the agricultural field, in order to cope with the aging of the population, the shortage of labor due to the decrease in the number of agricultural workers, and the increase in food demand on a global scale, an improvement in production efficiency and a sustainable and high-quality supply of crops are required. However, with the conventional methods, it is difficult to accurately grasp the situation of crops and provide an appropriate growing environment based on it, and since there are many operations that rely on manual labor, efficient operation is difficult.

Means for Solving the Problems

[0005] This invention provides a technology that incorporates a sensor unit that automatically detects the growth status and environmental conditions of crops, and performs data analysis via a generation AI unit. Based on the analysis results, the control unit generates an optimal work procedure, and the operation unit executes this procedure to automatically operate agricultural machinery. This solves labor shortages, improves production efficiency, and enables sustainable and high-quality crop cultivation.

[0006] The "sensor unit" is a device that measures the agricultural environment and the physical condition of crops, and generates data based on those measurements.

[0007] The "Generative AI Unit" is the part that includes artificial intelligence technology to analyze data provided by the sensor unit and evaluate the growth status of crops and environmental conditions.

[0008] The "control unit" is the part that has the function of formulating specific procedures to optimize agricultural work based on the results analyzed by the generation AI unit.

[0009] The "operation unit" is a device that automatically operates agricultural machinery according to the work procedures generated by the control unit. [Brief explanation of the drawing]

[0010] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0011] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0012] First, let's explain the terminology used in the following explanation.

[0013] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0014] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0015] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0016] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0017] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0018] [First Embodiment]

[0019] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0020] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0021] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0022] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0023] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0024] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0025] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0026] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0027] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0028] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0029] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0030] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0031] This invention is a system that realizes automation and efficiency in the agricultural field. The system is equipped with a sensor unit to monitor crop growth and surrounding environmental conditions in real time. This sensor unit collects environmental data such as temperature, humidity, and soil moisture content through cameras and various sensors, and generates image data of the crops.

[0032] The server collects this data and uses a generation AI unit to perform data analysis. Specifically, it analyzes the growth status of crops and evaluates whether there are any diseases or whether the environment is suitable for plant growth.

[0033] The analysis results are processed by a control unit within the server, which generates work procedures for maintaining an optimal agricultural environment. These procedures include adjusting temperature and humidity, determining appropriate fertilizer dosages, and suggesting the optimal harvest time.

[0034] The server sends the generated work procedures to a terminal (agricultural automation robot), which then operates the agricultural machinery accordingly. This enables automated farming to achieve the specified growing environment.

[0035] As an example, consider a farm cultivating tomatoes. The user inputs the target yield and quality into the system. The server analyzes the crop's growth status based on data from sensors and detects problems such as signs of disease or water shortage. It then optimizes the timing and amount of fertilizer and water application, and suggests a harvest time to help the user achieve their goals. The terminal automatically performs the necessary tasks according to these instructions, maintaining the crop in an optimal state of growth.

[0036] In this way, the present invention makes it possible to supply high-quality crops while solving problems such as an aging population and labor shortages.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] The terminal uses a sensor unit to collect image data of agricultural products and environmental data (temperature, humidity, soil moisture content, etc.).

[0040] Step 2:

[0041] The terminal transfers the collected data to the server.

[0042] Step 3:

[0043] The server preprocesses the received data. Specifically, it corrects missing data values, removes image noise, and adjusts the resolution.

[0044] Step 4:

[0045] The server analyzes the pre-processed data using the generation AI unit to evaluate the growth and health status of the crops.

[0046] Step 5:

[0047] The server calculates the optimal growing environment based on the analysis results and determines the necessary control parameters.

[0048] Step 6:

[0049] The server generates specific work procedures based on calculations and sends them to the terminal.

[0050] Step 7:

[0051] The terminal automatically operates agricultural machinery to perform tasks according to the received work instructions.

[0052] Step 8:

[0053] The server monitors the results of the task execution and uses the feedback data obtained for subsequent analyses.

[0054] (Example 1)

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

[0056] In the agricultural sector, aging and labor shortages are serious problems, and there is a need to achieve efficient and effective farming practices. Furthermore, it is difficult to properly manage crop growth and environmental conditions, making improvements in productivity and quality a challenge. Therefore, there is a need to develop a system that automates agricultural work based on environmental data and continuously monitors the health of crops.

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

[0058] In this invention, the server includes detection means for collecting environmental data and image data, generation AI means for analyzing the data obtained by the detection means and evaluating the health of crops and appropriate care, and control means for generating work procedures to optimize the agricultural environment based on the evaluation results. This enables the efficiency and automation of agricultural work, and allows for the management of crop growth under optimal conditions.

[0059] "Detection means" refers to devices or technical elements provided for collecting environmental data and image data, which acquire information such as temperature, humidity, and soil moisture content using sensors.

[0060] "Generative AI means" refers to processing technology that utilizes artificial intelligence to analyze data obtained by detection means and evaluate the health status and maintenance needs of crops.

[0061] "Control means" refers to a technical element for generating work procedures to optimize the agricultural environment based on the evaluation results of the generating AI means, and for managing and controlling the entire system accordingly.

[0062] "Operating means" refers to a mechanism for operating agricultural machinery according to the work procedures from the control means and performing specific actions.

[0063] This invention is a system for efficiently managing and optimizing crop growth and environmental conditions in agricultural settings, and is primarily implemented by three parties: a server, terminals, and users.

[0064] The server uses various sensors and cameras to measure temperature, humidity, and soil moisture as detection means for collecting environmental and image data. This makes it possible to accurately monitor crop growth and the external environment.

[0065] The server analyzes this data using a generative AI model. The generative AI model evaluates the health of the crops through data analysis and provides information for necessary care and environmental adjustments. Specifically, it quickly detects potential problems such as signs of disease or water shortages. An example of a prompt message would be, "Based on the current environmental data, evaluate the risk of tomato disease and suggest countermeasures."

[0066] Based on the analysis results, the server generates work procedures using control mechanisms and transmits them to the terminal. The terminal is equipped with action mechanisms for operating agricultural machinery and robots, and automatically performs the instructed tasks. For example, it can automate actions such as adjusting water levels by operating an irrigation system or administering the appropriate amount of fertilizer.

[0067] Users can input harvest targets, quality standards, and other information into the system, which then optimizes work procedures based on this input. This enables efficient farming that achieves the user's set goals, resulting in effective agricultural management even in fields facing aging populations and labor shortages.

[0068] This system will promote automation and efficiency in agriculture, enabling a sustainable supply of high-quality crops.

[0069] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0070] Step 1:

[0071] The server collects environmental data using sensors. Specifically, it acquires data from temperature, humidity, and soil moisture sensors, and collects images of crops using cameras. This data is recorded in a database. The inputs are raw environmental and image data from sensors and cameras, and the output is structured data stored in the database.

[0072] Step 2:

[0073] The server inputs the data collected in the previous step into a generating AI model. This AI model analyzes temperature, humidity, soil moisture, and image data to generate insights into crop health and growth. The inputs are structured environmental data and image data obtained from a database, and the outputs are assessments of crop health, signs of potential diseases, and moisture deficiencies. Specifically, the AI ​​uses image analysis algorithms to check for the presence of diseases and detects elements for optimization by comparing them with physical environmental conditions.

[0074] Step 3:

[0075] The server generates work procedures based on the output of the generated AI model. This control system creates work instructions that include optimal temperature control, humidity management, and fertilizer application timing based on the analysis results. The input is the analysis results from the generated AI model, and the output is a detailed work procedure document. Specifically, it generates clear instructions such as "add 50% more fertilizer in the next clockwise cycle" and "activate the irrigation system to maintain a humidity level of 60%."

[0076] Step 4:

[0077] The terminal operates agricultural machinery based on work procedures transmitted from the server. The terminal automatically performs specified farm tasks using its operating mechanisms. The input is the work procedures from the control mechanisms, and the output is the result of the actual farm work performed. Specific actions include starting the irrigation system, applying fertilizer, and adjusting the temperature control system. Once these actions are completed, the terminal feeds the results back to the server.

[0078] (Application Example 1)

[0079] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0080] Modern cities face rapid population growth and environmental and food security challenges. In particular, there is a need to ensure the efficiency and sustainability of agriculture in suburban areas. However, traditional farming methods have limitations in ensuring a stable supply of fresh produce due to the difficulty of analyzing vast amounts of data and managing the environment precisely. Furthermore, it has been difficult for users to understand the agricultural environment in real time and make efficient decisions.

[0081] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0082] In this invention, the server includes means for using multiple detection devices to detect the growth status of crops and environmental conditions, means for using a generation AI unit to analyze the information collected by the detection devices, and control means for optimizing farm environment management based on the analysis results. This enables efficient and sustainable agricultural management even in urban environments, and allows users to understand the state of the farm through an information terminal and make quick decisions.

[0083] A "detection device" is a device that measures the growth status of crops and environmental conditions, and collects data such as temperature, humidity, and soil moisture content.

[0084] The "Generation AI Department" is a department equipped with artificial intelligence that analyzes information collected by detection devices to predict crop growth and calculate optimal environmental conditions.

[0085] The "control means" refers to the means of adjusting and optimizing the farm's environmental management based on the analysis results of the generating AI unit.

[0086] "Operating means" refers to means of operating automated agricultural equipment based on control means.

[0087] "Communication means" refers to communication technology used to display farm status on information terminals and to receive operation commands and requests from users.

[0088] An "information terminal" is an electronic device used by users to view farm data in real time and input operational commands.

[0089] This invention provides a highly integrated system for efficiently managing multiple farms in an urban environment. The system consists of multiple detection devices, a generation AI unit, control means, operation means, communication means, and an information terminal.

[0090] The server first receives environmental data collected by detection devices installed on the farm. This data includes temperature, humidity, soil moisture content, and plant growth status. This data is sent to a generative AI unit running on a cloud service and analyzed using deep learning. During this process, APIs such as TENSORFLOW® and OpenAI® are used to perform highly accurate growth predictions and optimize environmental conditions.

[0091] Once the generation AI unit completes the analysis, the server uses the control means to transmit commands based on the analysis results to the operation means. The operation means transmits the commands to the automated agricultural equipment and performs the necessary environmental adjustments and tasks.

[0092] Furthermore, the server displays analysis results and environmental conditions on information terminals via communication. This allows users to check the information in real time using their smartphones or computers and send operational commands for specific crops as needed.

[0093] As a specific example, in a tomato farm, when a sensor detects an increase in evapotranspiration, the AI ​​calculates the optimal timing for watering. The user can then check this on an information terminal and transmit appropriate instructions to agricultural equipment, enabling a stable harvest.

[0094] An example of a prompt to input into the generating AI model is, "Evaluate the growth status of the tomatoes based on current sensor information and suggest appropriate environmental adjustments." Through this process, the farm can be operated efficiently, and a stable supply of fresh produce to urban areas can be ensured.

[0095] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0096] Step 1:

[0097] The server receives environmental data such as temperature, humidity, and soil moisture content in real time from detection devices installed on the farm. This input data forms the basis for analysis performed later in the generation AI unit.

[0098] Step 2:

[0099] The server sends the collected data to the Generative AI unit. The Generative AI unit uses TensorFlow to execute a machine learning model and perform data analysis. In this process, the algorithm evaluates the growth status of crops and outputs analysis results that enable early detection of diseases and determine whether the current cultivation environment is optimal.

[0100] Step 3:

[0101] The server receives the analysis results from the generation AI unit and, via the control system, creates commands for maintaining the optimal agricultural environment. These commands include requirements for temperature and humidity adjustment, water supply, and appropriate fertilizer application, and these are output as instructions for agricultural equipment.

[0102] Step 4:

[0103] The terminal receives commands from the server through its control system and transmits them to the agricultural equipment. The agricultural equipment then automatically performs the necessary environmental adjustment tasks according to the commands. For example, the water supply may be adjusted.

[0104] Step 5:

[0105] The user uses an information terminal to check reports on the progress of work and environmental conditions obtained from agricultural equipment. Based on this information, the user sends additional commands via the information terminal as needed to manage the farm. If a specific operation command is provided as input by the user, it is sent to the terminal.

[0106] Step 6:

[0107] The server receives input from the user, and if necessary, uses the generation AI unit again to perform analysis and maintain overall system optimization. At this stage, a new analysis is performed according to the prompt, and the proposed generation AI model is reflected in the final system output.

[0108] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0109] This invention relates to a system that combines an emotion engine with an agricultural assistance system to enable agricultural support tailored to the user's emotional state. The system includes a sensor unit for detecting crop growth and environmental conditions, a generation AI unit for analyzing data, a control unit for generating work procedures, and an operation unit for operating agricultural machinery. It also features an emotion engine that recognizes the user's emotions.

[0110] The terminal uses various sensors to detect environmental data and crop growth status, and transmits it to the server. The server analyzes this data in its AI generation unit to determine the optimal growing conditions for the crops. The control unit generates work procedures and creates steps to maintain an appropriate growing environment.

[0111] In addition, the emotion engine detects the user's emotional state and adjusts the work procedures accordingly. For example, if the user is feeling stressed, it suggests less burdensome work procedures; conversely, if the user is highly motivated, it provides proactive work instructions. Furthermore, the emotion engine analyzes the feedback information provided by the user and continuously improves the analysis algorithm of the generation AI unit.

[0112] As a concrete example, consider a user who is cultivating tomatoes. The emotion engine checks the user's daily feelings and accurately detects their mental state. The server analyzes data such as temperature and humidity to check the condition of the crops and makes suggestions tailored to each situation. Based on the user's emotions, it identifies days when the user should relax and days when they want to work actively, and creates a farming plan accordingly. This improves the user experience while also optimizing work efficiency. This invention makes it possible to solve the problems of conventional agricultural work while increasing operational flexibility.

[0113] The following describes the processing flow.

[0114] Step 1:

[0115] The device uses a sensor unit to collect data on the growth status of crops and the surrounding environment. This sensor unit has the function of measuring data such as temperature, humidity, and soil moisture content.

[0116] Step 2:

[0117] The device sends the collected data to the server.

[0118] Step 3:

[0119] The server preprocesses the transmitted data. For example, it performs tasks such as imputing missing values, filtering outliers, and denoising images.

[0120] Step 4:

[0121] The server uses the generation AI unit to analyze pre-processed data. Here, it evaluates crop growth status, detects diseases, and assesses appropriate environmental conditions.

[0122] Step 5:

[0123] Based on the analysis results, the server generates the optimal farming procedure in the control unit. This procedure includes things like the timing of watering and fertilizing.

[0124] Step 6:

[0125] The terminal continuously collects data from sensors, and the server uses this data to re-evaluate environmental conditions in real time.

[0126] Step 7:

[0127] The user's emotional state is detected by the emotion engine. The emotion engine analyzes the user's emotions from their facial expressions and voice via a camera or voice input device.

[0128] Step 8:

[0129] The server adjusts the generated work procedures according to the user's emotions, based on the emotional data provided by the emotion engine. It provides work procedures to reduce stress and work suggestions to maintain motivation.

[0130] Step 9:

[0131] The server sends the final work instructions to the terminal, which then operates the agricultural machinery to automatically carry out the work.

[0132] Step 10:

[0133] When user feedback is provided, the emotion engine analyzes this feedback and uses it to improve the algorithms of the generative AI unit so that suggestions for future work can be more appropriately adjusted.

[0134] (Example 2)

[0135] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0136] In agricultural work, it is essential to not only optimally manage environmental conditions and crop growth, but also to propose efficient and effective work practices that take into account the emotional state of the workers. Conventional systems do not provide flexible work instructions that take into account the psychological factors of workers, resulting in challenges such as reduced worker stress and decreased productivity due to lack of motivation.

[0137] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0138] In this invention, the server includes detection means, analysis means, control means, operation means, and emotion recognition means. This enables optimized support for agricultural work based on environmental data and emotion data.

[0139] A "detection device" is a technical device used to monitor the growth status of agricultural products and environmental conditions in real time.

[0140] "Analysis means" refers to a technical system that processes detected data and derives the optimal growing conditions for crops.

[0141] A "control device" is a technical device that generates work procedures based on analysis results and provides instructions to optimize agricultural work.

[0142] "Operating means" refers to the technical mechanism for operating agricultural machinery based on the generated work procedures.

[0143] An "emotion recognition system" is a technical system that detects the user's emotional state and adjusts work instructions based on that state.

[0144] This invention is a system that analyzes environmental data and crop growth status while considering the user's emotions during agricultural work, and provides efficient work procedures. Its main components involve three entities—a terminal, a server, and a user—working together.

[0145] The terminal uses temperature, humidity, light, and image sensors installed on the farm to detect environmental data and crop conditions. This data is transmitted to a server via a wireless network. The data collected by the terminal includes temperature, humidity, sunlight, and the color and shape of crop leaves. Specifically, the terminal collects data every hour and sends it to a cloud server to enable real-time monitoring.

[0146] The server uses a generative AI unit to analyze the optimal growing conditions for crops based on the received data. For this purpose, it utilizes a proprietary generative AI model. The AI ​​model calculates the optimal amount of water and fertilization timing, for example, by considering weather fluctuations and crop characteristics. The analysis results are sent to the control unit, which generates specific work procedures. The server also incorporates an emotion engine that analyzes user feedback to determine the user's emotional state. For example, the server uses the emotion engine to analyze the user's stress level input and adjusts work instructions accordingly. An example of a prompt message might be: "The current temperature is 25 degrees Celsius and the humidity is 60%. The tomato leaves are starting to turn slightly yellow. The user is feeling a little tired. Based on this data, please suggest the optimal farming tasks for today."

[0147] Users perform actual farm work following the work procedures suggested through this system. Users provide feedback via terminals or smartphones, and this feedback helps improve the accuracy of the system. To enhance the user experience, the emotion engine constantly analyzes user feedback and contributes to improving the algorithms of the generation AI unit. Through this cycle, the invention can improve not only work efficiency but also user satisfaction.

[0148] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0149] Step 1:

[0150] The terminal collects data from various sensors placed on the farm. Inputs include temperature, humidity, sunlight, and crop growth status. Based on this sensor data, the terminal understands the current state of the environment in real time. As output, the sensor data is transmitted to a server via a wireless network.

[0151] Step 2:

[0152] The server inputs the received sensor data into a generated AI model as an analysis tool. Based on this data, the AI ​​model uses statistical analysis and machine learning algorithms to derive the optimal method for growing crops. This process outputs specific instructions such as the optimal amount of irrigation and fertilization schedule.

[0153] Step 3:

[0154] The server generates work procedures based on the analysis results using control means. It uses the analysis results of the generated AI model as input and creates a specific farm work plan accordingly. This includes prioritizing tasks and allocating necessary resources. The output is passed to the operating means as an optimized work procedure.

[0155] Step 4:

[0156] The server detects the user's emotional state using emotion recognition technology. User feedback and past emotional data are used as input. The emotion recognition technology analyzes this information to understand the user's stress level and motivation. The output is instructions for adjusting work procedures according to the emotional state.

[0157] Step 5:

[0158] Users perform farm work according to the procedures suggested by the server. Automated machine operation is supported by the control system, allowing users to work efficiently. After completion, users provide feedback to the system via a terminal or other device. This feedback is used for future analysis and system improvement.

[0159] (Application Example 2)

[0160] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0161] In agricultural work, the user's emotional state significantly impacts work efficiency and experience, yet there is no system that appropriately utilizes this to optimize work. Furthermore, conventional systems simply issue work instructions based on environmental data, failing to provide work processes that take into account the user's mental state.

[0162] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0163] In this invention, the server includes a detection unit for perceiving the growth status of agricultural products and environmental conditions, an emotion recognition unit for recognizing the user's emotional state and adjusting the activity plan according to that emotion, and a control unit for creating an activity plan to optimize work activities. This enables flexible and optimal agricultural work in accordance with the user's emotional state.

[0164] "Agricultural products" refer to plant-based food products and cash crops obtained through agricultural production.

[0165] "Growth status" refers to an indicator used to evaluate the developmental status and health of agricultural products during their growth process.

[0166] "Environmental conditions" refer to the surrounding physical conditions, such as temperature, humidity, and soil moisture content, that affect the growth of agricultural products.

[0167] The term "detection unit" refers to a component that includes various sensors positioned to perceive the growth status of agricultural products and environmental conditions.

[0168] The "generation algorithm unit" refers to the component that analyzes the information obtained by the detection unit and performs data processing necessary for optimizing agricultural activities.

[0169] "Activity plan" refers to the process and procedures for efficiently carrying out agricultural work, based on the analysis results from the generation algorithm unit.

[0170] A "control unit" refers to a component that manages mechanical devices based on the generated activity plan and directs them to perform appropriate actions.

[0171] The term "operating unit" refers to a component that includes a drive mechanism for the mechanical device to operate based on instructions from the control unit.

[0172] The "emotion recognition unit" refers to a component that has the function of perceiving the user's emotional state and adjusting the activity plan accordingly.

[0173] This invention realizes a system that provides work support tailored to the user's emotional state while optimally managing the growth status of agricultural products. The system consists of a detection unit for detecting the growth status of agricultural products and environmental conditions, an emotion recognition unit for recognizing the user's emotional state, and a control unit for economically planning work activities based on this information.

[0174] The server receives information from the detection unit and performs analysis using a generation algorithm. Specifically, it uses a program built in Python to predict growth based on environmental information (temperature, humidity, soil moisture content, etc.). The OpenAI GPT model is used as the generation AI model to generate an optimal activity plan. The server transmits the activity plan to the control unit via ROS (Robot Operating System). The control unit then issues instructions to the operation unit to perform the necessary mechanical devices.

[0175] Furthermore, the system uses Google's Cloud Natural Language API for emotion recognition, analyzing user emotions from their voice and facial expressions. This enables flexible work instructions tailored to the user's emotions. For example, if the server hears a user say, "I'm tired today," the system will modify the activity plan and suggest less demanding tasks.

[0176] For example, if a user is watering plants and makes a positive statement such as, "They're feeling good today, so let's prune them," the system will guide them through the pruning process, including the necessary steps and precautions. The generating AI model can be input with the following prompt: "Suggest gardening tasks for when User A is in a good mood. We've had a string of sunny days recently." This prompt will then suggest more appropriate work procedures.

[0177] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0178] Step 1:

[0179] The terminal acquires environmental data such as ambient temperature, humidity, and soil moisture content from sensors. It processes this data and sends it to the server. The input is environmental sensor data, and the output is the transmission of data to the server.

[0180] Step 2:

[0181] The server receives environmental data sent from the terminal and uses a generation algorithm to predict crop growth. During this process, it performs data analysis using Python and generates an optimal activity plan. The input is the submitted environmental data, and the output is the generated activity plan.

[0182] Step 3:

[0183] To detect user emotions, the emotion recognition unit analyzes the user's voice and facial expressions using Google's Cloud Natural Language API. The input is the user's voice and video data, and the output is the user's emotional state.

[0184] Step 4:

[0185] The server dynamically adjusts the generated activity plan according to the user's emotional state. Specifically, it reconstructs the activity plan using an advanced generative AI model (OpenAI's GPT model). The input is the activity plan and the user's emotional state, and the output is the adjusted activity plan.

[0186] Step 5:

[0187] The server transmits the coordinated activity plan to the control unit. The control unit then transmits instructions to the operating unit based on this plan, managing the machine to operate as instructed. The input is the activity plan transmitted from the server, and the output is the operation of the machine.

[0188] Step 6:

[0189] The user performs farm work according to the activity plan and work instructions presented by the server. During this process, the user can provide feedback to the server through the emotion recognition unit, which further optimizes the system. The input is the content of the activity plan, and the output is feedback information.

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

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

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

[0193] [Second Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0206] This invention is a system that realizes automation and efficiency in the agricultural field. The system is equipped with a sensor unit to monitor crop growth and surrounding environmental conditions in real time. This sensor unit collects environmental data such as temperature, humidity, and soil moisture content through cameras and various sensors, and generates image data of the crops.

[0207] The server collects this data and uses a generation AI unit to perform data analysis. Specifically, it analyzes the growth status of crops and evaluates whether there are any diseases or whether the environment is suitable for plant growth.

[0208] The analysis results are processed by a control unit within the server, which generates work procedures for maintaining an optimal agricultural environment. These procedures include adjusting temperature and humidity, determining appropriate fertilizer dosages, and suggesting the optimal harvest time.

[0209] The server sends the generated work procedures to a terminal (agricultural automation robot), which then operates the agricultural machinery accordingly. This enables automated farming to achieve the specified growing environment.

[0210] As an example, consider a farm cultivating tomatoes. The user inputs the target yield and quality into the system. The server analyzes the crop's growth status based on data from sensors and detects problems such as signs of disease or water shortage. It then optimizes the timing and amount of fertilizer and water application, and suggests a harvest time to help the user achieve their goals. The terminal automatically performs the necessary tasks according to these instructions, maintaining the crop in an optimal state of growth.

[0211] In this way, the present invention makes it possible to supply high-quality crops while solving problems such as an aging population and labor shortages.

[0212] The following describes the processing flow.

[0213] Step 1:

[0214] The terminal uses a sensor unit to collect image data of agricultural products and environmental data (temperature, humidity, soil moisture content, etc.).

[0215] Step 2:

[0216] The terminal transfers the collected data to the server.

[0217] Step 3:

[0218] The server preprocesses the received data. Specifically, it corrects missing data values, removes image noise, and adjusts the resolution.

[0219] Step 4:

[0220] The server analyzes the pre-processed data using the generation AI unit to evaluate the growth and health status of the crops.

[0221] Step 5:

[0222] The server calculates the optimal growing environment based on the analysis results and determines the necessary control parameters.

[0223] Step 6:

[0224] The server generates specific work procedures based on calculations and sends them to the terminal.

[0225] Step 7:

[0226] The terminal automatically operates agricultural machinery to perform tasks according to the received work instructions.

[0227] Step 8:

[0228] The server monitors the results of the task execution and uses the feedback data obtained for subsequent analyses.

[0229] (Example 1)

[0230] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0231] In the agricultural sector, aging and labor shortages are serious problems, and there is a need to achieve efficient and effective farming practices. Furthermore, it is difficult to properly manage crop growth and environmental conditions, making improvements in productivity and quality a challenge. Therefore, there is a need to develop a system that automates agricultural work based on environmental data and continuously monitors the health of crops.

[0232] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0233] In this invention, the server includes detection means for collecting environmental data and image data, generation AI means for analyzing the data obtained by the detection means and evaluating the health of crops and appropriate care, and control means for generating work procedures to optimize the agricultural environment based on the evaluation results. This enables the efficiency and automation of agricultural work, and allows for the management of crop growth under optimal conditions.

[0234] "Detection means" refers to devices or technical elements provided for collecting environmental data and image data, which acquire information such as temperature, humidity, and soil moisture content using sensors.

[0235] "Generative AI means" refers to processing technology that utilizes artificial intelligence to analyze data obtained by detection means and evaluate the health status and maintenance needs of crops.

[0236] "Control means" refers to a technical element for generating work procedures to optimize the agricultural environment based on the evaluation results of the generating AI means, and for managing and controlling the entire system accordingly.

[0237] "Operating means" refers to a mechanism for operating agricultural machinery according to the work procedures from the control means and performing specific actions.

[0238] This invention is a system for efficiently managing and optimizing crop growth and environmental conditions in agricultural settings, and is primarily implemented by three parties: a server, terminals, and users.

[0239] The server uses various sensors and cameras to measure temperature, humidity, and soil moisture as detection means for collecting environmental and image data. This makes it possible to accurately monitor crop growth and the external environment.

[0240] The server analyzes this data using a generative AI model. The generative AI model evaluates the health of the crops through data analysis and provides information for necessary care and environmental adjustments. Specifically, it quickly detects potential problems such as signs of disease or water shortages. An example of a prompt message would be, "Based on the current environmental data, evaluate the risk of tomato disease and suggest countermeasures."

[0241] Based on the analysis results, the server generates work procedures using control mechanisms and transmits them to the terminal. The terminal is equipped with action mechanisms for operating agricultural machinery and robots, and automatically performs the instructed tasks. For example, it can automate actions such as adjusting water levels by operating an irrigation system or administering the appropriate amount of fertilizer.

[0242] Users can input harvest targets, quality standards, and other information into the system, which then optimizes work procedures based on this input. This enables efficient farming that achieves the user's set goals, resulting in effective agricultural management even in fields facing aging populations and labor shortages.

[0243] This system will promote automation and efficiency in agriculture, enabling a sustainable supply of high-quality crops.

[0244] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0245] Step 1:

[0246] The server collects environmental data using sensors. Specifically, it acquires data from temperature, humidity, and soil moisture sensors, and collects images of crops using cameras. This data is recorded in a database. The inputs are raw environmental and image data from sensors and cameras, and the output is structured data stored in the database.

[0247] Step 2:

[0248] The server inputs the data collected in the previous step into a generating AI model. This AI model analyzes temperature, humidity, soil moisture, and image data to generate insights into crop health and growth. The inputs are structured environmental data and image data obtained from a database, and the outputs are assessments of crop health, signs of potential diseases, and moisture deficiencies. Specifically, the AI ​​uses image analysis algorithms to check for the presence of diseases and detects elements for optimization by comparing them with physical environmental conditions.

[0249] Step 3:

[0250] The server generates work procedures based on the output of the generated AI model. This control system creates work instructions that include optimal temperature control, humidity management, and fertilizer application timing based on the analysis results. The input is the analysis results from the generated AI model, and the output is a detailed work procedure document. Specifically, it generates clear instructions such as "add 50% more fertilizer in the next clockwise cycle" and "activate the irrigation system to maintain a humidity level of 60%."

[0251] Step 4:

[0252] The terminal operates agricultural machinery based on work procedures transmitted from the server. The terminal automatically performs specified farm tasks using its operating mechanisms. The input is the work procedures from the control mechanisms, and the output is the result of the actual farm work performed. Specific actions include starting the irrigation system, applying fertilizer, and adjusting the temperature control system. Once these actions are completed, the terminal feeds the results back to the server.

[0253] (Application Example 1)

[0254] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0255] Modern cities face rapid population growth and environmental and food security challenges. In particular, there is a need to ensure the efficiency and sustainability of agriculture in suburban areas. However, traditional farming methods have limitations in ensuring a stable supply of fresh produce due to the difficulty of analyzing vast amounts of data and managing the environment precisely. Furthermore, it has been difficult for users to understand the agricultural environment in real time and make efficient decisions.

[0256] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0257] In this invention, the server includes means for using multiple detection devices to detect the growth status of crops and environmental conditions, means for using a generation AI unit to analyze the information collected by the detection devices, and control means for optimizing farm environment management based on the analysis results. This enables efficient and sustainable agricultural management even in urban environments, and allows users to understand the state of the farm through an information terminal and make quick decisions.

[0258] A "detection device" is a device that measures the growth status of crops and environmental conditions, and collects data such as temperature, humidity, and soil moisture content.

[0259] The "Generation AI Department" is a department equipped with artificial intelligence that analyzes information collected by detection devices to predict crop growth and calculate optimal environmental conditions.

[0260] The "control means" refers to the means of adjusting and optimizing the farm's environmental management based on the analysis results of the generating AI unit.

[0261] "Operating means" refers to means of operating automated agricultural equipment based on control means.

[0262] "Communication means" refers to communication technology used to display farm status on information terminals and to receive operation commands and requests from users.

[0263] An "information terminal" is an electronic device used by users to view farm data in real time and input operational commands.

[0264] This invention provides a highly integrated system for efficiently managing multiple farms in an urban environment. The system consists of multiple detection devices, a generation AI unit, control means, operation means, communication means, and an information terminal.

[0265] The server first receives environmental data collected by detection devices installed on the farm. This data includes temperature, humidity, soil moisture content, and plant growth status. This data is sent to a generative AI unit running on a cloud service and analyzed using deep learning. During this process, TensorFlow and OpenAI APIs are utilized to achieve highly accurate growth predictions and optimization of environmental conditions.

[0266] Once the generation AI unit completes the analysis, the server uses the control means to transmit commands based on the analysis results to the operation means. The operation means transmits the commands to the automated agricultural equipment and performs the necessary environmental adjustments and tasks.

[0267] Furthermore, the server displays analysis results and environmental conditions on information terminals via communication. This allows users to check the information in real time using their smartphones or computers and send operational commands for specific crops as needed.

[0268] As a specific example, in a tomato farm, when a sensor detects an increase in evapotranspiration, the AI ​​calculates the optimal timing for watering. The user can then check this on an information terminal and transmit appropriate instructions to agricultural equipment, enabling a stable harvest.

[0269] An example of a prompt to input into the generating AI model is, "Evaluate the growth status of the tomatoes based on current sensor information and suggest appropriate environmental adjustments." Through this process, the farm can be operated efficiently, and a stable supply of fresh produce to urban areas can be ensured.

[0270] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0271] Step 1:

[0272] The server receives environmental data such as temperature, humidity, and soil moisture content in real time from detection devices installed on the farm. This input data forms the basis for analysis performed later in the generation AI unit.

[0273] Step 2:

[0274] The server sends the collected data to the Generative AI unit. The Generative AI unit uses TensorFlow to execute a machine learning model and perform data analysis. In this process, the algorithm evaluates the growth status of crops and outputs analysis results that enable early detection of diseases and determine whether the current cultivation environment is optimal.

[0275] Step 3:

[0276] The server receives the analysis results from the generation AI unit and, via the control system, creates commands for maintaining the optimal agricultural environment. These commands include requirements for temperature and humidity adjustment, water supply, and appropriate fertilizer application, and these are output as instructions for agricultural equipment.

[0277] Step 4:

[0278] The terminal receives commands from the server through its control system and transmits them to the agricultural equipment. The agricultural equipment then automatically performs the necessary environmental adjustment tasks according to the commands. For example, the water supply may be adjusted.

[0279] Step 5:

[0280] The user uses an information terminal to check reports on the progress of work and environmental conditions obtained from agricultural equipment. Based on this information, the user sends additional commands via the information terminal as needed to manage the farm. If a specific operation command is provided as input by the user, it is sent to the terminal.

[0281] Step 6:

[0282] The server receives input from the user, and if necessary, uses the generation AI unit again to perform analysis and maintain overall system optimization. At this stage, a new analysis is performed according to the prompt, and the proposed generation AI model is reflected in the final system output.

[0283] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0284] The present invention is a system that enables agricultural support according to the emotional state of a user by combining an emotion engine with an agricultural assistance system. This system includes a sensor unit that detects the growth state and environmental state of crops, a generative AI unit that analyzes data, a control unit that generates work procedures, and an operation unit that operates agricultural machinery. It also has an emotion engine that recognizes the emotions of the user.

[0285] The terminal uses various sensors to detect environmental data and the growth state of crops and transmits them to the server. The server analyzes these data in the generative AI unit to derive the optimal growth conditions for the crops. The control unit generates work procedures and creates procedures to maintain an appropriate growth environment.

[0286] In addition, the emotion engine detects the emotional state of the user and adjusts the work procedures according to the user. For example, when the user is feeling stressed, it proposes work procedures with less burden, and conversely, when the motivation is high, it gives positive work instructions. Furthermore, the emotion engine analyzes the feedback information provided by the user and continuously improves the analysis algorithm of the generative AI unit.

[0287] As a specific example, consider the case where the user is growing tomatoes. The emotion engine checks the user's daily feelings and detects the exact mental state. The server analyzes data such as temperature and humidity to check the state of the agricultural crops and makes proposals according to each situation. Based on the user's emotions, it identifies days for relaxation or days when the user wants to work actively and formulates an agricultural work plan accordingly. This can optimize work efficiency while improving the user experience. With the present invention, it is possible to solve the problems of conventional agricultural work and enhance the flexibility of operation.

[0288] The following explains the processing flow.

[0289] Step 1:

[0290] The device uses a sensor unit to collect data on the growth status of crops and the surrounding environment. This sensor unit has the function of measuring data such as temperature, humidity, and soil moisture content.

[0291] Step 2:

[0292] The device sends the collected data to the server.

[0293] Step 3:

[0294] The server preprocesses the transmitted data. For example, it performs tasks such as imputing missing values, filtering outliers, and denoising images.

[0295] Step 4:

[0296] The server uses the generation AI unit to analyze pre-processed data. Here, it evaluates crop growth status, detects diseases, and assesses appropriate environmental conditions.

[0297] Step 5:

[0298] Based on the analysis results, the server generates the optimal farming procedure in the control unit. This procedure includes things like the timing of watering and fertilizing.

[0299] Step 6:

[0300] The terminal continuously collects data from sensors, and the server uses this data to re-evaluate environmental conditions in real time.

[0301] Step 7:

[0302] The user's emotional state is detected by the emotion engine. The emotion engine analyzes the user's emotions from their facial expressions and voice via a camera or voice input device.

[0303] Step 8:

[0304] Based on the emotion data provided by the emotion engine, the server adjusts the generated work procedures according to the user's emotions. It provides work procedures for stress reduction and work proposals for maintaining motivation.

[0305] Step 9:

[0306] The server sends the final work procedures to the terminal, and the terminal operates the agricultural machinery to automatically perform the work.

[0307] Step 10:

[0308] When feedback from the user is provided, the emotion engine analyzes this feedback and reflects it in the improvement of the algorithm of the generation AI section so that the proposals for subsequent work can be adjusted more appropriately.

[0309] (Example 2)

[0310] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0311] In agricultural work, it is required to propose efficient and effective work considering not only optimizing environmental conditions and crop growth states but also the emotional state of the operator. In conventional systems, due to the lack of flexible work instructions considering the psychological factors of the operator, issues such as stress reduction of the operator and productivity decline due to insufficient motivation have become problems.

[0312] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0313] In this invention, the server includes a detection means, an analysis means, a control means, an operation means, and an emotion recognition means. This enables optimized support for agricultural work based on environmental data and emotion data.

[0314] A "detection device" is a technical device used to monitor the growth status of agricultural products and environmental conditions in real time.

[0315] "Analysis means" refers to a technical system that processes detected data and derives the optimal growing conditions for crops.

[0316] A "control device" is a technical device that generates work procedures based on analysis results and provides instructions to optimize agricultural work.

[0317] "Operating means" refers to the technical mechanism for operating agricultural machinery based on the generated work procedures.

[0318] An "emotion recognition system" is a technical system that detects the user's emotional state and adjusts work instructions based on that state.

[0319] This invention is a system that analyzes environmental data and crop growth status while considering the user's emotions during agricultural work, and provides efficient work procedures. Its main components involve three entities—a terminal, a server, and a user—working together.

[0320] The terminal uses temperature, humidity, light, and image sensors installed on the farm to detect environmental data and crop conditions. This data is transmitted to a server via a wireless network. The data collected by the terminal includes temperature, humidity, sunlight, and the color and shape of crop leaves. Specifically, the terminal collects data every hour and sends it to a cloud server to enable real-time monitoring.

[0321] The server uses a generative AI unit to analyze the optimal growing conditions for crops based on the received data. For this purpose, it utilizes a proprietary generative AI model. The AI ​​model calculates the optimal amount of water and fertilization timing, for example, by considering weather fluctuations and crop characteristics. The analysis results are sent to the control unit, which generates specific work procedures. The server also incorporates an emotion engine that analyzes user feedback to determine the user's emotional state. For example, the server uses the emotion engine to analyze the user's stress level input and adjusts work instructions accordingly. An example of a prompt message might be: "The current temperature is 25 degrees Celsius and the humidity is 60%. The tomato leaves are starting to turn slightly yellow. The user is feeling a little tired. Based on this data, please suggest the optimal farming tasks for today."

[0322] Users perform actual farm work following the work procedures suggested through this system. Users provide feedback via terminals or smartphones, and this feedback helps improve the accuracy of the system. To enhance the user experience, the emotion engine constantly analyzes user feedback and contributes to improving the algorithms of the generation AI unit. Through this cycle, the invention can improve not only work efficiency but also user satisfaction.

[0323] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0324] Step 1:

[0325] The terminal collects data from various sensors placed on the farm. Inputs include temperature, humidity, sunlight, and crop growth status. Based on this sensor data, the terminal understands the current state of the environment in real time. As output, the sensor data is transmitted to a server via a wireless network.

[0326] Step 2:

[0327] The server inputs the received sensor data into a generated AI model as an analysis tool. Based on this data, the AI ​​model uses statistical analysis and machine learning algorithms to derive the optimal method for growing crops. This process outputs specific instructions such as the optimal amount of irrigation and fertilization schedule.

[0328] Step 3:

[0329] The server generates work procedures based on the analysis results using control means. It uses the analysis results of the generated AI model as input and creates a specific farm work plan accordingly. This includes prioritizing tasks and allocating necessary resources. The output is passed to the operating means as an optimized work procedure.

[0330] Step 4:

[0331] The server detects the user's emotional state using emotion recognition technology. User feedback and past emotional data are used as input. The emotion recognition technology analyzes this information to understand the user's stress level and motivation. The output is instructions for adjusting work procedures according to the emotional state.

[0332] Step 5:

[0333] Users perform farm work according to the procedures suggested by the server. Automated machine operation is supported by the control system, allowing users to work efficiently. After completion, users provide feedback to the system via a terminal or other device. This feedback is used for future analysis and system improvement.

[0334] (Application Example 2)

[0335] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0336] In agricultural work, the user's emotional state significantly impacts work efficiency and experience, yet there is no system that appropriately utilizes this to optimize work. Furthermore, conventional systems simply issue work instructions based on environmental data, failing to provide work processes that take into account the user's mental state.

[0337] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0338] In this invention, the server includes a detection unit for perceiving the growth status of agricultural products and environmental conditions, an emotion recognition unit for recognizing the user's emotional state and adjusting the activity plan according to that emotion, and a control unit for creating an activity plan to optimize work activities. This enables flexible and optimal agricultural work in accordance with the user's emotional state.

[0339] "Agricultural products" refer to plant-based food products and cash crops obtained through agricultural production.

[0340] "Growth status" refers to an indicator used to evaluate the developmental status and health of agricultural products during their growth process.

[0341] "Environmental conditions" refer to the surrounding physical conditions, such as temperature, humidity, and soil moisture content, that affect the growth of agricultural products.

[0342] The term "detection unit" refers to a component that includes various sensors positioned to perceive the growth status of agricultural products and environmental conditions.

[0343] The "generation algorithm unit" refers to the component that analyzes the information obtained by the detection unit and performs data processing necessary for optimizing agricultural activities.

[0344] "Activity plan" refers to the process and procedures for efficiently carrying out agricultural work, based on the analysis results from the generation algorithm unit.

[0345] A "control unit" refers to a component that manages mechanical devices based on the generated activity plan and directs them to perform appropriate actions.

[0346] The term "operating unit" refers to a component that includes a drive mechanism for the mechanical device to operate based on instructions from the control unit.

[0347] The "emotion recognition unit" refers to a component that has the function of perceiving the user's emotional state and adjusting the activity plan accordingly.

[0348] This invention realizes a system that provides work support tailored to the user's emotional state while optimally managing the growth status of agricultural products. The system consists of a detection unit for detecting the growth status of agricultural products and environmental conditions, an emotion recognition unit for recognizing the user's emotional state, and a control unit for economically planning work activities based on this information.

[0349] The server receives information from the detection unit and performs analysis using a generation algorithm. Specifically, it uses a program built in Python to predict growth based on environmental information (temperature, humidity, soil moisture content, etc.). The OpenAI GPT model is used as the generation AI model to generate an optimal activity plan. The server transmits the activity plan to the control unit via ROS (Robot Operating System). The control unit then issues instructions to the operation unit to perform the necessary mechanical devices.

[0350] Furthermore, the system uses Google's Cloud Natural Language API for emotion recognition, analyzing user emotions from their voice and facial expressions. This enables flexible work instructions tailored to the user's emotions. For example, if the server hears a user say, "I'm tired today," the system will modify the activity plan and suggest less demanding tasks.

[0351] For example, if a user is watering plants and makes a positive statement such as, "They're feeling good today, so let's prune them," the system will guide them through the pruning process, including the necessary steps and precautions. The generating AI model can be input with the following prompt: "Suggest gardening tasks for when User A is in a good mood. We've had a string of sunny days recently." This prompt will then suggest more appropriate work procedures.

[0352] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0353] Step 1:

[0354] The terminal acquires environmental data such as ambient temperature, humidity, and soil moisture content from sensors. It processes this data and sends it to the server. The input is environmental sensor data, and the output is the transmission of data to the server.

[0355] Step 2:

[0356] The server receives environmental data sent from the terminal and uses a generation algorithm to predict crop growth. During this process, it performs data analysis using Python and generates an optimal activity plan. The input is the submitted environmental data, and the output is the generated activity plan.

[0357] Step 3:

[0358] To detect user emotions, the emotion recognition unit analyzes the user's voice and facial expressions using Google's Cloud Natural Language API. The input is the user's voice and video data, and the output is the user's emotional state.

[0359] Step 4:

[0360] The server dynamically adjusts the generated activity plan according to the user's emotional state. Specifically, it reconstructs the activity plan using an advanced generative AI model (OpenAI's GPT model). The input is the activity plan and the user's emotional state, and the output is the adjusted activity plan.

[0361] Step 5:

[0362] The server transmits the coordinated activity plan to the control unit. The control unit then transmits instructions to the operating unit based on this plan, managing the machine to operate as instructed. The input is the activity plan transmitted from the server, and the output is the operation of the machine.

[0363] Step 6:

[0364] The user performs farm work according to the activity plan and work instructions presented by the server. During this process, the user can provide feedback to the server through the emotion recognition unit, which further optimizes the system. The input is the content of the activity plan, and the output is feedback information.

[0365] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0366] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0367] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0368] [Third Embodiment]

[0369] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0370] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0371] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0372] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0373] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0374] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0375] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0376] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0377] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0378] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0379] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0380] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0381] This invention is a system that realizes automation and efficiency in the agricultural field. The system is equipped with a sensor unit to monitor crop growth and surrounding environmental conditions in real time. This sensor unit collects environmental data such as temperature, humidity, and soil moisture content through cameras and various sensors, and generates image data of the crops.

[0382] The server collects this data and uses a generation AI unit to perform data analysis. Specifically, it analyzes the growth status of crops and evaluates whether there are any diseases or whether the environment is suitable for plant growth.

[0383] The analysis results are processed by a control unit within the server, which generates work procedures for maintaining an optimal agricultural environment. These procedures include adjusting temperature and humidity, determining appropriate fertilizer dosages, and suggesting the optimal harvest time.

[0384] The server sends the generated work procedures to a terminal (agricultural automation robot), which then operates the agricultural machinery accordingly. This enables automated farming to achieve the specified growing environment.

[0385] As an example, consider a farm cultivating tomatoes. The user inputs the target yield and quality into the system. The server analyzes the crop's growth status based on data from sensors and detects problems such as signs of disease or water shortage. It then optimizes the timing and amount of fertilizer and water application, and suggests a harvest time to help the user achieve their goals. The terminal automatically performs the necessary tasks according to these instructions, maintaining the crop in an optimal state of growth.

[0386] In this way, the present invention makes it possible to supply high-quality crops while solving problems such as an aging population and labor shortages.

[0387] The following describes the processing flow.

[0388] Step 1:

[0389] The terminal uses a sensor unit to collect image data of agricultural products and environmental data (temperature, humidity, soil moisture content, etc.).

[0390] Step 2:

[0391] The terminal transfers the collected data to the server.

[0392] Step 3:

[0393] The server preprocesses the received data. Specifically, it corrects missing data values, removes image noise, and adjusts the resolution.

[0394] Step 4:

[0395] The server analyzes the pre-processed data using the generation AI unit to evaluate the growth and health status of the crops.

[0396] Step 5:

[0397] The server calculates the optimal growing environment based on the analysis results and determines the necessary control parameters.

[0398] Step 6:

[0399] The server generates specific work procedures based on calculations and sends them to the terminal.

[0400] Step 7:

[0401] The terminal automatically operates agricultural machinery to perform tasks according to the received work instructions.

[0402] Step 8:

[0403] The server monitors the results of the task execution and uses the feedback data obtained for subsequent analyses.

[0404] (Example 1)

[0405] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0406] In the agricultural sector, aging and labor shortages are serious problems, and there is a need to achieve efficient and effective farming practices. Furthermore, it is difficult to properly manage crop growth and environmental conditions, making improvements in productivity and quality a challenge. Therefore, there is a need to develop a system that automates agricultural work based on environmental data and continuously monitors the health of crops.

[0407] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0408] In this invention, the server includes detection means for collecting environmental data and image data, generation AI means for analyzing the data obtained by the detection means and evaluating the health of crops and appropriate care, and control means for generating work procedures to optimize the agricultural environment based on the evaluation results. This enables the efficiency and automation of agricultural work, and allows for the management of crop growth under optimal conditions.

[0409] "Detection means" refers to devices or technical elements provided for collecting environmental data and image data, which acquire information such as temperature, humidity, and soil moisture content using sensors.

[0410] "Generative AI means" refers to processing technology that utilizes artificial intelligence to analyze data obtained by detection means and evaluate the health status and maintenance needs of crops.

[0411] "Control means" refers to a technical element for generating work procedures to optimize the agricultural environment based on the evaluation results of the generating AI means, and for managing and controlling the entire system accordingly.

[0412] "Operating means" refers to a mechanism for operating agricultural machinery according to the work procedures from the control means and performing specific actions.

[0413] This invention is a system for efficiently managing and optimizing crop growth and environmental conditions in agricultural settings, and is primarily implemented by three parties: a server, terminals, and users.

[0414] The server uses various sensors and cameras to measure temperature, humidity, and soil moisture as detection means for collecting environmental and image data. This makes it possible to accurately monitor crop growth and the external environment.

[0415] The server analyzes this data using a generative AI model. The generative AI model evaluates the health of the crops through data analysis and provides information for necessary care and environmental adjustments. Specifically, it quickly detects potential problems such as signs of disease or water shortages. An example of a prompt message would be, "Based on the current environmental data, evaluate the risk of tomato disease and suggest countermeasures."

[0416] Based on the analysis results, the server generates work procedures using control mechanisms and transmits them to the terminal. The terminal is equipped with action mechanisms for operating agricultural machinery and robots, and automatically performs the instructed tasks. For example, it can automate actions such as adjusting water levels by operating an irrigation system or administering the appropriate amount of fertilizer.

[0417] Users can input harvest targets, quality standards, and other information into the system, which then optimizes work procedures based on this input. This enables efficient farming that achieves the user's set goals, resulting in effective agricultural management even in fields facing aging populations and labor shortages.

[0418] This system will promote automation and efficiency in agriculture, enabling a sustainable supply of high-quality crops.

[0419] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0420] Step 1:

[0421] The server collects environmental data using sensors. Specifically, it acquires data from temperature, humidity, and soil moisture sensors, and collects images of crops using cameras. This data is recorded in a database. The inputs are raw environmental and image data from sensors and cameras, and the output is structured data stored in the database.

[0422] Step 2:

[0423] The server inputs the data collected in the previous step into a generating AI model. This AI model analyzes temperature, humidity, soil moisture, and image data to generate insights into crop health and growth. The inputs are structured environmental data and image data obtained from a database, and the outputs are assessments of crop health, signs of potential diseases, and moisture deficiencies. Specifically, the AI ​​uses image analysis algorithms to check for the presence of diseases and detects elements for optimization by comparing them with physical environmental conditions.

[0424] Step 3:

[0425] The server generates work procedures based on the output of the generated AI model. This control system creates work instructions that include optimal temperature control, humidity management, and fertilizer application timing based on the analysis results. The input is the analysis results from the generated AI model, and the output is a detailed work procedure document. Specifically, it generates clear instructions such as "add 50% more fertilizer in the next clockwise cycle" and "activate the irrigation system to maintain a humidity level of 60%."

[0426] Step 4:

[0427] The terminal operates agricultural machinery based on work procedures transmitted from the server. The terminal automatically performs specified farm tasks using its operating mechanisms. The input is the work procedures from the control mechanisms, and the output is the result of the actual farm work performed. Specific actions include starting the irrigation system, applying fertilizer, and adjusting the temperature control system. Once these actions are completed, the terminal feeds the results back to the server.

[0428] (Application Example 1)

[0429] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0430] Modern cities face rapid population growth and environmental and food security challenges. In particular, there is a need to ensure the efficiency and sustainability of agriculture in suburban areas. However, traditional farming methods have limitations in ensuring a stable supply of fresh produce due to the difficulty of analyzing vast amounts of data and managing the environment precisely. Furthermore, it has been difficult for users to understand the agricultural environment in real time and make efficient decisions.

[0431] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0432] In this invention, the server includes means for using multiple detection devices to detect the growth status of crops and environmental conditions, means for using a generation AI unit to analyze the information collected by the detection devices, and control means for optimizing farm environment management based on the analysis results. This enables efficient and sustainable agricultural management even in urban environments, and allows users to understand the state of the farm through an information terminal and make quick decisions.

[0433] A "detection device" is a device that measures the growth status of crops and environmental conditions, and collects data such as temperature, humidity, and soil moisture content.

[0434] The "Generation AI Department" is a department equipped with artificial intelligence that analyzes information collected by detection devices to predict crop growth and calculate optimal environmental conditions.

[0435] The "control means" refers to the means of adjusting and optimizing the farm's environmental management based on the analysis results of the generating AI unit.

[0436] "Operating means" refers to means of operating automated agricultural equipment based on control means.

[0437] "Communication means" refers to communication technology used to display farm status on information terminals and to receive operation commands and requests from users.

[0438] An "information terminal" is an electronic device used by users to view farm data in real time and input operational commands.

[0439] This invention provides a highly integrated system for efficiently managing multiple farms in an urban environment. The system consists of multiple detection devices, a generation AI unit, control means, operation means, communication means, and an information terminal.

[0440] The server first receives environmental data collected by detection devices installed on the farm. This data includes temperature, humidity, soil moisture content, and plant growth status. This data is sent to a generative AI unit running on a cloud service and analyzed using deep learning. During this process, TensorFlow and OpenAI APIs are utilized to achieve highly accurate growth predictions and optimization of environmental conditions.

[0441] Once the generation AI unit completes the analysis, the server uses the control means to transmit commands based on the analysis results to the operation means. The operation means transmits the commands to the automated agricultural equipment and performs the necessary environmental adjustments and tasks.

[0442] Furthermore, the server displays analysis results and environmental conditions on information terminals via communication. This allows users to check the information in real time using their smartphones or computers and send operational commands for specific crops as needed.

[0443] As a specific example, in a tomato farm, when a sensor detects an increase in evapotranspiration, the AI ​​calculates the optimal timing for watering. The user can then check this on an information terminal and transmit appropriate instructions to agricultural equipment, enabling a stable harvest.

[0444] An example of a prompt to input into the generating AI model is, "Evaluate the growth status of the tomatoes based on current sensor information and suggest appropriate environmental adjustments." Through this process, the farm can be operated efficiently, and a stable supply of fresh produce to urban areas can be ensured.

[0445] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0446] Step 1:

[0447] The server receives environmental data such as temperature, humidity, and soil moisture content in real time from detection devices installed on the farm. This input data forms the basis for analysis performed later in the generation AI unit.

[0448] Step 2:

[0449] The server sends the collected data to the Generative AI unit. The Generative AI unit uses TensorFlow to execute a machine learning model and perform data analysis. In this process, the algorithm evaluates the growth status of crops and outputs analysis results that enable early detection of diseases and determine whether the current cultivation environment is optimal.

[0450] Step 3:

[0451] The server receives the analysis results from the generation AI unit and, via the control system, creates commands for maintaining the optimal agricultural environment. These commands include requirements for temperature and humidity adjustment, water supply, and appropriate fertilizer application, and these are output as instructions for agricultural equipment.

[0452] Step 4:

[0453] The terminal receives commands from the server through its control system and transmits them to the agricultural equipment. The agricultural equipment then automatically performs the necessary environmental adjustment tasks according to the commands. For example, the water supply may be adjusted.

[0454] Step 5:

[0455] The user uses an information terminal to check reports on the progress of work and environmental conditions obtained from agricultural equipment. Based on this information, the user sends additional commands via the information terminal as needed to manage the farm. If a specific operation command is provided as input by the user, it is sent to the terminal.

[0456] Step 6:

[0457] The server receives input from the user, and if necessary, uses the generation AI unit again to perform analysis and maintain overall system optimization. At this stage, a new analysis is performed according to the prompt, and the proposed generation AI model is reflected in the final system output.

[0458] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0459] This invention relates to a system that combines an emotion engine with an agricultural assistance system to enable agricultural support tailored to the user's emotional state. The system includes a sensor unit for detecting crop growth and environmental conditions, a generation AI unit for analyzing data, a control unit for generating work procedures, and an operation unit for operating agricultural machinery. It also features an emotion engine that recognizes the user's emotions.

[0460] The terminal uses various sensors to detect environmental data and crop growth status, and transmits it to the server. The server analyzes this data in its AI generation unit to determine the optimal growing conditions for the crops. The control unit generates work procedures and creates steps to maintain an appropriate growing environment.

[0461] In addition, the emotion engine detects the user's emotional state and adjusts the work procedures accordingly. For example, if the user is feeling stressed, it suggests less burdensome work procedures; conversely, if the user is highly motivated, it provides proactive work instructions. Furthermore, the emotion engine analyzes the feedback information provided by the user and continuously improves the analysis algorithm of the generation AI unit.

[0462] As a concrete example, consider a user who is cultivating tomatoes. The emotion engine checks the user's daily feelings and accurately detects their mental state. The server analyzes data such as temperature and humidity to check the condition of the crops and makes suggestions tailored to each situation. Based on the user's emotions, it identifies days when the user should relax and days when they want to work actively, and creates a farming plan accordingly. This improves the user experience while also optimizing work efficiency. This invention makes it possible to solve the problems of conventional agricultural work while increasing operational flexibility.

[0463] The following describes the processing flow.

[0464] Step 1:

[0465] The device uses a sensor unit to collect data on the growth status of crops and the surrounding environment. This sensor unit has the function of measuring data such as temperature, humidity, and soil moisture content.

[0466] Step 2:

[0467] The device sends the collected data to the server.

[0468] Step 3:

[0469] The server preprocesses the transmitted data. For example, it performs tasks such as imputing missing values, filtering outliers, and denoising images.

[0470] Step 4:

[0471] The server uses the generation AI unit to analyze pre-processed data. Here, it evaluates crop growth status, detects diseases, and assesses appropriate environmental conditions.

[0472] Step 5:

[0473] Based on the analysis results, the server generates the optimal farming procedure in the control unit. This procedure includes things like the timing of watering and fertilizing.

[0474] Step 6:

[0475] The terminal continuously collects data from sensors, and the server uses this data to re-evaluate environmental conditions in real time.

[0476] Step 7:

[0477] The user's emotional state is detected by the emotion engine. The emotion engine analyzes the user's emotions from their facial expressions and voice via a camera or voice input device.

[0478] Step 8:

[0479] The server adjusts the generated work procedures according to the user's emotions, based on the emotional data provided by the emotion engine. It provides work procedures to reduce stress and work suggestions to maintain motivation.

[0480] Step 9:

[0481] The server sends the final work instructions to the terminal, which then operates the agricultural machinery to automatically carry out the work.

[0482] Step 10:

[0483] When user feedback is provided, the emotion engine analyzes this feedback and uses it to improve the algorithms of the generative AI unit so that suggestions for future work can be more appropriately adjusted.

[0484] (Example 2)

[0485] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0486] In agricultural work, it is essential to not only optimally manage environmental conditions and crop growth, but also to propose efficient and effective work practices that take into account the emotional state of the workers. Conventional systems do not provide flexible work instructions that take into account the psychological factors of workers, resulting in challenges such as reduced worker stress and decreased productivity due to lack of motivation.

[0487] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0488] In this invention, the server includes detection means, analysis means, control means, operation means, and emotion recognition means. This enables optimized support for agricultural work based on environmental data and emotion data.

[0489] A "detection device" is a technical device used to monitor the growth status of agricultural products and environmental conditions in real time.

[0490] "Analysis means" refers to a technical system that processes detected data and derives the optimal growing conditions for crops.

[0491] A "control device" is a technical device that generates work procedures based on analysis results and provides instructions to optimize agricultural work.

[0492] "Operating means" refers to the technical mechanism for operating agricultural machinery based on the generated work procedures.

[0493] An "emotion recognition system" is a technical system that detects the user's emotional state and adjusts work instructions based on that state.

[0494] This invention is a system that analyzes environmental data and crop growth status while considering the user's emotions during agricultural work, and provides efficient work procedures. Its main components involve three entities—a terminal, a server, and a user—working together.

[0495] The terminal uses temperature, humidity, light, and image sensors installed on the farm to detect environmental data and crop conditions. This data is transmitted to a server via a wireless network. The data collected by the terminal includes temperature, humidity, sunlight, and the color and shape of crop leaves. Specifically, the terminal collects data every hour and sends it to a cloud server to enable real-time monitoring.

[0496] The server uses a generative AI unit to analyze the optimal growing conditions for crops based on the received data. For this purpose, it utilizes a proprietary generative AI model. The AI ​​model calculates the optimal amount of water and fertilization timing, for example, by considering weather fluctuations and crop characteristics. The analysis results are sent to the control unit, which generates specific work procedures. The server also incorporates an emotion engine that analyzes user feedback to determine the user's emotional state. For example, the server uses the emotion engine to analyze the user's stress level input and adjusts work instructions accordingly. An example of a prompt message might be: "The current temperature is 25 degrees Celsius and the humidity is 60%. The tomato leaves are starting to turn slightly yellow. The user is feeling a little tired. Based on this data, please suggest the optimal farming tasks for today."

[0497] Users perform actual farm work following the work procedures suggested through this system. Users provide feedback via terminals or smartphones, and this feedback helps improve the accuracy of the system. To enhance the user experience, the emotion engine constantly analyzes user feedback and contributes to improving the algorithms of the generation AI unit. Through this cycle, the invention can improve not only work efficiency but also user satisfaction.

[0498] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0499] Step 1:

[0500] The terminal collects data from various sensors placed on the farm. Inputs include temperature, humidity, sunlight, and crop growth status. Based on this sensor data, the terminal understands the current state of the environment in real time. As output, the sensor data is transmitted to a server via a wireless network.

[0501] Step 2:

[0502] The server inputs the received sensor data into a generated AI model as an analysis tool. Based on this data, the AI ​​model uses statistical analysis and machine learning algorithms to derive the optimal method for growing crops. This process outputs specific instructions such as the optimal amount of irrigation and fertilization schedule.

[0503] Step 3:

[0504] The server generates work procedures based on the analysis results using control means. It uses the analysis results of the generated AI model as input and creates a specific farm work plan accordingly. This includes prioritizing tasks and allocating necessary resources. The output is passed to the operating means as an optimized work procedure.

[0505] Step 4:

[0506] The server detects the user's emotional state using emotion recognition technology. User feedback and past emotional data are used as input. The emotion recognition technology analyzes this information to understand the user's stress level and motivation. The output is instructions for adjusting work procedures according to the emotional state.

[0507] Step 5:

[0508] Users perform farm work according to the procedures suggested by the server. Automated machine operation is supported by the control system, allowing users to work efficiently. After completion, users provide feedback to the system via a terminal or other device. This feedback is used for future analysis and system improvement.

[0509] (Application Example 2)

[0510] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0511] In agricultural work, the user's emotional state significantly impacts work efficiency and experience, yet there is no system that appropriately utilizes this to optimize work. Furthermore, conventional systems simply issue work instructions based on environmental data, failing to provide work processes that take into account the user's mental state.

[0512] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0513] In this invention, the server includes a detection unit for perceiving the growth status of agricultural products and environmental conditions, an emotion recognition unit for recognizing the user's emotional state and adjusting the activity plan according to that emotion, and a control unit for creating an activity plan to optimize work activities. This enables flexible and optimal agricultural work in accordance with the user's emotional state.

[0514] "Agricultural products" refer to plant-based food products and cash crops obtained through agricultural production.

[0515] "Growth status" refers to an indicator used to evaluate the developmental status and health of agricultural products during their growth process.

[0516] "Environmental conditions" refer to the surrounding physical conditions, such as temperature, humidity, and soil moisture content, that affect the growth of agricultural products.

[0517] The term "detection unit" refers to a component that includes various sensors positioned to perceive the growth status of agricultural products and environmental conditions.

[0518] The "generation algorithm unit" refers to the component that analyzes the information obtained by the detection unit and performs data processing necessary for optimizing agricultural activities.

[0519] "Activity plan" refers to the process and procedures for efficiently carrying out agricultural work, based on the analysis results from the generation algorithm unit.

[0520] A "control unit" refers to a component that manages mechanical devices based on the generated activity plan and directs them to perform appropriate actions.

[0521] The term "operating unit" refers to a component that includes a drive mechanism for the mechanical device to operate based on instructions from the control unit.

[0522] The "emotion recognition unit" refers to a component that has the function of perceiving the user's emotional state and adjusting the activity plan accordingly.

[0523] This invention realizes a system that provides work support tailored to the user's emotional state while optimally managing the growth status of agricultural products. The system consists of a detection unit for detecting the growth status of agricultural products and environmental conditions, an emotion recognition unit for recognizing the user's emotional state, and a control unit for economically planning work activities based on this information.

[0524] The server receives information from the detection unit and performs analysis using a generation algorithm. Specifically, it uses a program built in Python to predict growth based on environmental information (temperature, humidity, soil moisture content, etc.). The OpenAI GPT model is used as the generation AI model to generate an optimal activity plan. The server transmits the activity plan to the control unit via ROS (Robot Operating System). The control unit then issues instructions to the operation unit to perform the necessary mechanical devices.

[0525] Furthermore, the system uses Google's Cloud Natural Language API for emotion recognition, analyzing user emotions from their voice and facial expressions. This enables flexible work instructions tailored to the user's emotions. For example, if the server hears a user say, "I'm tired today," the system will modify the activity plan and suggest less demanding tasks.

[0526] For example, if a user is watering plants and makes a positive statement such as, "They're feeling good today, so let's prune them," the system will guide them through the pruning process, including the necessary steps and precautions. The generating AI model can be input with the following prompt: "Suggest gardening tasks for when User A is in a good mood. We've had a string of sunny days recently." This prompt will then suggest more appropriate work procedures.

[0527] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0528] Step 1:

[0529] The terminal acquires environmental data such as ambient temperature, humidity, and soil moisture content from sensors. It processes this data and sends it to the server. The input is environmental sensor data, and the output is the transmission of data to the server.

[0530] Step 2:

[0531] The server receives environmental data sent from the terminal and uses a generation algorithm to predict crop growth. During this process, it performs data analysis using Python and generates an optimal activity plan. The input is the submitted environmental data, and the output is the generated activity plan.

[0532] Step 3:

[0533] To detect user emotions, the emotion recognition unit analyzes the user's voice and facial expressions using Google's Cloud Natural Language API. The input is the user's voice and video data, and the output is the user's emotional state.

[0534] Step 4:

[0535] The server dynamically adjusts the generated activity plan according to the user's emotional state. Specifically, it reconstructs the activity plan using an advanced generative AI model (OpenAI's GPT model). The input is the activity plan and the user's emotional state, and the output is the adjusted activity plan.

[0536] Step 5:

[0537] The server transmits the coordinated activity plan to the control unit. The control unit then transmits instructions to the operating unit based on this plan, managing the machine to operate as instructed. The input is the activity plan transmitted from the server, and the output is the operation of the machine.

[0538] Step 6:

[0539] The user performs farm work according to the activity plan and work instructions presented by the server. During this process, the user can provide feedback to the server through the emotion recognition unit, which further optimizes the system. The input is the content of the activity plan, and the output is feedback information.

[0540] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0541] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0542] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0543] [Fourth Embodiment]

[0544] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0545] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0546] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0547] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0548] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0549] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0550] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0551] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0552] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0553] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0554] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0555] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0556] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0557] This invention is a system that realizes automation and efficiency in the agricultural field. The system is equipped with a sensor unit to monitor crop growth and surrounding environmental conditions in real time. This sensor unit collects environmental data such as temperature, humidity, and soil moisture content through cameras and various sensors, and generates image data of the crops.

[0558] The server collects this data and uses a generation AI unit to perform data analysis. Specifically, it analyzes the growth status of crops and evaluates whether there are any diseases or whether the environment is suitable for plant growth.

[0559] The analysis results are processed by a control unit within the server, which generates work procedures for maintaining an optimal agricultural environment. These procedures include adjusting temperature and humidity, determining appropriate fertilizer dosages, and suggesting the optimal harvest time.

[0560] The server sends the generated work procedures to a terminal (agricultural automation robot), which then operates the agricultural machinery accordingly. This enables automated farming to achieve the specified growing environment.

[0561] As an example, consider a farm cultivating tomatoes. The user inputs the target yield and quality into the system. The server analyzes the crop's growth status based on data from sensors and detects problems such as signs of disease or water shortage. It then optimizes the timing and amount of fertilizer and water application, and suggests a harvest time to help the user achieve their goals. The terminal automatically performs the necessary tasks according to these instructions, maintaining the crop in an optimal state of growth.

[0562] In this way, the present invention makes it possible to supply high-quality crops while solving problems such as an aging population and labor shortages.

[0563] The following describes the processing flow.

[0564] Step 1:

[0565] The terminal uses a sensor unit to collect image data of agricultural products and environmental data (temperature, humidity, soil moisture content, etc.).

[0566] Step 2:

[0567] The terminal transfers the collected data to the server.

[0568] Step 3:

[0569] The server preprocesses the received data. Specifically, it corrects missing data values, removes image noise, and adjusts the resolution.

[0570] Step 4:

[0571] The server analyzes the pre-processed data using the generation AI unit to evaluate the growth and health status of the crops.

[0572] Step 5:

[0573] The server calculates the optimal growing environment based on the analysis results and determines the necessary control parameters.

[0574] Step 6:

[0575] The server generates specific work procedures based on calculations and sends them to the terminal.

[0576] Step 7:

[0577] The terminal automatically operates agricultural machinery to perform tasks according to the received work instructions.

[0578] Step 8:

[0579] The server monitors the results of the task execution and uses the feedback data obtained for subsequent analyses.

[0580] (Example 1)

[0581] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0582] In the agricultural sector, aging and labor shortages are serious problems, and there is a need to achieve efficient and effective farming practices. Furthermore, it is difficult to properly manage crop growth and environmental conditions, making improvements in productivity and quality a challenge. Therefore, there is a need to develop a system that automates agricultural work based on environmental data and continuously monitors the health of crops.

[0583] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0584] In this invention, the server includes detection means for collecting environmental data and image data, generation AI means for analyzing the data obtained by the detection means and evaluating the health of crops and appropriate care, and control means for generating work procedures to optimize the agricultural environment based on the evaluation results. This enables the efficiency and automation of agricultural work, and allows for the management of crop growth under optimal conditions.

[0585] "Detection means" refers to devices or technical elements provided for collecting environmental data and image data, which acquire information such as temperature, humidity, and soil moisture content using sensors.

[0586] "Generative AI means" refers to processing technology that utilizes artificial intelligence to analyze data obtained by detection means and evaluate the health status and maintenance needs of crops.

[0587] "Control means" refers to a technical element for generating work procedures to optimize the agricultural environment based on the evaluation results of the generating AI means, and for managing and controlling the entire system accordingly.

[0588] "Operating means" refers to a mechanism for operating agricultural machinery according to the work procedures from the control means and performing specific actions.

[0589] This invention is a system for efficiently managing and optimizing crop growth and environmental conditions in agricultural settings, and is primarily implemented by three parties: a server, terminals, and users.

[0590] The server uses various sensors and cameras to measure temperature, humidity, and soil moisture as detection means for collecting environmental and image data. This makes it possible to accurately monitor crop growth and the external environment.

[0591] The server analyzes this data using a generative AI model. The generative AI model evaluates the health of the crops through data analysis and provides information for necessary care and environmental adjustments. Specifically, it quickly detects potential problems such as signs of disease or water shortages. An example of a prompt message would be, "Based on the current environmental data, evaluate the risk of tomato disease and suggest countermeasures."

[0592] Based on the analysis results, the server generates work procedures using control mechanisms and transmits them to the terminal. The terminal is equipped with action mechanisms for operating agricultural machinery and robots, and automatically performs the instructed tasks. For example, it can automate actions such as adjusting water levels by operating an irrigation system or administering the appropriate amount of fertilizer.

[0593] Users can input harvest targets, quality standards, and other information into the system, which then optimizes work procedures based on this input. This enables efficient farming that achieves the user's set goals, resulting in effective agricultural management even in fields facing aging populations and labor shortages.

[0594] This system will promote automation and efficiency in agriculture, enabling a sustainable supply of high-quality crops.

[0595] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0596] Step 1:

[0597] The server collects environmental data using sensors. Specifically, it acquires data from temperature, humidity, and soil moisture sensors, and collects images of crops using cameras. This data is recorded in a database. The inputs are raw environmental and image data from sensors and cameras, and the output is structured data stored in the database.

[0598] Step 2:

[0599] The server inputs the data collected in the previous step into a generating AI model. This AI model analyzes temperature, humidity, soil moisture, and image data to generate insights into crop health and growth. The inputs are structured environmental data and image data obtained from a database, and the outputs are assessments of crop health, signs of potential diseases, and moisture deficiencies. Specifically, the AI ​​uses image analysis algorithms to check for the presence of diseases and detects elements for optimization by comparing them with physical environmental conditions.

[0600] Step 3:

[0601] The server generates work procedures based on the output of the generated AI model. This control system creates work instructions that include optimal temperature control, humidity management, and fertilizer application timing based on the analysis results. The input is the analysis results from the generated AI model, and the output is a detailed work procedure document. Specifically, it generates clear instructions such as "add 50% more fertilizer in the next clockwise cycle" and "activate the irrigation system to maintain a humidity level of 60%."

[0602] Step 4:

[0603] The terminal operates agricultural machinery based on work procedures transmitted from the server. The terminal automatically performs specified farm tasks using its operating mechanisms. The input is the work procedures from the control mechanisms, and the output is the result of the actual farm work performed. Specific actions include starting the irrigation system, applying fertilizer, and adjusting the temperature control system. Once these actions are completed, the terminal feeds the results back to the server.

[0604] (Application Example 1)

[0605] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0606] Modern cities face rapid population growth and environmental and food security challenges. In particular, there is a need to ensure the efficiency and sustainability of agriculture in suburban areas. However, traditional farming methods have limitations in ensuring a stable supply of fresh produce due to the difficulty of analyzing vast amounts of data and managing the environment precisely. Furthermore, it has been difficult for users to understand the agricultural environment in real time and make efficient decisions.

[0607] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0608] In this invention, the server includes means for using multiple detection devices to detect the growth status of crops and environmental conditions, means for using a generation AI unit to analyze the information collected by the detection devices, and control means for optimizing farm environment management based on the analysis results. This enables efficient and sustainable agricultural management even in urban environments, and allows users to understand the state of the farm through an information terminal and make quick decisions.

[0609] A "detection device" is a device that measures the growth status of crops and environmental conditions, and collects data such as temperature, humidity, and soil moisture content.

[0610] The "Generation AI Department" is a department equipped with artificial intelligence that analyzes information collected by detection devices to predict crop growth and calculate optimal environmental conditions.

[0611] The "control means" refers to the means of adjusting and optimizing the farm's environmental management based on the analysis results of the generating AI unit.

[0612] "Operating means" refers to means of operating automated agricultural equipment based on control means.

[0613] "Communication means" refers to communication technology used to display farm status on information terminals and to receive operation commands and requests from users.

[0614] An "information terminal" is an electronic device used by users to view farm data in real time and input operational commands.

[0615] This invention provides a highly integrated system for efficiently managing multiple farms in an urban environment. The system consists of multiple detection devices, a generation AI unit, control means, operation means, communication means, and an information terminal.

[0616] The server first receives environmental data collected by detection devices installed on the farm. This data includes temperature, humidity, soil moisture content, and plant growth status. This data is sent to a generative AI unit running on a cloud service and analyzed using deep learning. During this process, TensorFlow and OpenAI APIs are utilized to achieve highly accurate growth predictions and optimization of environmental conditions.

[0617] Once the generation AI unit completes the analysis, the server uses the control means to transmit commands based on the analysis results to the operation means. The operation means transmits the commands to the automated agricultural equipment and performs the necessary environmental adjustments and tasks.

[0618] Furthermore, the server displays analysis results and environmental conditions on information terminals via communication. This allows users to check the information in real time using their smartphones or computers and send operational commands for specific crops as needed.

[0619] As a specific example, in a tomato farm, when a sensor detects an increase in evapotranspiration, the AI ​​calculates the optimal timing for watering. The user can then check this on an information terminal and transmit appropriate instructions to agricultural equipment, enabling a stable harvest.

[0620] An example of a prompt to input into the generating AI model is, "Evaluate the growth status of the tomatoes based on current sensor information and suggest appropriate environmental adjustments." Through this process, the farm can be operated efficiently, and a stable supply of fresh produce to urban areas can be ensured.

[0621] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0622] Step 1:

[0623] The server receives environmental data such as temperature, humidity, and soil moisture content in real time from detection devices installed on the farm. This input data forms the basis for analysis performed later in the generation AI unit.

[0624] Step 2:

[0625] The server sends the collected data to the Generative AI unit. The Generative AI unit uses TensorFlow to execute a machine learning model and perform data analysis. In this process, the algorithm evaluates the growth status of crops and outputs analysis results that enable early detection of diseases and determine whether the current cultivation environment is optimal.

[0626] Step 3:

[0627] The server receives the analysis results from the generation AI unit and, via the control system, creates commands for maintaining the optimal agricultural environment. These commands include requirements for temperature and humidity adjustment, water supply, and appropriate fertilizer application, and these are output as instructions for agricultural equipment.

[0628] Step 4:

[0629] The terminal receives commands from the server through its control system and transmits them to the agricultural equipment. The agricultural equipment then automatically performs the necessary environmental adjustment tasks according to the commands. For example, the water supply may be adjusted.

[0630] Step 5:

[0631] The user uses an information terminal to check reports on the progress of work and environmental conditions obtained from agricultural equipment. Based on this information, the user sends additional commands via the information terminal as needed to manage the farm. If a specific operation command is provided as input by the user, it is sent to the terminal.

[0632] Step 6:

[0633] The server receives input from the user, and if necessary, uses the generation AI unit again to perform analysis and maintain overall system optimization. At this stage, a new analysis is performed according to the prompt, and the proposed generation AI model is reflected in the final system output.

[0634] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0635] This invention relates to a system that combines an emotion engine with an agricultural assistance system to enable agricultural support tailored to the user's emotional state. The system includes a sensor unit for detecting crop growth and environmental conditions, a generation AI unit for analyzing data, a control unit for generating work procedures, and an operation unit for operating agricultural machinery. It also features an emotion engine that recognizes the user's emotions.

[0636] The terminal uses various sensors to detect environmental data and crop growth status, and transmits it to the server. The server analyzes this data in its AI generation unit to determine the optimal growing conditions for the crops. The control unit generates work procedures and creates steps to maintain an appropriate growing environment.

[0637] In addition, the emotion engine detects the user's emotional state and adjusts the work procedures accordingly. For example, if the user is feeling stressed, it suggests less burdensome work procedures; conversely, if the user is highly motivated, it provides proactive work instructions. Furthermore, the emotion engine analyzes the feedback information provided by the user and continuously improves the analysis algorithm of the generation AI unit.

[0638] As a concrete example, consider a user who is cultivating tomatoes. The emotion engine checks the user's daily feelings and accurately detects their mental state. The server analyzes data such as temperature and humidity to check the condition of the crops and makes suggestions tailored to each situation. Based on the user's emotions, it identifies days when the user should relax and days when they want to work actively, and creates a farming plan accordingly. This improves the user experience while also optimizing work efficiency. This invention makes it possible to solve the problems of conventional agricultural work while increasing operational flexibility.

[0639] The following describes the processing flow.

[0640] Step 1:

[0641] The device uses a sensor unit to collect data on the growth status of crops and the surrounding environment. This sensor unit has the function of measuring data such as temperature, humidity, and soil moisture content.

[0642] Step 2:

[0643] The device sends the collected data to the server.

[0644] Step 3:

[0645] The server preprocesses the transmitted data. For example, it performs tasks such as imputing missing values, filtering outliers, and denoising images.

[0646] Step 4:

[0647] The server uses the generation AI unit to analyze pre-processed data. Here, it evaluates crop growth status, detects diseases, and assesses appropriate environmental conditions.

[0648] Step 5:

[0649] Based on the analysis results, the server generates the optimal farming procedure in the control unit. This procedure includes things like the timing of watering and fertilizing.

[0650] Step 6:

[0651] The terminal continuously collects data from sensors, and the server uses this data to re-evaluate environmental conditions in real time.

[0652] Step 7:

[0653] The user's emotional state is detected by the emotion engine. The emotion engine analyzes the user's emotions from their facial expressions and voice via a camera or voice input device.

[0654] Step 8:

[0655] The server adjusts the generated work procedures according to the user's emotions, based on the emotional data provided by the emotion engine. It provides work procedures to reduce stress and work suggestions to maintain motivation.

[0656] Step 9:

[0657] The server sends the final work instructions to the terminal, which then operates the agricultural machinery to automatically carry out the work.

[0658] Step 10:

[0659] When user feedback is provided, the emotion engine analyzes this feedback and uses it to improve the algorithms of the generative AI unit so that suggestions for future work can be more appropriately adjusted.

[0660] (Example 2)

[0661] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0662] In agricultural work, it is essential to not only optimally manage environmental conditions and crop growth, but also to propose efficient and effective work practices that take into account the emotional state of the workers. Conventional systems do not provide flexible work instructions that take into account the psychological factors of workers, resulting in challenges such as reduced worker stress and decreased productivity due to lack of motivation.

[0663] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0664] In this invention, the server includes detection means, analysis means, control means, operation means, and emotion recognition means. This enables optimized support for agricultural work based on environmental data and emotion data.

[0665] A "detection device" is a technical device used to monitor the growth status of agricultural products and environmental conditions in real time.

[0666] "Analysis means" refers to a technical system that processes detected data and derives the optimal growing conditions for crops.

[0667] A "control device" is a technical device that generates work procedures based on analysis results and provides instructions to optimize agricultural work.

[0668] "Operating means" refers to the technical mechanism for operating agricultural machinery based on the generated work procedures.

[0669] An "emotion recognition system" is a technical system that detects the user's emotional state and adjusts work instructions based on that state.

[0670] This invention is a system that analyzes environmental data and crop growth status while considering the user's emotions during agricultural work, and provides efficient work procedures. Its main components involve three entities—a terminal, a server, and a user—working together.

[0671] The terminal uses temperature, humidity, light, and image sensors installed on the farm to detect environmental data and crop conditions. This data is transmitted to a server via a wireless network. The data collected by the terminal includes temperature, humidity, sunlight, and the color and shape of crop leaves. Specifically, the terminal collects data every hour and sends it to a cloud server to enable real-time monitoring.

[0672] The server uses a generative AI unit to analyze the optimal growing conditions for crops based on the received data. For this purpose, it utilizes a proprietary generative AI model. The AI ​​model calculates the optimal amount of water and fertilization timing, for example, by considering weather fluctuations and crop characteristics. The analysis results are sent to the control unit, which generates specific work procedures. The server also incorporates an emotion engine that analyzes user feedback to determine the user's emotional state. For example, the server uses the emotion engine to analyze the user's stress level input and adjusts work instructions accordingly. An example of a prompt message might be: "The current temperature is 25 degrees Celsius and the humidity is 60%. The tomato leaves are starting to turn slightly yellow. The user is feeling a little tired. Based on this data, please suggest the optimal farming tasks for today."

[0673] Users perform actual farm work following the work procedures suggested through this system. Users provide feedback via terminals or smartphones, and this feedback helps improve the accuracy of the system. To enhance the user experience, the emotion engine constantly analyzes user feedback and contributes to improving the algorithms of the generation AI unit. Through this cycle, the invention can improve not only work efficiency but also user satisfaction.

[0674] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0675] Step 1:

[0676] The terminal collects data from various sensors placed on the farm. Inputs include temperature, humidity, sunlight, and crop growth status. Based on this sensor data, the terminal understands the current state of the environment in real time. As output, the sensor data is transmitted to a server via a wireless network.

[0677] Step 2:

[0678] The server inputs the received sensor data into a generated AI model as an analysis tool. Based on this data, the AI ​​model uses statistical analysis and machine learning algorithms to derive the optimal method for growing crops. This process outputs specific instructions such as the optimal amount of irrigation and fertilization schedule.

[0679] Step 3:

[0680] The server generates work procedures based on the analysis results using control means. It uses the analysis results of the generated AI model as input and creates a specific farm work plan accordingly. This includes prioritizing tasks and allocating necessary resources. The output is passed to the operating means as an optimized work procedure.

[0681] Step 4:

[0682] The server detects the user's emotional state using emotion recognition technology. User feedback and past emotional data are used as input. The emotion recognition technology analyzes this information to understand the user's stress level and motivation. The output is instructions for adjusting work procedures according to the emotional state.

[0683] Step 5:

[0684] Users perform farm work according to the procedures suggested by the server. Automated machine operation is supported by the control system, allowing users to work efficiently. After completion, users provide feedback to the system via a terminal or other device. This feedback is used for future analysis and system improvement.

[0685] (Application Example 2)

[0686] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0687] In agricultural work, the user's emotional state significantly impacts work efficiency and experience, yet there is no system that appropriately utilizes this to optimize work. Furthermore, conventional systems simply issue work instructions based on environmental data, failing to provide work processes that take into account the user's mental state.

[0688] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0689] In this invention, the server includes a detection unit for perceiving the growth status of agricultural products and environmental conditions, an emotion recognition unit for recognizing the user's emotional state and adjusting the activity plan according to that emotion, and a control unit for creating an activity plan to optimize work activities. This enables flexible and optimal agricultural work in accordance with the user's emotional state.

[0690] "Agricultural products" refer to plant-based food products and cash crops obtained through agricultural production.

[0691] "Growth status" refers to an indicator used to evaluate the developmental status and health of agricultural products during their growth process.

[0692] "Environmental conditions" refer to the surrounding physical conditions, such as temperature, humidity, and soil moisture content, that affect the growth of agricultural products.

[0693] The term "detection unit" refers to a component that includes various sensors positioned to perceive the growth status of agricultural products and environmental conditions.

[0694] The "generation algorithm unit" refers to the component that analyzes the information obtained by the detection unit and performs data processing necessary for optimizing agricultural activities.

[0695] "Activity plan" refers to the process and procedures for efficiently carrying out agricultural work, based on the analysis results from the generation algorithm unit.

[0696] A "control unit" refers to a component that manages mechanical devices based on the generated activity plan and directs them to perform appropriate actions.

[0697] The term "operating unit" refers to a component that includes a drive mechanism for the mechanical device to operate based on instructions from the control unit.

[0698] The "emotion recognition unit" refers to a component that has the function of perceiving the user's emotional state and adjusting the activity plan accordingly.

[0699] This invention realizes a system that provides work support tailored to the user's emotional state while optimally managing the growth status of agricultural products. The system consists of a detection unit for detecting the growth status of agricultural products and environmental conditions, an emotion recognition unit for recognizing the user's emotional state, and a control unit for economically planning work activities based on this information.

[0700] The server receives information from the detection unit and performs analysis using a generation algorithm. Specifically, it uses a program built in Python to predict growth based on environmental information (temperature, humidity, soil moisture content, etc.). The OpenAI GPT model is used as the generation AI model to generate an optimal activity plan. The server transmits the activity plan to the control unit via ROS (Robot Operating System). The control unit then issues instructions to the operation unit to perform the necessary mechanical devices.

[0701] Furthermore, the system uses Google's Cloud Natural Language API for emotion recognition, analyzing user emotions from their voice and facial expressions. This enables flexible work instructions tailored to the user's emotions. For example, if the server hears a user say, "I'm tired today," the system will modify the activity plan and suggest less demanding tasks.

[0702] For example, if a user is watering plants and makes a positive statement such as, "They're feeling good today, so let's prune them," the system will guide them through the pruning process, including the necessary steps and precautions. The generating AI model can be input with the following prompt: "Suggest gardening tasks for when User A is in a good mood. We've had a string of sunny days recently." This prompt will then suggest more appropriate work procedures.

[0703] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0704] Step 1:

[0705] The terminal acquires environmental data such as ambient temperature, humidity, and soil moisture content from sensors. It processes this data and sends it to the server. The input is environmental sensor data, and the output is the transmission of data to the server.

[0706] Step 2:

[0707] The server receives environmental data sent from the terminal and uses a generation algorithm to predict crop growth. During this process, it performs data analysis using Python and generates an optimal activity plan. The input is the submitted environmental data, and the output is the generated activity plan.

[0708] Step 3:

[0709] To detect user emotions, the emotion recognition unit analyzes the user's voice and facial expressions using Google's Cloud Natural Language API. The input is the user's voice and video data, and the output is the user's emotional state.

[0710] Step 4:

[0711] The server dynamically adjusts the generated activity plan according to the user's emotional state. Specifically, it reconstructs the activity plan using an advanced generative AI model (OpenAI's GPT model). The input is the activity plan and the user's emotional state, and the output is the adjusted activity plan.

[0712] Step 5:

[0713] The server transmits the coordinated activity plan to the control unit. The control unit then transmits instructions to the operating unit based on this plan, managing the machine to operate as instructed. The input is the activity plan transmitted from the server, and the output is the operation of the machine.

[0714] Step 6:

[0715] The user performs farm work according to the activity plan and work instructions presented by the server. During this process, the user can provide feedback to the server through the emotion recognition unit, which further optimizes the system. The input is the content of the activity plan, and the output is feedback information.

[0716] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0717] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0718] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0719] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0720] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0721] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0722] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0723] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0724] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0725] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0726] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0727] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0728] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0729] 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.

[0730] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0731] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0732] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0733] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0734] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0735] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0736] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0737] The following is further disclosed regarding the embodiments described above.

[0738] (Claim 1)

[0739] A sensor unit for detecting the growth status of crops and environmental conditions,

[0740] A generation AI unit analyzes the data detected by the aforementioned sensor unit,

[0741] Based on the aforementioned analysis results, a control unit generates work procedures for optimizing agricultural work,

[0742] An operating unit for operating agricultural machinery based on the aforementioned work procedure,

[0743] A system that includes this.

[0744] (Claim 2)

[0745] The system according to claim 1, wherein the sensor unit has the function of detecting temperature, humidity, and soil moisture content.

[0746] (Claim 3)

[0747] The system according to claim 1, wherein the generation AI unit predicts the growth of crops and calculates the optimal environmental conditions accordingly.

[0748] "Example 1"

[0749] (Claim 1)

[0750] Detection means for collecting environmental data and image data,

[0751] A generation AI means for analyzing the data obtained by the detection means and evaluating the health status of crops and appropriate care,

[0752] Based on the evaluation results, a control means for generating work procedures to optimize the agricultural environment,

[0753] Operating means for operating agricultural machinery according to the aforementioned work procedure,

[0754] A system that includes this.

[0755] (Claim 2)

[0756] The system according to claim 1, wherein the detection means has the function of detecting temperature, humidity, and soil moisture content using a plurality of sensors.

[0757] (Claim 3)

[0758] The system according to claim 1, wherein the generating AI means analyzes the collected data to predict crop growth and calculates the optimal environmental conditions accordingly.

[0759] "Application Example 1"

[0760] (Claim 1)

[0761] Multiple detection devices for detecting the growth status of crops and environmental conditions,

[0762] A generation AI unit that analyzes the information collected by the aforementioned detection device,

[0763] Based on the analysis results, a control means for optimizing farm environment management is provided.

[0764] An operating means for operating automated agricultural equipment based on the control means,

[0765] A communication means that displays farm status via an information terminal and receives information in response to requests from users,

[0766] A system that includes this.

[0767] (Claim 2)

[0768] The system according to claim 1, wherein the detection device has the function of collecting temperature, humidity, and soil moisture content.

[0769] (Claim 3)

[0770] The system according to claim 1, wherein the generation AI unit predicts the growth of crops and calculates optimal environmental conditions and market information accordingly.

[0771] "Example 2 of combining an emotion engine"

[0772] (Claim 1)

[0773] A detection means for detecting the growth status and environmental conditions of agricultural products,

[0774] An analysis means for analyzing the data obtained by the detection means,

[0775] Based on the aforementioned analysis results, a control means for generating procedures to optimize agricultural work,

[0776] Operating means for operating agricultural machinery based on the above procedure,

[0777] An emotion recognition means for detecting the user's emotional state and adjusting the procedure,

[0778] A system that includes this.

[0779] (Claim 2)

[0780] The system according to claim 1, comprising the detection means having the function of detecting temperature, humidity, amount of sunlight, and the condition of crops.

[0781] (Claim 3)

[0782] The system according to claim 1, wherein the generation AI unit performs data analysis and user status analysis to adjust agricultural work.

[0783] "Application example 2 when combining with an emotional engine"

[0784] (Claim 1)

[0785] A detection unit for perceiving the growth status and environmental conditions of agricultural products,

[0786] A generation algorithm unit that analyzes the information obtained by the detection unit,

[0787] Based on the analysis results, a control unit creates an activity plan to optimize work activities,

[0788] An operating unit for operating machinery and equipment based on the aforementioned activity plan,

[0789] An emotion recognition unit that recognizes the user's emotional state and adjusts the activity plan according to that emotion,

[0790] A system that includes this.

[0791] (Claim 2)

[0792] The system according to claim 1, wherein the detection unit has the function of sensing atmospheric temperature, humidity, and ground moisture content.

[0793] (Claim 3)

[0794] The system according to claim 1, wherein the generation algorithm unit predicts the growth of agricultural products and calculates the optimal environmental conditions accordingly. [Explanation of symbols]

[0795] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A sensor unit for detecting the growth status of crops and environmental conditions, A generation AI unit analyzes the data detected by the aforementioned sensor unit, Based on the aforementioned analysis results, a control unit generates work procedures for optimizing agricultural work, An operating unit for operating agricultural machinery based on the aforementioned work procedure, A system that includes this.

2. The system according to claim 1, wherein the sensor unit has the function of detecting temperature, humidity, and soil moisture content.

3. The system according to claim 1, wherein the generation AI unit predicts the growth of crops and calculates the optimal environmental conditions accordingly.

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